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Nicotinamide N-methyl-transferase ( NNMT ) is an essential contributor to various metabolic and epigenetic processes , including the regulating of aging , cellular stress response , and body weight gain . Epidemiological studies show that NNMT is a risk factor for psychiatric diseases like schizophrenia and neurodegeneration , especially Parkinson’s disease ( PD ) , but its neuronal mechanisms of action remain obscure . Here , we describe the role of neuronal NNMT using C . elegans . We discovered that ANMT-1 , the nematode NNMT ortholog , competes with the methyltransferase LCMT-1 for methyl groups from S—adenosyl methionine . Thereby , it regulates the catalytic capacities of LCMT-1 , targeting NPRL-2 , a regulator of autophagy . Autophagy is a core cellular , catabolic process for degrading cytoplasmic material , but very little is known about the regulation of autophagy during aging . We report an important role for NNMT in regulation of autophagy during aging , where high neuronal ANMT-1 activity induces autophagy via NPRL-2 , which maintains neuronal function in old wild type animals and various disease models , also affecting longevity . In younger animals , however , ANMT-1 activity disturbs neuronal homeostasis and dopamine signaling , causing abnormal behavior . In summary , we provide fundamental insights into neuronal NNMT/ANMT-1 as pivotal regulator of behavior , neurodegeneration , and lifespan by controlling neuronal autophagy , potentially influencing PD and schizophrenia risk in humans .
Neurodegenerative disorders are a major health concern in all aging populations . The modifications associated with these diseases are mostly progressive and irreversible , and while several compounds have been developed that relieve symptoms in the short term , no cure has been identified for any of these conditions . Causes leading to neurodegeneration are both diverse and complex , and include various genetic , epigenetic , and environmental factors . Parkinson’s disease ( PD ) is among the most prevalent neurodegenerative diseases and numbers increase steadily with age , reaching approximately 2% of all octogenarians affected worldwide . PD is characterized by dopaminergic ( DA ) cell death in the substantia nigra in the midbrain , leading to a variety of motor and psychiatric symptoms , such as tremor , depression , and dementia . For decades , PD was considered mostly an idiopathic and sporadic disease with only a small genetic component [1 , 2] . However , genetic studies in the last decade have been instrumental in identifying the heredity basis of PD . Recent studies suggest that 27–60% of all cases might be caused by genetic factors [3–5] . The gene encoding α-synuclein , SCNA , was the first gene identified causing autosomal recessive inherited PD when mutated , followed by the discovery of PARK2 and PINK1 [6–8] . Interestingly , all of these proteins are involved in macroautophagy ( referred to herein as autophagy ) processes , leading to the conclusion that autophagy plays an important role in PD . Indeed , studies have shown that the autophagic flux is profoundly disrupted in PD patients , whereas it remains a matter of debate whether this is a cause or result of the disease [9] . A large meta-analysis of genome-wide association studies of PD analyzed the genome of over 13 , 000 PD patients and compared it to more than 95 , 000 controls . Many new genes causative for PD were identified from this study , including LRRK2 , GBA , TMEM175 , GPNMB , MAPT , SCARB2 , and SREBF1 , each of which is directly or indirectly involved in autophagy [10] . Autophagy is a conserved catabolic cellular process during which macromolecules , organelles , and cytosol fractions are degraded by the lysosomes , which contain acid hydrolases ( such as proteases , lipases , or nucleases ) that break down the internalized macromolecules . Essential components of these macromolecules can be used for energy yield and the building of new cellular material . Although initially described as a stress-induced mechanism , autophagy exerts basal activity and has a major role in the quality control of proteins to maintain cellular homeostasis . The role of autophagy for neuroprotection and neurodegeneration in general is well established , and its stringent regulation is critical for healthy neuronal homeostasis [11] . For instance , autophagy can ameliorate symptoms of PD by removing aggregated proteins , whereas excessive autophagy in contrast may contribute to DA cell death [12] . Interestingly , impaired autophagy is also linked to schizophrenia [13] , a mental illness that typically commences in young adulthood with a lifetime prevalence of about 0 . 5% [14] . Symptoms , such as abnormal social behavior and the inability to distinguish between reality and the imaginary , are usually attributable to disturbed dopamine signaling . In contrast to PD , where the lack of dopamine is the perpetrator , high dopamine levels and dopamine hyper-responsiveness seem to be responsible for schizophrenic symptoms [15] . A potential role in both PD and schizophrenia is attributed to the enzyme nicotinamide-N-methyltransferase ( NNMT ) , which is expressed in all body tissues including the nervous system and represents a key player in NAD+/NADH metabolism . The central coenzyme for fuel oxidation and interconversion of different classes of metabolites is NAD+ , which is typically reduced to NADH during these processes . It can furthermore be used by the sirtuins , which link lysine deacetylation to the turnover of NAD+ , and poly ADP-ribose polymerases ( PARPs ) for DNA repair . One of the products of these reactions is nicotinamide ( NAM ) . NAM is one of three NAD+ precursor vitamins ( vitamin B3 ) and can be salvaged and used in re-synthesis of NAD+ , or converted irreversibly by NNMT to N-methylnicotinamide ( MNA ) , using S-adenosyl methionine ( SAM ) as methyl group donor [16] . NNMT has been shown to play a crucial role in obesity , as limiting re-synthesis of NAD+ decreased fuel oxidation , leading to fat storage in mice [17] . In contrast , NNMT in liver improves lipid parameters via sirtuin 1 stabilization to protect from some effects of high fat diet-induced obesity [18] , pointing towards significant tissue-specificity of NNMT . The enzyme has furthermore been shown to play a crucial role in reactive oxygen species signaling and aging [19] , and is highly expressed in cancer cells [20 , 21] , where it influences epigenetic regulation [22] . Several studies have implicated NNMT in PD , schizophrenia , and other neurological disorders such as bipolar disorder and epilepsy [23–28] . Notably , both PD and schizophrenia are associated with methylation disturbances in the cell [29 , 30] . Although some in vitro studies have found potential mechanisms that contribute to NNMT action in neurons [31 , 32] , no mechanism of action has been described in vivo so far . Here we investigate for the first time the neuronal role of NNMT in the context of neuronal homeostasis , behavior , neurodegeneration , and lifespan in vivo using the model organism Caenorhabditis elegans . This small nematode has provided valuable insights into the cellular mechanisms of neurodegeneration , neurological control of behavior , and aging . The C . elegans nervous system is simple , yet many of its structural features and the associated cellular and biochemical processes are very similar to those of most vertebrate nervous systems . C . elegans is the only organism whose neuronal wiring has been completely documented , showing surprisingly complex neuronal circuits and behavioral plasticity . Additionally , its short lifespan of about 4 weeks allows for the study of living , aging animals , which is an important consideration since age is a major risk factor for neurodegeneration . anmt-1 , the C . elegans ortholog of human nnmt , is naturally not expressed in the nervous system of the worm . We discovered that a mild ectopic neuronal expression of anmt-1 regulates neurotransmitter production and neuronal autophagy via influencing the availability of intracellular methyl groups . Thus , ANMT-1 influences neuronal homeostasis , behavior , degeneration , and organismal health- and lifespan . ANMT-1 competes for methyl groups from SAM with another methyltransferase , LCMT-1 , the worm ortholog of human LCMT1 ( leucine carboxyl methyltransferase 1 ) , as the methylation to MNA is irreversible , creating methylation drainage in the cell . Consequently , LCMT-1 activity is limited , leading to a switch in a pathway containing LET-92/PP2A ( protein phosphatase 2 ) and NPRL-2/NPRL2 ( human NPR2-like , GATOR1 complex subunit ) , which induces autophagy . We further show that the regulation of autophagy via ANMT-1 is beneficial in neurodegenerative disease models . Notably , in the case of neuronal autophagy dysfunction , high ANMT levels become a trigger for neurodegeneration . In summary , ANMT-1/NNMT is a pivotal element in neuronal cell metabolism that regulates neuronal homeostasis and may contribute to the prevalence of neurological disorders .
NNMT in humans is suspected of being involved in the progression of PD , which is characterized by loss of DA neurons in the substantia nigra in the brain . Also , NNMT may play a role in mental disorders such as schizophrenia and bipolar disorder that both are characterized by disturbances in dopamine levels and signaling . In this context , we investigated the influence of neuronal ANMT-1 in the DA system of C . elegans by expressing anmt-1 using the DA neuronal-specific dat-1 promoter ( anmt-1dopa ) and the MosSCI cloning system , which results in a mild ectopic expression in the DA neuronal tissue . anmt-1 is , in contrast to nnmt in humans , not expressed in the C . elegans nervous system [19] . To control for specific ANMT-1 activity we expressed a mutated version of anmt-1 ( anmt-1dopa-MUT ) in the DA neurons , which contains point mutations in 6 conserved SAM binding sites ( details can be found in Methods ) . A mild ectopic expression of anmt-1 in the GABAergic motor neuronal system ( anmt-1GABA ) was used as control for DA-specific effects . S1A Fig depicts a transgenic animal that expresses GFP under the control of dat-1 ( GFPdopa ) , which was used to visualize the DA nervous system . As PD in humans is an age-dependent disease , we analyzed worms at day 15 after L4 , which represents an old stage of life for these animals , and counted DA neuronal cell bodies . Interestingly , we found that the number of DA neurons in old anmt-1dopa is significantly higher than in wt worms of the same age ( Fig 1A , see S1B Fig for DA neurons broken down into CEP , ADE , and PDE cell subclasses ) . We also analyzed DA neuronal morphology by checking worms for CEP dendrite dysmorphia , axonal breaks , and abnormal cell body and axon positioning as shown in different stages of neurodegeneration in S1A Fig . In agreement with the DA cell body count , 15 days old anmt-1dopa animals display a higher percentage of healthy individuals that do not show the above-mentioned morphological defects when compared to wt ( Fig 1B ) . Analyses of neurodegeneration phenotypes at different ages revealed that a tendency towards less morphological defects could be observed as early as day 5 of adulthood ( S1D Fig ) , whereas no differences were found for DA cell body quantity at young ages ( S1C and S1E Fig ) , Fig 1C depicts the presence of DA neurons in anmt-1dopa and wt animals over time . Notably , the two investigated strains that express a mutated version of anmt-1 ( anmt-1dopa-MUT 1 and 2 ) did not show decreased neurodegeneration when compared to wt at day 15 of adulthood ( S1F Fig ) . Table 1 contains data from a pulse and chase experiment where anmt-1dopa animals with an exclusively neuronal RNAi-sensitive background were treated with RNAi against anmt-1 . Worms were put on anmt-1 RNAi at different time points and microscopy for neurodegeneration was performed at day 15 of adulthood . The beneficial effect of neuronal anmt-1 expression is completely abolished when worms were fed anmt-1 RNAi beginning from the young adult stage as DA neuronal cell body quantity is indistinguishable from wt animals of the same age ( S1G Fig; see Table 1 for other neurodegeneration phenotypes ) . Interestingly , anmt-1dopa animals that were treated with anmt-1 RNAi from hatching experience worse neurodegeneration than wt at day 15 ( S1G Fig ) , pointing towards a critical role for anmt-1 during neurodevelopment . The GABAergic nervous system , as visualized with an mCherry reporter ( mCherryGABA; S2A Fig ) , was not affected by anmt-1: No differences in GABAergic cell body and commissure number ( S2B and S2C Fig ) , and GABAergic axonal breaks ( S2D Fig ) were observed in anmt-1GABA and wt at day 15 . We wondered whether the lack of age-dependent neurodegeneration in anmt-1dopa might also affect their longevity and found that indeed their lifespan is significantly extended compared to wt ( Fig 1D ) , whereas lifespan analyses in anmt-1dopa-MUT 1 and 2 , transgenics that have mutations potentially disturbing ANMT-1 enzymatic function , did not yield such a result ( S1H Fig ) . anmt-1dopa with a neuronal-sensitive background for RNAi treated with anmt-1 RNAi lived significantly shorter than anmt-1dopa fed with control RNAi ( S1I Fig ) , suggesting that indeed the ectopic anmt-1 expression in DA neurons is responsible for the observed longevity . Surprisingly , anmt-1GABA transgenics also showed a slight lifespan extension ( S2E Fig ) , although no effect on GABAergic neurodegeneration was observed . Furthermore , fertility tests revealed a reduced brood size in anmt-1dopa ( S1J Fig ) , but not in anmt-1GABA animals ( S2F Fig ) . These data suggest that DA neuronal ANMT-1 signaling regulates not only neurodegeneration and aging , but also influences reproduction , suggesting that ANMT-1 may act in an endocrine or neuroendocrine manner . Previous research by us and other laboratories has shown that some of the beneficial effects of ANMT-1/NNMT are due to elevated concentrations of MNA , the product of NAM methylation [16] . We wondered whether this is also the case in neurodegeneration and incubated wt worms with 1 μM MNA , a concentration that has been shown previously to be lifespan-extending in C . elegans [19] . At 15 days after L4 , these worms showed no significant differences in DA neuron number ( S1K Fig ) and morphology of the DA system compared to controls ( S1L Fig ) . Thus , we concluded that increasing MNA levels alone are insufficient for the beneficial effects of neuronal anmt-1 expression . Since we found DA neurodegeneration modulated by ANMT-1 , we wondered whether DA neuronal anmt-1 expression influences dopamine-dependent behaviors in C . elegans , possibly resembling features of schizophrenia in humans , which is characterized by dysfunctional dopamine signaling . Therefore , we tested two common dopamine-dependent behaviors in anmt-1dopa animals , namely the abilities to sense food and ethanol [33 , 34] . Briefly , when C . elegans is kept without food , they survey their environment for a potential food source . The mechanic stimulus of bacteria will make them slow down and they will remain on the food rather than moving on to other areas . This so-called “basal slowing response” is mediated by DA neurons and dopamine signaling , as is the avoidance response to ethanol . At 1 and 5 days after L4 , anmt-1dopa worms do not stick to a discovered food source like wt worms ( Fig 2A ) . Furthermore , they do not actively avoid the smell of ethanol at day 5 ( Fig 2B ) . It is interesting to note that these behavioral abnormalities manifest only in adult worms , as they are not present in L4 larvae ( Fig 2A and 2B ) . The fact that these behavioral assays rely on movement prevented us from examining older animals that are less mobile . We therefore tested basal slowing employing a different approach according to [35] . In short , we counted body bends of worms washed free of bacteria and put either on empty NGM plates or plates seeded with OP50 . Wt animals had a significantly lower number of body bends when put on OP50 than on empty plates at day 1 ( S3A Fig ) , 5 ( S3B Fig ) , and 10 ( S3C Fig ) of adulthood . This was not the case in anmtdopa transgenics as their movement was the same on empty plates and plates with bacteria , confirming the phenotype of Fig 2A . The behavior of anmt-1dopa-MUT 1 and 2 , however , resembled as expected that of wt worms , because ANMT-1 is not functional ( S3D Fig ) . Additional phenotypes related to impaired dopamine signaling , such as swimming-induced paralysis , could not be observed . C . elegans locomotion is GABA-dependent , thus we examined locomotion in anmt-1GABA transgenics , but we did not observe any differences compared to wt controls in either 5 or 10 days old worms ( S2G and S2H Fig ) . Abnormal dopamine-dependent behaviors have been observed previously in C . elegans with low dopamine levels , and could be rescued with external dopamine [36] . Therefore , we incubated worms with 50 mM dopamine for 4 to 6 hours before basal slowing response and ethanol avoidance assays were performed , which however did not change either of these behaviors ( S3E and S3F Fig ) . We then tested dopamine and GABA concentration in anmt-1dopa and anmt-1GABA , respectively . A significant increase to 8 ± 2 pmol dopamine per mg protein was found in anmt-1dopa compared to wt animals with 3 . 9 ± 0 . 9 pmol/mg ( Fig 2C ) . These elevated levels explain why additional external dopamine could not correct the dopamine-dependent behaviors . anmt-1 overexpressed in GABAergic neurons had no impact on GABA concentration ( S2I Fig ) . Because of the increase dopamine levels in anmt-1dopa , we wondered whether the longevity and decreased neurodegeneration of this strain was dependent on dopamine synthesis and signaling . Therefore , we crossed anmt-1dopa animals into cat-2 ( n4547 ) worms , which have a loss-of-function deletion in the gene that encodes the tyrosine hydroxylase CAT-2 , the key enzyme in dopamine synthesis [36] . cat-2 ( n4547 ) ;anmt-1dopa animals do not show increased lifespan compared to cat-2 ( n4547 ) controls ( S3G Fig ) . C . elegans has at least three dopamine receptors . DOP-1 and DOP-3 , homologs of mammalian D1 and D2 dopamine receptors , that work together antagonistically to regulate dopamine-dependent locomotion , whereas a function for DOP-2 has not yet been determined [35] . In contrast to disabled dopamine synthesis , anmt-1dopa animals with a deletion in dop-3 ( dop-3 ( vs106 ) ;anmt-1dopa ) , hence disturbed dopamine signaling , still show lifespan extension ( S3H Fig ) , suggesting that dopamine production and elevated levels may mediate longevity , rather than DOP-3 signaling . We also tested neurodegeneration in old ( 15 d ) cat-2 ( n4547 ) and dop-3 ( vs106 ) animals and found that anmt-1dopa is still able to prevent loss of DA neurons in cat-2 ( n4547 ) ;anmt-1dopa and dop-3 ( vs106 ) ;anmt-1dopa ( Figs 2D and S3I ) , therefore acting independently of CAT-2 and DOP-3 in regards to neuroprotection . Since neither the ANMT-1/NNMT metabolite MNA , nor dopamine signaling was responsible for the neuroprotective effect of DA neuronal anmt-1 expression , we wondered if the methylation itself that is catalyzed by ANMT-1/NNMT influences neuronal cell metabolism . ANMT-1/NNMT uses SAM as methyl group donor , and as the reaction to MNA is irreversible , the methyl groups used cannot be recycled to recreate SAM , a mechanism that is highly conserved through evolution [22] . It has been reported in yeast that decreasing SAM levels act as stress and/or starvation signal for the cell to induce autophagy [37 , 38] . Autophagy is a tightly regulated catabolic process within cell metabolism to degrade misfolded proteins and damaged macromolecules , and dysregulation resulting in too high or too low levels is observed in PD and schizophrenia [9 , 13] . We hypothesized that neuronal ANMT-1/NNMT activates autophagy via decreasing SAM levels . Overly active autophagy could be problematic in early life and the abnormal behavior we observed in young adult animals might be a result of excessive autophagy . In contrast , these same autophagy levels might be of advantage later , degrading misfolded proteins and dysfunctional macromolecules as they increase with age . Since the behavioral phenotypes are not accompanied by DA neurodegeneration at older stages , we hypothesized that elevated autophagy helps to maintain neuronal health and extends lifespan in anmt-1dopa animals . We tested whether the phenotypes we observed in anmt-1dopa worms could be reversed by inhibiting neuronal autophagy . We achieved this by knocking down the neuronal expression of essential genes driving autophagy ( bec-1/BECN1 , atg-13/ATG13 , lgg-1/MAP1LC3A ) using RNAi in an exclusively neuronal RNAi-sensitive background . bec-1 and atg-13 are critical for initiating autophagy , and lgg-1 is important at the later stage of autophagosome formation [39] . Applying RNAi against each of these genes rescued the abnormal basal slowing response of anmt-1dopa animals at 5 days of adulthood ( Fig 3A ) and abolished the neuroprotective effect on DA cell body maintenance and morphology at day 15 ( Figs 3B , 3C and S4A ) . Similar treatment had no effect , or was even beneficial in regards to neurodegeneration , in control animals ( S4B , S4C and S4D Fig ) . Notably , the neuroprotective effect of anmt-1dopa is not only abolished when these worms experience a knockdown in neuronal autophagy genes , but anmt-1dopa show an increased loss of DA cells and a higher degree of morphological defects compared to wt under these circumstances ( Fig 3B and 3C ) . The same effect in anmt-1dopa was observed at day 5 ( S4E and S4F Fig ) , whereas the contrary could be found in wt ( S4G and S4H Fig ) . This suggests a secondary increase in neurodegeneration due to DA neuronal anmt-1 expression when autophagy is dysfunctional , which could contribute to PD progression under these circumstances . We wondered if these results are reflected in life expectancy and thus , analyzed lifespan of both neuronal RNAi-sensitive anmt-1dopa and wt animals treated with bec-1 , atg-13 and lgg-1 RNAis . As expected , the above-mentioned RNAis decreased lifespan significantly in anmt-1dopa animals ( Fig 3D ) , strikingly well below wt life expectancy . In wt , neuronal loss of bec-1 and atg-13 slightly extended lifespan , while lgg-1 RNAi decreased lifespan ( S4I Fig ) . In sum , these data suggest that the behavioral changes , neuroprotection , and lifespan extension observed in anmt-1dopa animals is dependent on neuronal autophagy . This beneficial effect on neuronal health and lifespan is reversed when autophagy is depleted , revealing a potential mechanism of how high anmt-1/NNMT expression could increase PD risk . To investigate more directly whether ANMT-1/NNMT is able to regulate autophagy , we used a reporter strain in which LGG-1 is tagged with mCherry [Pnhx-2::mCherry::lgg-1] . LGG-1 occurs diffusely in the cytosol under non-stressed conditions . Autophagy induction leads to LGG-1 organisation around autophagosomes , which then become visible and quantifiable as puncta . To induce autophagy , we starved worms for about 24h prior quantification and observed an increase in the average number of autophagosomes/puncta per wt worm , ranging from 34 . 4 ± 12 . 9 to 110 ± 35 . 5 in 1 day old adults ( S5A and S5B Fig ) . We also used pimozide as positive control , since it has been identified as autophagy inducer in C . elegans [40] , and the potent autophagy inhibitor 3-methyladenine in starved worms as negative control [41] ( S5A Fig ) . To investigate whether autophagy levels change during aging , we determined basal autophagy levels in wt animals in 5 and 10 days old adults . Puncta quantity was similar in 5 days old worms versus 1 day old , but later increased significantly to 107 . 7 ± 25 . 3 per animal at day 10 of adulthood ( S5C Fig ) . Strong background fluorescent prevented us from quantifying puncta at later time points . Interestingly , the increase in puncta formation due to starvation appeared to be progressively dysregulated with aging , as the increase in young adults was > 3-fold when compared to fed animals , > 2-fold at day 5 , and no increase at all could be detected at day 10 ( S5C Fig ) . Although LGG-1 puncta do occur in neurons in general , quantification in the DA neurons is difficult , as number and size of these cells are too small to gain evaluable results . We therefore decided to address this aspect in the whole animal instead of only DA neurons . A strain that overexpresses anmt-1 under the control of its endogenous promoter ( Panmt-1::anmt-1; anmt-1OEx ) , as well as anmt-1 ( gk457 ) , a loss-of-function deletion mutant of anmt-1 , were crossed into the mCherry::lgg-1 autophagy reporter strain and puncta in whole worms were quantified at various time points . We found that basal levels of autophagy were significantly higher in anmt-1OEx transgenics and significantly lower in anmt-1 ( gk457 ) mutants ( Fig 4A and 4B ) . When worms were starved for 24h to induce autophagy the same significant differences could be observed ( Fig 4A and 4B ) , suggesting that ANMT-1 regulates autophagy proportionally to its expression . Subsequently , we investigated whether SAM is the mediator between ANMT-1 and autophagy . Since ANMT-1 uses methyl groups from SAM , we hypothesized it regulates its cellular levels . We analyzed SAM concentrations depending on anmt-1 expression and found significantly lower SAM levels in anmt-1OEx worms , and significantly higher levels in anmt-1 ( gk457 ) mutants ( Fig 4C ) , suggesting that ANMT-1 indeed regulates cellular SAM concentration . SAM levels were also analyzed in anmt-1dopa whole animals and lower concentrations were found compared to wt ( Fig 4D ) , which is surprising here given that anmt-1 is overexpressed in only eight cells of the animal . When SAM is used in a metabolic reaction , it is hydrolyzed to yield homocysteine . Homocysteine is converted either to cysteine or via tetrahydrofolate into methionine , which leads to recycling of SAM [42] . When methyl groups are not limited in the cell , SAM and homocysteine reciprocally regulate each other; i . e . high SAM causes low homocysteine , and vice versa . In anmt-1 ( gk457 ) worms SAM is not used by ANMT-1 and its levels increase ( Fig 4C ) , whereas homocysteine expectedly decreases ( S5D Fig ) . In anmt-1OEx and anmt-1dopa , however , both SAM and homocysteine levels are decreased ( Figs 4C and 4D and S5D and S5E ) , suggesting the cycle is out of balance and methyl groups do indeed appear lost from metabolism . In C . elegans , the majority of SAM is synthesized from methionine by S-adenosyl methionine synthetase ( SAMS-1 ) , which is encoded by the sams-1 gene [43] . sams-1 ( ok3033 ) animals carry a major deletion in sams-1 , leading to loss of function of the protein . We found that SAM concentration in sams-1 ( ok3033 ) mutants is with 2 . 3 nM/mg protein about 50% lower than in wt worms ( S5F Fig ) . As expected given the low SAM levels , we found significantly increased puncta numbers in sams-1 ( ok3033 ) compared to wt , suggesting that sams-1 ( ok3033 ) mutants have increased levels of autophagy ( S5G Fig ) . Notably , starvation did not further elevate puncta formation , consistent with a dysregulation of autophagic processes in the absence of sams-1 and reduced SAM . To directly address whether varying SAM levels are indeed responsible for autophagy regulation due to ANMT-1 , we deprived anmt-1OEx and anmt-1 ( gk457 ) animals genetically from SAM by crossing them with sams-1 ( ok3033 ) mutants . We found the regulation of autophagosome formation through anmt-1 completely abolished , as indicated by anmt-1OEx and anmt-1 ( gk457 ) mutants showing the same autophagy levels in a sams-1 ( ok3033 ) background ( Figs 4E , S5H and S5I ) . On a side note , we observed that anmt-1 ( gk457 ) ;sams-1 ( ok3033 ) double mutants were completely sterile , suggesting an important developmental aspect of this pathway . Similarly , anmt-1OEx;sams-1 ( ok3033 ) animals did not have progeny that were homozygous for both the transgene and the mutation , indicating that the overexpression of anmt-1 is lethal when SAM abundance is reduced or limited . To examine whether the phenotypes observed in anmt-1dopa are dependent on SAM , we analyzed the presence of DA neurons and morphology at day 15 , as well as lifespan in anmt-1dopa;sams-1 ( ok3033 ) . sams-1 ( ok3033 ) seems to be neuroprotective for DA neurons and increases lifespan compared to wt ( Fig 4F , 4G and 4H ) . However , anmt-1dopa could not further increase these beneficial effects ( Fig 4F , 4G and 4H ) , suggesting that the neuroprotection and longevity caused by anmt-1dopa and loss of sams-1 share a common mechanism , which include the reduction of SAM availability , hence activating autophagy . In yeast , SAM regulates autophagy through the methyltransferase Ppm1p , an evolutionary conserved enzyme with orthologs in humans ( leucine carboxyl methyltransferase , LCMT1 ) and C . elegans ( LCMT-1 ) . Ppm1p uses methyl groups provided by SAM to methylate and therefore activate the catalytic subunit of PP2Ap ( protein phosphatase 2A in humans , LET-92 in C . elegans ) . Methylated PP2A then induces dephosphorylation of Npr2p ( human NPR2-like , GATOR1 complex subunit and C . elegans NPRL-2 ) , which is part of a complex that controls autophagy and cell growth via the regulation of TORC1 , and potentially others [37 , 44] . In C . elegans , the pathway is reportedly involved in reproduction and development [45] whereas its neuronal role has not been further investigated . However , it is known that let-92 is highly expressed in the neurons [46] . We speculate that ANMT-1 competes with LCMT-1 for methyl groups from SAM , impairing the methylation ability of LCMT-1 . This leads to decreased methylation and activity of LET-92 , which can no longer dephosphorylate NPRL-2 , thus inducing autophagy . Indeed , neuronal-specific RNAi downregulation of the genes involved in the pathway , lcmt-1 and nprl-2 , in anmt-1dopa animals led to decreased DA cell body number at day 15 ( Figs 5A and S6A ) , and was milder at day 5 ( S6B Fig ) . In wt , the same treatment caused no , or a slightly beneficial , effect on DA neuronal loss at day 5 and 15 ( S6C and S6D Fig ) . Morphological damage was strongly increased by neuronal loss of lcmt-1 and nprl-2 in anmt-1dopa at day 5 ( S6E Fig ) and day 15 ( Fig 5B ) , but decreased in wt at day 5 and 15 ( S6F and S6G Fig ) . Both neuronal lcmt-1 and nprl-2 RNAi had a slight lifespan shortening effect in anmt-1dopa animals ( Fig 5C ) in contrast to wt , where lcmt-1 RNAi had no effect and nprl-2 RNAi extended lifespan ( S6H Fig ) . We were unable to test the contribution of PP2A as it is an essential gene in C . elegans , and even RNAi applied only from L4 in an exclusively neuronal-specific RNAi-sensitive background had many non-specific effects that confounded phenotypic analyses . The data obtained from neuronal lcmt-1 and nprl-2 loss resemble the results when autophagy was blocked by bec-1 , atg-13 and lgg-1 RNAi ( Figs 3 and S4 ) , suggesting that knocking down lcmt-1 and nprl-2 might indeed affect autophagy . Subsequently , the mCherry::lgg-1 autophagy reporter strain was grown on lcmt-1 and nprl-2 RNAi and a downregulation of basal autophagy under feeding conditions compared to the control RNAi was found , as puncta quantity was significantly lower between the groups ( Fig 5D ) . In 1 day old adults , a small increase from 28 . 2 ± 13/28 . 9 ± 14 puncta per animal under feeding conditions to 37 . 7 ± 13 . 1/41 . 3 ± 10 . 8 puncta following starvation was observed when worms were grown on lcmt-1 and nprl-2 RNAi , respectively ( Fig 5D ) . At day 5 this effect was completely abolished ( S6I Fig ) , suggesting that lcmt-1 and nprl-2 play an important role in regulating starvation-induced autophagy . The same pattern was observed in anmt-1OEx , where RNAi against lcmt-1 and nprl-2 was able to completely abolish starvation-induced autophagy in 1 day old adults ( Fig 5E ) . Interestingly , knocking down lcmt-1 and nprl-2 also led to downregulation of basal autophagy levels of anmt-1OEx back to wt levels ( S6J Fig ) . Strikingly , in anmt-1 ( gk457 ) mutants starvation could still induce autophagy when grown on lcmt-1 and nprl-2 RNAi ( Fig 5F ) , suggesting that the pathway including lcmt-1 and nprl-2 exclusively regulates autophagy in the presence of ANMT-1 . Autophagy is known to be subject to epigenetic regulation , and NNMT has been shown to regulate epigenetic processes by modifying SAM concentrations , influencing the progression of tumorigenesis [22 , 47] . Whereas the epigenetic link between NNMT and autophagy is beyond the scope of this study and needs further investigation , we wondered how autophagy and sams-1 gene expression is influenced by anmt-1 . Notably , besides post-translational regulation , an important role for transcriptional control of autophagy has been described in recent research [47 , 48] . Thus , we analyzed gene expression of atg-13 and lgg-1 in mixed populations of anmt-1OEx , anmt-1dopa , and anmt-1 ( gk457 ) animals . We found a stable upregulation of both genes by anmt-1OEx and anmt-1dopa compared to wt , whereas anmt-1 ( gk457 ) had no effect ( Fig 6A ) . Since ANMT-1 controls autophagy via the lcmt-1/nprl-2 pathway , we sought to test the expression of these genes and found that nprl-2 was upregulated by both anmt-1OEx and anmt-1dopa , suggesting that ANMT-1-induced autophagy is regulated both post-translationally and transcriptionally ( Fig 6B ) . However , the results of lcmt-1 gene expression showed pronounced differences between the experiments , leading to high standard deviations ( Fig 6B ) , which is consistent with a potential post-translational nature of LCMT-1 activity regulation via SAM . Surprisingly , we also found a strong transcriptional downregulation of sams-1 only through anmt-1dopa ( Fig 6C ) . This could mean that lower SAM levels in anmt-1dopa are not exclusively due to metabolic consumption , but could also be regulated on a transcriptional level , potentially through epigenetic effects . Notably , all tested genes were regulated by anmt-1dopa . We hypothesize that the effect of gene regulation in only the eight DA neurons would be too small to detect in whole animals , thus again hinting towards an endocrine-like signaling of anmt-1dopa that effects not only the nervous system , but potentially the whole organism . In summary , these data reveal an alternative pathway of SAM and autophagy regulation in addition to post-translational/metabolic regulation , involving control on the transcriptional level , possibly through epigenetic processes . After establishing a role of ANMT-1 in regulation of autophagy , especially in the neurons , and therefore preventing age-related neurodegeneration , we wondered if anmt-1 expression was neuroprotective in disease-like states , induced by either PD-related neurotoxins or mutations . Thus , we tested a variety of compounds that have been associated with DA neurodegeneration and/or increased risk of PD onset to determine whether increased anmt-1 expression influences the neurotoxicity of these compounds . Worms were exposed to the respective substances from the egg stage and DA morphology was measured at L4 , day 5 and day 10 of adulthood . β-hexachlorocyclohexane ( β-HCH ) is classified as persistent organic pollutant and serum levels may correlate with PD onset risk [49] . We tested the ability of β-HCH ( 1 mM ) to damage C . elegans DA neurons and found increased morphological damage in wt as early as in the L4 stage when compared to DMSO ( S7A and S7B Fig , S1 Table for statistics ) . Paraquat ( PQ; 300 μM ) and 6-OHDA ( 1 mM ) caused a trend towards DA morphological damage at L4 and day 5 ( S7A , S7B , S7C and S7D Fig ) that became significant in 10-day old worms ( Figs 7A and S7E ) when compared to controls . Both compounds can damage DA neurons , which was reported previously in C . elegans [50 , 51] and other organisms as well as humans [52–54] , and are therefore suspected to cause PD . Surprisingly , anmt-1dopa worms seemed to be completely protected against the neurotoxic effects of β-HCH , PQ , and 6-OHDA ( Figs 7A , S7A , S7B , S7C , S7D and S7E ) . While this neuroprotective effect of ANMT-1 may be due to increased autophagy , other mechanisms could contribute to this resilience , such as upregulated stress response caused by oxidative stress , which is evoked by PQ and 6-OHDA and has been reported previously for ANMT-1 [19] . However , given the structural and functional differences of the tested compounds , it is likely that the neuroprotection caused by anmt-1dopa is a general effect rather than specific to a particular family of molecules . To investigate whether anmt-1 interacts with genetic risk factors for PD and influences their pathologies , we tested a variety of disease models . An autosomal-dominant mutation in the alpha-synuclein gene SNCA ( an alanine-to-threonine substitution at position 53 , A53T ) was the first discovered to be responsible for heritable cases of PD [6 , 55] . Shortly after , mutations in the parkin gene PARK2 were found to be responsible for autosomal-recessive juvenile PD [7] . Later , mutations in both genes have been found to contribute to sporadic PD cases as well [56] . Therefore , we employed a C . elegans model that expresses human SNCA A53T in their DA neurons ( Pdat-1::SNCA-A53T; SNCA-A53Tdopa ) , and pdr-1 ( gk488 ) , a loss of function mutant of pdr-1 , the worm orthologue of human PARK2 . As previously shown [57 , 58] , we found that both strains show degeneration of their DA neurons at day 5 of adulthood , as indicated by the loss of DA cell bodies ( S8A Fig ) and abnormal positioning of these cells ( S8B Fig ) , and other morphologic anomalies of the DA system ( S8C and S8D Fig ) . When anmt-1 was expressed in the DA neurons of these strains ( SNCA-A53Tdopa;anmt-1dopa and pdr-1 ( gk488 ) ;anmt-1dopa ) , we found that the loss and abnormal positioning of DA cell bodies compared to wt was completely abolished at day 5 ( S8A and S8B Fig ) and day 15 ( Figs 7B , 7C and S8E ) , and dysmorphia of CEP dendrites ( S8C and S8F Fig ) , and axonal breaks ( S8D and S8G Fig ) are the same as in anmt-1dopa . SNCA tends to build aggregates , especially when mutated . It has furthermore been reported that SNCA and SNCA-A53T might diminish autophagic processes [59] . The E3 ubiquitin ligase PARK2 is an important mediator of mitophagy , which is the selective autophagic degradation of mitochondria . We speculate that the effects of SNCA A53T and pdr-1 loss on DA neurons are , at least in part , due to accumulation of aggregated SNCA and damaged mitochondria , respectively . anmt-1 expression induces autophagic processes , which could lead to reduction of these aggregates and dysfunctional organelles , restoring neuronal function .
We explored the neuronal role of ANMT-1/NNMT in vivo and found that it regulates neuronal autophagy ( Fig 8 ) in the DA nervous system , with wide-ranging effects on neurodegeneration , behavior , fertility , and lifespan . NNMT has been previously reported as eliciting contradictory outcomes regarding PD risk: elevated NNMT levels were found in the brains and lumbar cerebrospinal fluid of PD patients [28 , 60 , 61] , whereas other studies in cell tissue culture found it to be neuroprotective [32 , 62–64] . Our data suggest a neuroprotective role for ANMT-1/NNMT , but it cannot be ruled out that higher expression levels and/or encountering other risk factors could lead to further dysregulation and neurodegeneration . The neuroprotection results from the deprivation of SAM , which likely acts as starvation signal to the cell . It has been shown that SAM reciprocally regulates autophagy , promoting growth under high concentrations and boosting autophagy when levels decrease [37 , 38] . SAMS-1 , the key enzyme in SAM biosynthesis , was initially identified in an RNAi screen for positive regulators of longevity via dietary restriction [43] . sams-1 mutants show extended lifespan and mimic other phenotypes of DR worms , such as reduced brood size and delayed reproduction [65] , resembling the phenotypes that we observe in anmt-1dopa worms . Reduced SAMS-1 mRNA levels as in anmt-1dopa have also been described in genetic models of dietary restriction [43] . We therefore hypothesize that high neuronal ANMT-1/NNMT activity mimics dietary restriction by reducing the availability of cellular SAM , leading to lifespan extension . Furthermore , decreased SAM , and hence reduced methylation potential , could modulate histone and DNA methylation and affect epigenetic processes [22] . In young ( 5 day old ) anmt-1dopa individuals , however , dopamine-dependent behavior is disturbed , which could be due to increased dopamine levels in anmt-1dopa animals . It is interesting to note that schizophrenia , with a general onset age in early adulthood [66] , is associated with excessive dopamine , leading to abnormal signaling and the typical behavioral outcomes . Therapy involves the use of antipsychotic drugs that block dopamine receptors , whereas drugs that drive dopamine release or increase dopamine transmission , such as amphetamines , will exacerbate psychosis in patients with schizophrenia , and can induce schizophrenia-like symptoms in otherwise healthy individuals [15] . NNMT has been associated with schizophrenia in humans [23–25] , which according to our results may be due to its influence on dopamine concentration and/or signaling . Furthermore , autophagy dysregulation in the brain plays a key role in the pathology of schizophrenia [13] , and an NNMT-mediated increase in autophagy could therefore also contribute to the progression of the disease . Since ANMT-1/NNMT seems to increase autophagy levels independently of age , perhaps levels are too high in young adulthood , and become beneficial only with age as the incidence of damaged macromolecules and dysfunctional organelles in the neurons increase . Increased autophagic clearance could therefore be the basis of the ANMT-1/NNMT-dependent neuroprotection and lifespan extension we observed . Autophagy has been linked to longevity in many organisms and an emerging field of investigation concerns the differential regulation of autophagy during aging , and effects on longevity [67] and neurodegeneration [68] have been reported depending on the relative age of the organism in question . We chose to examine neurodegeneration phenotypes at the age of 15 days after L4 , which may resemble the human age that is most prevalent for PD onset ( around 65 years ) [69] . Future research could continue in this direction and establish whether an increase in autophagic processes only in older age is sufficient to mediate the beneficial effects without influencing other autophagy-sensitive diseases in younger individuals . Notably , we also found some neurodevelopmental issues in L4 larvae ( S1E Fig ) that were expected to not experience any neuronal loss , which could not be confirmed by our analysis . It would be interesting to further investigate whether the individuals that have neurodevelopmental problems experience an earlier onset or faster progression of neurodegeneration in older age . Given the influence of anmt-1 expression in the DA neurons on dopamine , and the dependency of lifespan extension on dopamine production , we speculate that dopamine might act beyond its known functions and perhaps via receptors not yet described . However , we found a greater loss of DA cells and higher morphological damage , i . e . features of PD , in anmt-1dopa than in wt animals when autophagy was abrogated . Maybe this secondary increase in neurodegeneration , when ANMT-1/NNMT levels are high and autophagy is dysfunctional , could account for the increased NNMT expression in PD patients observed in other studies [28 , 60] . The LCMT-1/NPRL-2 pathway , which links ANMT-1/NNMT to autophagy regulation , also involves PP2A . We were not able to test the worm ortholog LET-92 , however , given its high expression levels in the nervous system , it is likely to have an important neuronal role [46] . Recently , it has been reported that NNMT silencing is able to activate PP2A via its effects on LCMT-1 in glioblastoma cells [70] . Subsequently , this activation of PP2A lead to inactivation of serine threonine kinases ( STKs ) . A genome-wide association study on PD in large populations of Europe and the USA found that polymorphisms in the gene encoding STK39 significantly increases the risk for PD [10] . Thus , in the context of dysfunctional autophagy , NNMT might modulate the activity of STKs such as STK39 to trigger DA neurodegeneration , while under wt conditions the overall beneficial effects of autophagy outweigh the potentially damaging effect of modulating STK39 . Interestingly , the antipsychotic drug perphenazine , currently used to treat schizophrenia , activates PP2A and rescues a potential PP2A inhibition by NNMT [70] . Taken together , our research shows the contribution of NNMT to neuroprotection and its involvement in neuronal diseases and provides evidence for autophagy as underlying biochemical pathway . We have detailed this novel molecular mechanism regulating neuronal autophagy during aging and raise the possibility of the NNMT pathway as a potential target for neuroprotective interventions in PD , schizophrenia , and other neurological diseases . Further research is required to enlighten the DA-neuronal specificity of NNMT action , and to investigate how epigenetic regulation intervenes in these processes .
C . elegans were maintained as described elsewhere [71] . Briefly , worms were kept on NGM agar plates that were streaked with E . coli OP50 as food source at 15°C . All assays were performed at 20°C , and worms were grown at 20°C at least one generation before the experiment . The N2 Bristol wildtype strain ( wt ) , as well as BZ555 ( Pdat-1::GFP; GFPdopa ) , MT15620 ( cat-2 ( n4547 ) ) , LX703 ( dop-3 ( vs106 ) ) , VK1093 ( vkEx1093[nhx-2p::mCherry::lgg-1] ) , TU3401 ( sid-1 ( pk3321 ) ;[pCFJ90 ( Pmyo-2::mCherry ) + unc-119p::sid-1] ) , RB2240 ( sams-1 ( ok3033 ) ) , and VC1024 ( pdr-1 ( gk488 ) ) were provided by the Caenorhabditis Genetics Center at the University of Minnesota . Mutant strains were outcrossed to wt at least 4 times . Strains MIR8 ( Panmt-1::anmt-1::GFP; anmt-1OEx ) and MIR16 ( anmt-1 ( gk457 ) ) were made as described previously [19] . FX14471 ( tmIS904; Pdat-1::a-syn A53T ) were generated by the Iwatsubo lab [72] and obtained from Dr . Shohei Mitani . Other C . elegans strains obtained by crossing and used in this study can be found in Table 2 . Homozygosity of all genotypes was confirmed by PCR . Transgenic animals were generated as follows . Plasmid DNA with the anmt-1 gene under the control of either a DA-neuronal ( dat-1 ) or GABA-motor neuron specific promoter ( unc-47 ) was prepared using the Gateway Cloning system and the site-specific vector pCFJ606 . Transgenics were generated by microinjection of plasmid DNA and stably integrated into a defined site of the genome ( locus ttTi14024 , position X:22 . 84 ) using the MosSCI technique . Alternatively , the dat-1p::anmt-1 construct , or a mutated version of this construct , together with a co-injection marker were injected into BZ555 , resulting in extrachromosomal expression of either wt or mutated anmt-1 in the DA nervous system . anmt-1 was mutated using site-directed mutagenesis , and two resulting lines were analyzed ( anmt-1dopa-MUT 1 and anmt-1dopa-MUT 2 ) . Strains were outcrossed 4 times to wt . Amino acid residues of ANMT-1 that are important for SAM binding were identified using UniProt [73] ( entry P34254 ) , which provided 5 potential binding sites at positions 35 ( tyrosine; Y ) , 40 ( Y ) , 80 ( Y ) , 96 ( aspartic acid; D ) and 101 ( asparagine; N ) . Protein sequence alignment of C . elegans ANMT-1 and human NNMT showed high conservation between these residues . Analysing the crystal structure of human NNMT , Peng et al . reported an additional important residue at position 197 ( D ) [74] , which is potentially conserved with a small gap , given the existence of a D in the ANMT-1 sequence at position 219 that matches the amino acid context of D197 in NNMT . All identified potentially active residues where replaced by alanine ( A ) , resulting in the following mutations: Y35A , Y40A , Y80A , D96A , N101A , and D219A . Mutations were generated using a QuikChange II XL Site-Directed Mutagenesis Kit ( Agilent Technologies ) . RNAi experiments were performed according to Kamath et al . [75] . RNAi clones ( E . coli HT115 ) of bec-1 ( T19E7 . 3 ) , atg-13 ( D2007 . 5 ) , lgg-1 ( C32D5 . 9 ) , lcmt-1 ( B0285 . 4 ) and nprl-2 ( F49E8 . 1 ) were taken from the ORFeome RNAi library ( Open Biosystems ) and compared to an empty vector clone ( L4440 ) . Sequencing to confirm correct clones was performed for all RNAis before use . Adult worms were put on RNAi NGM plates containing 1 mM isopropyl-ß-D-thiogalactopyranoside and 50 μg/ml ampicillin and allowed to lay eggs for about 4 hours . Progeny from L4 on was transferred every two days to avoid contamination with younger generations . Neuronal RNAi experiments were performed using the respective strain crossed into TU3401 for neuronal-specific gene silencing . Synchronized worms of different ages were placed on microscopy slides with 2% agarose pads and immobilized with 5 mM levamisole in M9 buffer . Neuronal fluorescence microscopy was conducted with a Zeiss Axio Imager M2 microscope and Zen Pro software ( Carl Zeiss Canada ) with 40x amplification . Present DA and GABA cell bodies and GABA commissures were counted , and worms were screened for breaks in axons and dysmorphia ( breaks , punctated GFP signal , dislocation ) in CEP dendrites , and abnormal DA cellular positioning . At least fifteen animals were analyzed in each of at least 3 independent experiments per condition and two-tailed t-test was performed to determine significance . Single hermaphrodites at the L4 stage were put on NGM plates and allow to lay eggs . Parental worms were transferred to fresh plates every 12 hours for the first 3 days , then every 24 hours until they stopped laying eggs . Progeny that reached L4 per parental worm was counted . Three independent experiments were performed in quadruplicates . Lifespan assays were performed as previously described [19] . Briefly , worms were synchronized at the egg stage ( day 0 ) . At L4 , around 50 nematodes were transferred to each of 3 fresh lifespan plates per condition . After 24–48 hours , worms were transferred on plates containing 10 μM FUDR to prevent progeny contamination . FUDR was solved in water and applied on top of the grown bacteria lawn . C . elegans that did not react to repeated gentle stimulation were scored as dead . Lost animals or non-natural deaths ( bagging , protrusive vulvae ) were censored . JMP 11 . 0 . 0 ( SAS institute Inc . ) was used for statistical analyses ( see Table 3 ) . The assay plates were prepared as follows: an about 1 cm diameter droplet of OP50 was placed on one site of a 10 cm NGM petri dish and allowed to dry . About 100 well fed worms per experiment were placed on an empty NGM plate and let crawl up to an hour to get rid of excessive bacteria . Worms were then transferred to assay plates on the opposite site of the bacterial lawn and allowed to move for 1 hour . Subsequently , worms inside and outside the bacterial lawn were counted and the basal slowing index was calculated as follows: ( worms outside of lawn ) - ( worms in lawn ) / ( complete number of worms ) , where a result between 1 and 0 represents a healthy behavior . For verification we used an alternative method of testing basal slowing according to [35] . Worms were synchronized at the L4 stage and experiments were performed at day 1 , 5 , and 10 of adulthood . 35 μl of an overnight E . coli OP50 culture were spread on a 6 cm agar dish and incubated over night at 37°C . Control plates without bacteria were treated the same . Animals were washed free of bacteria with M9 . After 3 min in M9 , worms were allowed a 90 sec recovery period on the respective assay plate . Subsequently , body bends were counted for five consecutive 20 sec periods . A body bend was defined as a change of direction of the complete head and pharynx region relative to the vertical axis . At least 5 animals per condition were tested . The assay plates were prepared as follows: a 10 cm NGM plate streaked with OP50 was quartered . 60 μl 96% ethanol was pipetted on two quarters of the plate and allowed to dry for about 5 mins . About 100 well fed worms per experiment were placed in the center of the plate . Worms were allowed to move freely for 1 hour . Subsequently , worms inside and outside the ethanol quadrants were counted and the chemotaxis index was calculated as follows: ( ( worms outside of lawn ) - ( worms in lawn ) / ( complete number of worms ) ) *-1 , where a result between 1 and 0 represents a healthy behavior . Dopamine drug pre-treatment was performed as described previously [36] . In sum , a 50 mM dopamine hydrochloride solution in M9 buffer was prepared freshly before the assay . 400 μl of this solution were put on a 5 cm NGM plate seeded with OP50 and allowed to dry . For control , 400 μl M9 was added . Worms were put on the prepared dopamine and control plates 4 to 6 hours before the basal slowing response and ethanol avoidance assay . In a 96-well-plate , 30 age-synchronized worms were transferred into a well filled with 100 μl M9 buffer and OP50 . Swimming locomotion was automatically tracked for 10 h using a worm tracking machine ( Wmicrotracker , Phylum Tech ) that performs measurements as follows . Each microtiter well is crossed by two infrared light rays from top to bottom . A detector determines how often the light rays were interrupted by worms moving in the well , and the signal is used to calculate a movement score , which is the amount of animal movement in a fixed time period . All measurements were performed in triplicates in 3 independent experiments and compared to wt worms of the same age . Worms were synchronized , grown on OP50 or RNAi bacteria from the egg stage and transferred to fresh plates every 2 days from day 1 of adulthood . Compound plates ( positive/negative control plates ) were poured fresh before each assay , with pimozide at a concentration of 20 μM and 3-methyladenine at 5 mM dissolved in DMSO . Assays were performed at different ages as follows . Worms were put on empty streptomycin plates for about an hour to get rid of excess bacteria . About 24 hours before the assay , fed worms went back on fresh plates with food , starved worms were placed on the same plates that were not streaked with bacteria . For RNAi experiments , the starvation period was about 16 hours . For puncta assessment , worms were put on microscope slides with 2% agarose pads and immobilized with levamisole , and assessment was performed with Zeiss Axio Imager M2 microscope and Zen Pro software at 587 nm excitation/610 nm emission for mCherry . Pictures were taken and analysed with the “Find maxima” function of ImageJ 1 . 49V . Heterozygous strains ( used when homozygosity of genotype caused sterility ) were put in lysis buffer immediately after microscopy and stored at -20°C for single worm PCR to determine genotype . Detection of dopamine , GABA , SAM and one-carbon metabolites was performed via HPLC ( high performance liquid chromatography ) coupled with ESI-MS/MS ( electrospray ionisation tandem mass spectrometry ) detection . The method was adapted from Wojnicz et al . [76] . Metabolites were extracted by sonication in acidified water ( 1 . 89% formic acid; sonication in ice-cold water for a total of 40 sec , with pulses of 10 sec at 40% intensity using a micro tip probe ) followed by acetonitrile protein precipitation and sample concentration by drying using a refrigerated CentriVap set at 10°C . Reconstituted samples kept at 4°C were injected ( 30μL ) and separated by a Nexera X2 HPLC system ( Shimadzu ) using a C18-PFP column 4 . 6 x 150 mm , 3 μm particle size ( ACE , Scotland ) protected by a C18-PFP guard column 3 . 0 x 10mm , 3μm particle size ( ACE , Scotland ) ; column compartment set at 30°C; gradient elution at 0 . 6mL/min in mobile phases A ( 0 . 1% formic acid in H2Odd ) and B ( acetonitrile ) as follows: 0 min 5% B , 2 min 5% B , 5 min 90% B , 8 min 90% B , 10 min 5% B , 14 min 5% B . Detection was performed by ESI-MS/MS in positive ion mode on a 6500 QTrap ( Sciex ) . Transitions used were for GABA 104 . 0 = >87 . 0 ( collision energy ( CE ) :15 ) , for d2-GABA 106 . 0 = >89 . 0 ( CE:15 ) , for dopamine 154 . 0 = >91 . 0 ( CE:33 ) , for d3-dopamine 157 . 0 = >93 . 0 ( CE:46 ) , for SAM 399 . 0 = >250 . 1 ( CE:21 ) , for d3-SAM 402 . 0 = >250 . 0 ( CE:25 ) , and 136 . 0 = >90 . 0 ( CE:15 ) for homocysteine . Total RNA was obtained using Trizol ( Invitrogen ) /chloroform extraction as described previously [19] , quantified photometrically with a NanoPhotometer ( Implen ) and stored at -80°C until further use . cDNA from 500 ng total RNA was generated using QuantiTect reverse transcriptase ( Qiagen ) and diluted 1:10 to 1:1000 to determine a concentration for each gene that yielded a CT value between 15 and 25 . Gene expression was analyzed using TaqMan Gene Expression Assays ( Applied Biosystems ) and a QuantStudio 3 Real-Time PCR System ( Thermo Fisher ) . Data were normalized to the housekeeping gene cdc-42 and analyzed using the Δ/Δ-CT method . Compound plates were poured fresh before each assay and streaked with OP50 . We chose a concentration of each compound where there was no visible impairment of bacterial or nematode growth . Beta-chlorocyclohexane ( β-HCH ) was dissolved in DMSO and used at a concentration of 1 mM . Paraquat ( PQ ) and 6-Hydroxydopamine ( 6-OHDA ) were dissolved in water and tested at concentrations of 300 μM and 1 mM , respectively . All compounds were obtained from Sigma . Young adult worms were allowed to lay eggs on compound plates for about 4 hours . Progeny was investigated via neuronal fluorescence microscopy at L4 , day 5 , and day 10 , before a significant proportion of the population started to die . Worms were transferred every 2 days to fresh plates . Morphology of the DA system was calculated from cell body count and positioning , presence of axonal breaks , and dysmorphia in CEP dendrites . The category of no degeneration ( “none” ) was assumed when average cell body presence was > 95% , and < 20% of animals showed axonal breaks and abnormal positioning , abnormal cell body positioning , and dysmorphia in CEP dendrites . Slight or severe degeneration was assumed when average cell body presence was > 50% or < 50% , and axonal breaks , abnormal cell body and axon positioning , and dysmorphia in CEP dendrites occurred in < 60% and > 60% of animals , respectively . At least fifteen animals were analyzed in each of at least 3 independent experiments per condition and two-tailed t-test was performed to determine significance . | Neuronal disorders are a threat to human health and understanding the underlying genetic and cellular regulatory networks is a first step towards successful treatment . In this context , it has been suggested in epidemiological studies that the metabolism of nicotinamide , specifically the enzyme nicotinamide N-methyl-transferase ( NNMT ) , could contribute to behavioral and neurodegenerative diseases such as Parkinson’s disease ( PD ) and schizophrenia . We used the simple nematode worm C . elegans and expressed NNMT in its dopaminergic neurons as this specific neuronal population is primarily affected by both PD and schizophrenia . We found that neuronal NNMT expression in C . elegans influences behavior , neurodegeneration , and life expectancy . NNMT activity controls the concentration of S—adenosyl methionine ( SAM ) in the neuronal cell . High NNMT activity leads to low SAM levels that the cell interprets as starvation signal , therefore inducing autophagy , a catabolic process important for balancing energy sources and cell homeostasis . Depending on the age of the worms , this can result in disturbed behavioral paradigms in young animals , or preserves neuronal health and decreases neurodegeneration processes in old individuals . Taken together , these identified mechanisms of NNMT action in C . elegans neurons could provide novel insights into the development of neuronal disorders in humans . | [
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"... | 2018 | Nicotinamide-N-methyltransferase controls behavior, neurodegeneration and lifespan by regulating neuronal autophagy |
Vesicular trafficking plays a key role in tuning the activity of Notch signaling . Here , we describe a novel and conserved Rab geranylgeranyltransferase ( RabGGT ) -α–like subunit that is required for Notch signaling-mediated lateral inhibition and cell fate determination of external sensory organs . This protein is encoded by tempura , and its loss affects the secretion of Scabrous and Delta , two proteins required for proper Notch signaling . We show that Tempura forms a heretofore uncharacterized RabGGT complex that geranylgeranylates Rab1 and Rab11 . This geranylgeranylation is required for their proper subcellular localization . A partial dysfunction of Rab1 affects Scabrous and Delta in the secretory pathway . In addition , a partial loss Rab11 affects trafficking of Delta . In summary , Tempura functions as a new geranylgeranyltransferase that regulates the subcellular localization of Rab1 and Rab11 , which in turn regulate trafficking of Scabrous and Delta , thereby affecting Notch signaling .
Notch signaling is an evolutionarily conserved pathway that plays a pivotal role in many developmental processes , including lateral inhibition , binary cell fate determination , and boundary formation [1] , [2] . Aberrant Notch signaling is implicated in diseases such as Alagille syndrome , spondylocostal dysostosis ( SCD ) , cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy ( CADASIL ) , and numerous types of cancer [3] , [4] . Notch signaling depends on the direct contact between cells: the membrane-bound ligand , Delta ( Dl ) or Serrate , activates Notch on neighboring cells , resulting in proteolytic cleavages of Notch to generate a Notch intracellular domain ( NICD ) that activates the transcription of target genes [5] . The developing adult external sensory organs ( ESOs ) on the Drosophila notum serves as a model system to study lateral inhibition and cell fate determination [6] and have led to the isolation of some Notch signaling components in forward genetic screens [7]–[10] . An ESO consists of four cells—shaft , socket , sheath , and neuron—which are derived from a single mother cell , the sensory organ precursor ( SOP or pI ) ( Figure 1A and 1A′ ) . Lateral inhibition ensures that only one SOP is selected from a proneural cluster . SOPs undergo several rounds of asymmetric cell division to generate four different cells and Notch signaling activity determines cell fates in each division ( Figure 1A and 1A′ ) . Loss of Notch signaling during lateral inhibition results in a higher density of ESOs , whereas its loss during cell fate determination causes ESO cells to take on neuronal fates , resulting in adult notum balding [11] . Notch signaling activity can be altered by defects in vesicular trafficking [12] , coordinated by specific Rabs and their effectors . Rabs are small GTPases belonging to the Ras superfamily of small G proteins , which can switch between GDP-bound inactive and GTP-bound active forms [13]–[15] . Newly synthesized Rabs are prenylated with geranylgeranyl groups on C-terminal cysteines by a protein prenyltransferase ( PPT ) complex , a process required for their proper membrane localization and hence function [16]–[20] . PPTs are composed of αβ heterodimers and they add prenyl lipids ( 15-carbon farnesyl or 20-carbon geranylgeranyl groups ) to cysteine residues located close to the C-termini of their substrates . Three PPTs have been identified so far: farnesyltransferase ( FT ) , geranylgeranyltransferase I ( GGTI ) , and the Rab geranylgeranyl-transferase [RabGGT , also known as geranylgeranyltransferase II ( GGTII ) ] ( Figure S1 ) [21] , [22] . Here , we describe the isolation of mutations in a novel PPT α subunit repeat ( PPTA ) motif containing protein from an unbiased forward mosaic genetic screen for essential genes that affect Notch signaling . Due to its role in adding lipids to its substrates , we name this gene “tempura” ( temp ) , based on a Japanese deep-fried dish . Our data show that Temp forms a new PPT to modify a small subset of Rabs , including Rab1 , which has not previously been implicated in Notch signaling , and Rab11 . Loss of temp leads to aberrant subcellular distribution of Rab1 and Rab11 , which in turn leads to Notch signaling defects . In summary , we describe the function of a previously unidentified PPT , provide the first link between Rab1 and Notch signaling , and show that some Rabs are modified by two nonredundant PPTs . These data imply a complex regulation of Rabs by different PPTs that was not previously appreciated .
To identify novel modulators of Notch signaling , we performed a forward genetic screen on the Drosophila X chromosome using ethyl methanesulfonate ( EMS ) [23] . We induced homozygous mutant clones of essential genes in the notum of otherwise heterozygous mutant animals with the FLP/FRT system [24] , and screened for adult notum balding . We identified a novel complementation group named temp , consisting of seven alleles that exhibit a strong balding phenotype ( Figure 1B ) . To characterize the ESO development defects in temp mutant clones , we examined lateral inhibition and cell fate determination at different pupal stages . At 12 h after puparium formation ( APF ) , the density of SOPs [marked by Senseless ( Sens ) ] [25] is higher in temp mutant clones than in neighboring wild-type ( wt ) tissue , indicating a lateral inhibition defect ( Figure 1C ) . To determine if cell fate specification is impaired , we assessed the expression of Tramtrack ( Ttk ) , a downstream effector of Notch that is up-regulated in the signal-receiving pIIa cell but not in the signal-sending pIIb cell at 19 h AFP [26] , [27] . We observed a loss of Ttk in temp mutant pIIa , indicating a loss of Notch signaling during cell fate determination at the two-cell stage ( Figure 1D ) . At 27 h APF , when the four cells comprising an ESO are specified , many temp mutant ESOs contain multiple neurons ( marked by the neuronal marker Embryonic Lethal Abnormal Vision , ELAV , [28] and lack socket cells , marked by Suppressor of Hairless , Su ( H ) ) [29] , indicating an alteration in cell fate ( Figure 1A and 1E–F ) . This phenotype is not fully penetrant as 63% of the tempB mutant ESO cells are ELAV-positive , similar to what we observed previously for dEHBP1 and sec15 [8] , [9] , two players that affect vesicle trafficking and Notch signaling during ESO development . Although we observe some minor apoptosis in some tempA mutant clones ( a strong allele; Figure S2A ) , there is no obvious apoptosis in tempB mutant clones ( a less strong allele; Figure S2B ) . Overexpression of the antiapoptotic protein p35 in tempA mutant clones ( Figure S2C ) does not alter the phenotype ( Figure S2D ) when compared to temp mutant clones without p35 expression . In addition , there is no decrease in the number of sensory progenitor cells ( marked by either Sens or Cut ) ( Figure 1C–F ) . These data indicate that lateral inhibition and cell fate transformation are due to loss-of-Notch signaling . Hence , temp is necessary for proper Notch signaling during the development of ESO lineage . Through duplication mapping [30] , deficiency mapping [31] , and complementation tests with existing lethal P element insertion lines [32] , we mapped temp to CG3073 ( Figure 2A ) . This gene encodes a 398 amino acid ( a . a . ) protein containing a single small 29 a . a . PPTA motif which has only been found to be present in the α subunit of PPTs ( Figure 2B ) . temp is evolutionarily conserved in most but not all species queried ( Figure 2C ) , implicating that the function of the temp homolog might be assumed by another protein in species lacking this gene . The vertebrate homolog of temp is named PPTA containing protein 1 ( PTAR1 ) , but its biochemical or in vivo function has not yet been characterized . The lethality and loss-of-Notch signaling phenotypes of temp are rescued by both genomic and cDNA rescue constructs ( Figure 2A and 2D ) . Moreover , the human PTAR1 cDNA can also rescue the ESO developmental defects in temp mutant clones ( Figure 2D ) . These data indicate that the lethality and balding phenotypes in temp mutants are caused by mutations in CG3073 and that the molecular function of temp is evolutionarily conserved between fly and human . To investigate the endogenous expression pattern of Temp , we attempted to generate several antibodies against Temp , but these were unsuccessful . We therefore examined the expression pattern of Hemagglutinin ( HA ) -tagged genomic constructs ( HA–gtemp ) in a tempA homozygous mutant background . We find that Temp is expressed weakly during early ESO development ( Figure 2E ) and that it is somewhat enriched in the cytoplasm of ESOs at the four-cell stage ( Figure 2E′ ) . HA–gtemp is expressed weakly and dispersed throughout the cytoplasm . Similarly , when we overexpress HA–temp cDNA using dpp–Gal4 in the wing disc , HA–Temp is also diffuse in the cytoplasm ( Figure S3 ) . The elevated SOP density in temp mutant clones ( Figure 1C ) is similar to what has been observed in scabrous ( sca ) mutants ( Figure 3A ) [33] . Sca is secreted by the SOP to facilitate Notch signaling in nearby cells to promote lateral inhibition [33]–[36] . It is rapidly secreted and degraded upon synthesis [37] and therefore is very difficult to detect in wt tissue on the notum ( Figure 3B; GFP-positive cells ) . Interestingly , Sca is strongly up-regulated in temp mutant ESOs ( Figure 3B; GFP-negative cells ) . A similar elevation of Sca in sensory organs is also observed in developing wing and eye imaginal discs ( Figure S4A–B′ ) , indicating that this elevation is not limited to the notum . Given that the lateral inhibition defect in temp mutant clones is similar to that of loss-of-function of sca , we hypothesized that Sca is produced , but that it fails to be secreted , and hence accumulates in temp mutant ESOs . To test this idea , we first determined whether the up-regulation of Sca in temp mutant is transcriptional or posttranscriptional . The expression of the sca–lacZ reporter [36] , [38] , a readout for sca transcription , is similar between temp mutant and wt ESO ( Figures 3C and S4C–D ) , indicating that the level of Sca in temp mutant ESOs is posttranscriptionally up-regulated . To determine if Sca secretion is impaired , we developed a secretion assay by overexpressing a Sca–GFP fusion protein [39] in wt and temp mutant clones using the mosaic analysis with a repressible cell marker ( MARCM ) [40] . Sca–GFP can be secreted from wt clones into the neighboring area that does not produce Sca–GFP ( Figure 3D ) . However , Sca–GFP produced in temp mutant clones fails to be secreted into the neighboring area ( Figure 3E ) , indicating defective Sca secretion . To determine where Sca accumulates intracellularly , we performed coimmunostaining of Sca and an array of subcellular organelle markers ( Table S1 ) . The Sca-positive puncta mainly colocalize with GM130 [41] , a cis-Golgi marker , but not with Syntaxin 16 ( Syx16 ) [42] , a trans-Golgi marker ( Figure 3F–G′ ) . Therefore , in temp mutant cells , Sca accumulates in a GM130-positive compartment and cannot be secreted , which in turn contributes to the defect in Notch-signaling–mediated lateral inhibition . Since Sca is primarily involved in the lateral inhibition process , other proteins are likely to contribute to the cell fate specification defect in temp mutant clones ( Figure 1E and 1F ) . Cell fate determinants , including the adaptor protein Numb [26] , [43] and the E3 ligase Neuralized [44]–[46] , are asymmetrically segregated during the division of the pI cell to bias Notch signaling between the pIIa and pIIb . We did not observe obvious defects in the localization of either protein , suggesting that asymmetric segregation of cell fate determinants is not affected ( not shown ) . Although the expression and localization of Notch is not affected ( Figure S5A–C ) , the number of Dl-positive puncta is increased in temp mutant sensory organs ( Figure 4A and 4A′ ) . We previously proposed that recycled Dl travels to the apical actin-rich structure ( ARS ) localized between the pIIa and pIIb at the two-cell stage ( Figure 4B ) [10] . This process is necessary for proper Notch signaling activation and requires the Arp2/3 complex as well as the vesicle trafficking regulators Sec15 and dEHBP1 , two binding partners of Rab11 [8] , [9] , [47] , [48] . Mutations in these genes have been shown to exhibit cell fate defects and notum balding , similar to the loss of temp . While the ARS is properly formed ( not shown ) and apical-basal polarity is not affected in temp mutant clones ( Figure S5E ) , we found that dEHBP1 accumulates basally ( Figure 4C ) , similar to what is observed in sec15 mutants [8] . In dEHBP1 mutant ESOs , the level of Dl is reduced at the cell surface [8] , a feature that we also observe in about half ( yellow arrows ) of the ESOs in temp mutant clones ( Figure S5D ) . These data indicate that Dl accumulates intracellularly and that the mislocalization of EHBP1 may at least partially contribute to the Dl trafficking defect in temp mutant ESOs . Additionally , we found that many of the Dl and Sca puncta colocalize in temp mutant ESO , indicating that some of them are trapped in the same intracellular compartments ( Figure 4D and 4D′ ) . The majority of colocalized proteins are in the Golgi complex ( Figure 4E , blue arrows ) . However , some of the Dl- and Sca-positive puncta colocalize with a late endosomal and lysosomal marker LAMP1–GFP ( Figure 4F ) , suggesting that defects in the secretory pathway may cause some Sca and Dl to be sorted to the endo-lysosomal pathway for degradation . Therefore , mistrafficking of Dl is also likely to contribute to both lateral inhibition and cell fate defects in temp mutants . Protein prenylation regulates protein targeting and activity of numerous proteins [21] , [22] . Given that Temp contains a PPTA motif and that temp mutants affect protein trafficking , Temp may regulate the prenylation of proteins involved in vesicular trafficking . Indeed , RabGGTβ was identified as a potential interactor for Drosophila Temp in a high-throughput yeast-2 hybrid screen [49] . RabGGTβ forms a complex with RabGGTα [also called PPTA containing protein 3 ( PTAR3 ) ] and Rab escort protein ( REP ) to geranylgeranylate Rabs ( Figure 5A ) [16] , [50]–[54] , which are major coordinators of vesicle trafficking [13] , [14] . We therefore tested if Temp may act as an alternative α subunit of the RabGGT complex ( Figure 5B ) . Indeed , we found that Temp can interact with RabGGTβ and REP in coimmunoprecipitation ( coIP ) experiments in Drosophila S2 cells ( Figure 5C and 5D ) . In the presence of RabGGTβ , the expression level of Temp is increased , suggesting that Temp is stabilized by RabGGTβ . To assess whether Temp is an additional subunit of the original RabGGT complex or an alternative α subunit in a new RabGGT complex , we performed a RabGGTβ competition assay ( Figure 5E ) . We pulled down the same amount of RabGGTβ and found that its binding to Temp is reduced when the amount of RabGGTα increases , indicating that Temp and RabGGTα compete for RabGGTβ . Note that the total level of Temp is also decreased when it cannot bind to RabGGTβ . These data suggest that RabGGTβ can form two RabGGT complexes: a canonical complex with RabGGTα and a novel complex with Temp . Rabs are the only known substrates of the canonical RabGGT complex ( RabGGTα–RabGGTβ ) [16] , [21] , [55] . Hence , the targets of the Temp–RabGGTβ complex may also be Rabs . To identify the substrate Rabs responsible for the loss-of-Notch signaling phenotypes in temp mutants , we performed a genetic screen and overexpressed a subset of UAS–YFP dominant-negative Rab ( DN-Rab ) lines [56] in wing imaginal discs and pupal notum to screen for Sca accumulation and balding phenotypes similar to temp mutant clones , respectively ( Table S2 ) . Because Sca secretion defects in temp mutants are not restricted to the notum , we tested Rabs that are broadly expressed [57] and those involved in protein secretion [14] . Among the 15 Rabs tested for Sca accumulation , only overexpression of DN-Rab1 reproduces key features of temp mutant cells . It causes a strong Sca accumulation in ESO in third instar wing discs ( Table S2 ) , consistent with what we observed in rab1 homozygous mutant clones ( Figure S6A ) [58] . We also observe a co-accumulation of both Dl and Sca in DN-Rab1-expressing ESOs on the pupal notum ( Figure S6B ) . Similar to temp mutants , large Sca puncta colocalize with enlarged GM130 compartments [59] when we overexpress DN-Rab1 ( Figure 6A–A″ ) . Most importantly , Rab1 accumulates in enlarged GM130-positive compartments in temp mutant clones ( Figure 6B and 6B′ ) , suggesting that Temp regulates the subcellular distribution of Rab1 . Together , these data suggest that Rab1 may be a substrate for Temp . Therefore , loss of temp leads to aberrant localization of Rab1 and its effector GM130 , which in turn causes an accumulation of Sca and Dl . Because dysfunction of Rab1 only leads to minor adult balding ( Table S2 and Figure S6C ) , the cell fate transformations observed in temp mutant cells may be contributed by the dysfunction of other Rab ( s ) . Among the 10 broadly expressed Rabs [57] , we found that expression of DN-Rab5 and DN-Rab11 lead to strong balding ( Table S2 ) . Because we observed aberrant subcellular localization of Rab11 ( Figure 6C ) , but not Rab5 ( not shown ) , in temp mutant clones , we focused on Rab11 , which functions in Dl recycling during ESO development [9] , [47] . In temp mutant cells , Rab11 is enriched apically ( Figure 6C ) . This apical clustering is likely due to mislocalization of Rab11 because the protein level of Rab11 is not changed in temp mutant animals ( Figure S6D ) . . Cells that express DN-Rab11 exhibit an accumulation of Dl but not of Sca ( Figure S6E and S6F ) , indicating that Rab11 regulates trafficking of Dl but not of Sca . In addition , dEHBP1 , a Rab11 binding partner [8] , aberrantly accumulates basally in DN-Rab11-expressing notum ( Figure 6D ) , similar to temp mutants ( Figure 4C ) . Therefore , loss of temp causes an aberrant localization of Rab11 , which leads to a mislocalization of dEHBP1 and mistrafficking of Dl . These data indicate that Rab11 may also be a substrate of Temp . Because temp mutant clones exhibit an altered distribution of both Rab1 and Rab11 , we tested whether they are misdistributed to the same intracellular compartment . Coimmunostaining of Rab1 and Rab11 reveals that they mostly do not overlap in temp mutant clones ( Figure 6E ) , suggesting that additional factors regulate their aberrant subcellular distribution in the absence of temp . As shown previously , Temp can form a new RabGGT complex with RabGGTβ and interact with REP ( Figure 5B–D ) . If Rab1 and Rab11 are substrates of this complex , they should physically interact with Temp . Indeed , Temp binds to Rab1 and Rab11 in coIP experiments in S2 cells ( Figure 7A and 7B , last lanes ) . Temp and these Rabs can also be pulled down without cotransfecting RabGGTβ or REP , possibly because of the presence of these proteins in S2 cells . We knocked down the REP and RabGGTα proteins in S2 cells using RNAis that we designed . Unfortunately , these cells grow very slowly and most die , suggesting that REP and RabGGTα are required for the viability of S2 cells . These data are in agreement with our observation that RabGGTα mutant cells are lethal in vivo ( unpublished data ) . To determine whether Temp can prenylate Rab1 and Rab11 with geranylgeranyl groups , we performed an in vitro prenylation assay [60] . We cotransfected REP , RabGGTβ , and HA–Temp or HA–RabGGTα into S2 cells and pulled down the enzyme complex with anti-HA beads ( Figure 7C and 7D , left panels ) . We also purified GST–Rab1 and GST–Rab11 as unmodified substrates from bacteria . The prenylation assays were performed by adding GST–Rab1 or GST–Rab11 to biotin-labeled geranylgeranyl groups and the anti-HA beads loaded with the enzymatic complex ( HA–Temp–RabGGTβ–REP or HA–RabGGTα–RabGGTβ–REP ) . In the absence of Temp and RabGGTα , we observe weakly prenylated bands , likely due to nonspecific pull-down of endogenous RabGGT complexes ( Figure 7C and 7D ) . However , in the presence of Temp or RabGGTα ( positive control ) the prenylated band is obviously enhanced ( Figure 7C and 7D , right panels ) . These data indicate that the complexes containing Temp can prenylate Rab1 and Rab11 . Temp seems to be more efficient in prenylating Rab1 , whereas RabGGTα seems to be more efficient in prenylating Rab11 , indicating that they may have different substrate preferences . In conclusion , our data show that Temp can form a novel PPT complex to prenylate Rab1 and Rab11 .
We isolated a novel Notch signaling player , temp , whose loss causes defects in lateral inhibition and cell fate determination of ESO from an unbiased genetic screen . temp encodes an unstudied protein with a 29 a . a . PPTA motif implicated in protein prenylation . Here , we show that Temp forms a complex with RabGGTβ and REP to prenylate Rab1 and Rab11 . Loss of temp leads to an aberrant subcellular distribution of Rab1 and Rab11 . Loss-of-function of Rab1 causes an accumulation of Sca and Dl , whereas loss-of-function of Rab11 further contributes to the Dl trafficking defect . Together , our data indicate that Temp functions as the α subunit of a new RabGGT complex that modulates Notch signaling via Rab1 and Rab11 . Although the role of Rab11 in Notch signaling pathway has been previously documented [47] , our data indicate that Rab1 is required for proper trafficking of Sca and Dl . To our knowledge , this is the first time that Rab1 has been linked to Notch signaling . Sca trafficking is mainly affected by Rab1 , whereas Dl trafficking is affected by both Rab1 and Rab11 . Because null alleles of both Rabs are cell lethal ( not shown ) , but temp mutant clones are not , the loss of Rab1 and Rab11 function can only be partial in the temp mutant clones . This suggests that the other α subunit , RabGGTα , still prenylates a portion of Rab1 and Rab11 that play a role in other cellular processes in the absence of temp , consistent with the prenylation assay results ( Figure 7C and 7D ) . In addition , overexpression of constitutively active ( CA ) forms of Rab1 and Rab11 ( Rab1CA and Rab11CA ) in temp mutant clones does not alter Sca accumulation ( Figure S7A and S7B ) nor does it alter cell fate transformation ( Figure S7C and S7D ) . This suggests that loss of temp is epistatic to the constitutively active forms of Rabs . These genetic interaction data are consistent with our model as constitutively active forms of Rab must be properly prenylated [18] . In temp mutant clones and notum cells expressing DN-Rab1 , Sca mostly accumulates in GM130-positive compartments , traditionally considered as cis-Golgi . However , because Rab1 functions in ER-to-Golgi trafficking [13] , [14] , loss of Rab1 is expected to prevent cargo from entering the Golgi apparatus . This may be because the cis-Golgi is closely associated with the ER exit sites ( tER ) to form the “tER–Golgi unit” [61] , which is optically difficult to distinguish from the cis-Golgi compartment . Indeed , a number of Golgi proteins including GM130 , GRASP , and p115 localize at tER sites in S2 cells [61]–[63] . For example , GRASP also colocalizes with Sca in temp mutants ( Figure S4E ) . Therefore , loss of temp or its target , Rab1 , causes Sca to accumulate in GM130-positive compartments corresponding to the cis-Golgi , the tER , or an intermediate compartment between the two . In yeast and cultured mammalian cells , Rabs that lack proper geranylgeranylation diffuse in the cytoplasm [17] , [18] . Surprisingly , we find that the distribution of Rab1 and Rab11 in temp mutants is restricted to specific subcellular compartments: Rab1 colocalizes with enlarged GM130 compartments ( Figure 6B ) whereas the majority of Rab11 is apically enriched ( Figure 6C ) . Given that Rab1 and Rab11 do not diffuse in the cytoplasm and that they do not redistribute to the same microdomains ( Figure 6E ) , we propose that the subcellular localization of Rab1 and Rab11 is in part determined by different/other Rab binding partners upon reduction of proper prenylation in temp mutants . The fly and human genomes both contain three genes that encode proteins containing the PPTA motif: PTAR1/Temp , PTAR2/FTα , and PTAR3/RabGGTα . PTAR2 forms PPTs with two different β subunits , whereas PTAR3 forms a RabGGT with RabGGTβ ( Figure S1 ) [21] , [22] , [52]–[54] , [64] . We show that PTAR1 and RabGGTβ form an alternative RabGGT complex to prenylate Rab1 and Rab11 . This raises an interesting question: what is the labor distribution of RabGGTα–RabGGTβ and Temp–RabGGTβ complexes ? Because the PTAR1 ( temp ) homolog is absent in some species like S . cerevisiae and C . elegans , but is present in Dictyostelium and Arabidopsis ( Figure 2C ) , it is likely that it was lost in some evolutionary branches and that its function is covered by PTAR3 in these species . While we can obtain large homozygous mutant clones with temp null alleles , we find that homozygous RabGGTα mutant clones in the thorax and eyes are cell lethal ( not shown ) . Hence , we speculate that Temp has evolved to play a more specific role to prenylate a subset of Rabs , whereas RabGGTα is able to modify most , if not all , Rabs [16] , [21] , [55] yet is not sufficient for proper trafficking of Scabrous and Delta . Indeed , although both Temp and RabGGTα can prenylate Rab1 and Rab11 in vitro with different efficiency ( Figure 7C and 7D ) , expression of fly RabGGTα fails to rescue the ESO phenotypes in temp mutant clones ( Figure 2D ) . Moreover , expression of fly temp does not alleviate the cell lethality in RabGGTα mutant clones ( data not shown ) . These data indicate that the functions of the two RabGGT complexes are nonredundant in vivo and that the functions of the two RabGGT complexes towards different Rabs may also be regulated in a tissue-specific manner through unknown interaction partners and/or posttranslational modifications in vivo . This tissue-specific regulation is supported by the gene expression data from FlyAtlas ( Figure S8 ) [65] , [66] . RabGGTα mRNA is transcribed ubiquitously at moderate levels and the expression pattern of temp mRNA is much more restricted to the nervous system with high levels in thoracic-abdominal ganglion cells . This suggests that Temp plays a role in the nervous system , including ESO development . As major coordinators of vesicular trafficking , Rabs are crucial for maintaining normal cellular function and misregulation of some Rabs results in cellular dysfunction . Indeed , dysfunction of some Rabs and their prenylation factors are implicated in several diseases [13] , [55] , [67] . For example , Choroideremia , an inherited retinal degenerative disease , is caused by mutations in REP1 . On the other hand , Rab1 is hijacked by the pathogen Legionella pneumophila during infection to support the bacterium with the ER-to-Golgi secretory system [68] . In various cancers , the expression level of numerous Rabs , including Rab1 and Rab11 , is up-regulated . Up-regulation of Rab11 family members ( Rab11A/11B/25 ) is associated with more aggressive prostate , ovarian , and breast cancers [13] , [55] . Finally , toxins produced by Bacillus anthracis inhibit Rab11 and Sec15 , which in turn reduce Notch signaling activity in both flies and mammalian endothelial cells [69] , suggesting a possible role for Temp in aberrant Notch signaling induced by bacterial infection .
The following stocks were used in this study: ( 1 ) isogenized y w FRT19A ( y w FRT19Aiso or iso19A ) , ( 2 ) Df ( 1 ) JA27/FM7c Kr-GFP , ( 3 ) w sn FRT19A; Ubx-FLP ( 2 ) , ( 4 ) cl ( 1 ) Ubi-GFP FRT19A/FM6; Ubx-FLP ( 2 ) , ( 5 ) Ubx-FLP tub-GAL80 FRT19A; Actin-GAL4 UAS-CD8::GFP ( MARCM line ) , ( 6 ) hs-FLP Ubi-GFP FRT19A ( D . Bilder ) , ( 7 ) y w; P{lacW}scaA2–6 ( sca-lacZ reporter ) [36] , [38] , ( 8 ) y w; UAS-Sca-GFP/Cyo ( Sca-GFP used in secretion assay ) [39] , ( 9 ) w; UAS-CFP-Golgi ( 2 ) [70] , ( 10 ) w; UAS-Lamp1-GFP; M3–12/S-T ( C-K Yao ) [71] , ( 11 ) y w; T ( 2;3 ) ap[Xa]/SM5; TM3 , Sb , ER-YFP [19] , ( 12 ) C96-Gal4/ ( TM3 , Sb ) , ( 13 ) Dp ( 1;2;Y ) w+ , ( 14 ) w Df ( 1 ) ED6574/FM7h , ( 15 ) w Df ( 1 ) ED409/FM7h , ( 16 ) P{lacW}l ( 1 ) G0144 w/FM7c [72] , ( 17 ) FRT82B dar6[12-3-73]/TM3 , Sb [58] ( a severe loss-of-function allele for Rab1 ) , ( 18 ) y w Ubx-FLP; FRT82B M [19] Ubi-GFP , ( 19 ) y w; P{w[+mW . hs] = FRT ( w[hs] ) }2A P{ry[+t7 . 2] = neoFRT} 82B PBac{SAstopDsRed}LL03248 P{y[+t7 . 7] ry[+t7 . 2] = Car20y}96E/TM6B , Tb [73] , and ( 20 ) UAS-p35 ( a kind gift from Andreas Bergmann ) . The forward genetic screen that identified alleles of temp was performed as previously described [23] . A total of 577 stocks have wing notching or notum balding phenotypes and were mapped through X chromosome duplication mapping , deficiency mapping , and complementation tests . Genomic and cDNA rescue transgenic flies were generated by phiC31-mediated transgenesis at vk33 or vk37 docking sites [74] . Phenotypic rescue was assessed in temp mutant background either in whole animal or by overexpression in temp MARCM clones . UAS–YFP–DN-Rab flies [56] were crossed to C96–Gal4 [75] , which can drive ectopic expression of UAS transgenes around the wing margin , and used for immunostaining against Sca in the third instar larval wing disc . We overexpressed UAS–GFPnls as a negative control . We used the MARCM strategy in the balding screen and to assess Sca secretion and mark subcellular compartments . Because these transgenes encode GFP/YFP/CFP fusion proteins , we crossed out Actin–Gal4 , UAS CD8::GFP in MARCM line , which was then combined with the transgene of interest and subsequently crossed to either y w FRT19Aiso; Actin–Gal4/Cyo or y w temp FRT19A; Actin–Gal4/Cyo ( tempA and tempD ) for performing experiments in control and mutant genetic backgrounds , respectively . Stocks were maintained at RT and crosses were performed at 25°C . A genomic rescue construct was constructed by PCR amplification of a 4 . 5 kb amplicon spanning CG3073 locus and cloned into pattB [76] . We added N-terminal HA or mCherry tags to the genomic rescue construct via conventional cloning methods . cDNA of RabGGTβ was constructed in the pMT vector , while human PTAR1 ( hPTAR1 ) , temp , RabGGTα , REP , rab1 , and rab11 were cloned into pUASTattB [76] with N-terminal HA and FLAG tags . In addition , rab1 and rab11 were cloned to pGEX vectors ( GE Healthcare ) . Cloning and DNA purification were performed based on standard protocols . Enzymes are from NEB , and DNA purification kits are from Invitrogen and Qiagen . All constructs were sequenced before injection or transfection . For notum immunostaining , fly pupae were aged until the indicated time points at 25°C . For the wing disc staining , we dissected third instar larvae . Dissection and immunostaining were performed as previously described [8] . Primary antibodies were used at the following dilutions: mouse α-Rab11 1∶100 ( BD Biosciences ) , rabbit α-Rab11 1∶1 , 000 [69] , mouse α-Rab1 1∶500 [77] , guinea pig α-Boca 1∶1 , 000 [78] , guinea pig α-Hrs 1∶600 [79] , mouse α-Dl 1∶200 ( DSHB ) [80] , guinea pig α-Dl 1∶1 , 000 [81] , mouse α-NICD 1∶200 ( DSHB ) [82] , mouse α-NECD 1∶100 ( DSHB ) , chicken α-GFP 1∶1 , 000 ( Abcam ) , rabbit α-GFP 1∶500 ( Invitrogen ) , rabbit α-GM130 1∶500 ( Abcam ) , rabbit α-Syx16 1∶500 ( Abcam ) , rabbit α-Neur 1∶600 [83] , rabbit α-beta-Galactosidase 1∶500 ( Cappel ) , rabbit α-Numb 1∶1 , 000 [43] , rabbit α-GRASP55 1∶500 [84] , guinea pig α-Sens 1∶1 , 000 [25] , rat α-ELAV 1∶500 ( DSHB ) [85] , mouse α-Cut 1∶500 ( DSHB ) [86] , rat α-Su ( H ) 1∶500 [29] , rat α-DE-cadherin 1∶50 ( DSHB ) [87] , and rat α-Ttk69 1∶500 [88] . Alexa 488–conjugated ( Invitrogen ) and Cy2- , Cy3- , Cy5- , or DyLight649-conjugated secondary antibodies ( Jackson ImmunoResearch ) were used at 1∶200 . Samples were mounted in Slowfade reagent ( Invitrogen ) . All confocal figures were acquired with confocal microscope ( LSM510; Carl Zeiss ) using Plan Apochromat 40× NA 1 . 4 and Plan Apochromat 63× NA 1 . 4 objectives ( Carl Zeiss ) , followed by processing in LSM software , ImageJ , and Photoshop ( Adobe ) . For Sca secretion assays , we assess extracellular Sca–GFP by staining for rabbit α-GFP without any detergents . Then , we permeabilize the notum tissue with 0 . 1% Triton-PBS and perform a staining for the total GFP using chicken α-GFP antibody . To costain both extracellular and total Dl in temp mosaic notums , we first stain extracellular Dl using mouse α-Dl antibody without detergents . Then we permeabilize the noum with 0 . 1% Triton-PBS and stain for the total Dl with the guinea pig α-Dl antibody . For Notch extracellular staining , the notum tissue is first stained with mouse α-NECD antibody without any detergents . Then , we permeabilize the notum tissue with 0 . 1% Triton-PBS and perform the later steps of immunostaining similarly . S2 cell line was cultured in Schneider's media ( Gibco ) with 10% fetal bovine serum ( FBS ) ( Sigma-Aldrich ) and antibiotics mix ( penicillin and streptomycin , Invitrogen ) at RT . The transfection was performed using Effectene ( Qiagen ) according to the manufacturer's instructions . Proteins were expressed using Actin–GAL4/UAS system or pMT inducible system ( Invitrogen ) . In co-IP experiments , cells were harvested 48 h after transfection and lysed on ice in lysis buffer [50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 5% glycerol , 0 . 5% TritonX-100 , and EDTA-free cocktail complete protease inhibitor ( Roche ) ] . Lysate supernatant was incubated with EZ red α-HA beads ( Sigma-Aldrich ) overnight , and beads were washed with lysis buffer and boiled in 2× Laemmli buffer . PAGE , transfer , and Western blot were performed according to standard protocols . In the RabGGTβ competition assay , we performed coIP using α-V5 beads ( Sigma-Aldrich ) instead . Primary antibodies were used at the following dilution: rabbit α-HA 1∶2 , 000 ( Abcam ) , rabbit α-FLAG antibody 1∶2 , 000 ( Abcam ) , mouse α-FLAG ( M2 , Sigma-Aldrich ) 1∶1 , 000 , mouse α-V5 1∶5 , 000 ( Invitrogen ) , mouse α-Actin 1∶1 , 000 ( MP Biomedicals ) , and mouse α-Rab11 1∶500 ( BD Biosciences ) . Goat HRP-conjugated secondary antibodies were used at 1∶2 , 000 dilution ( Jackson ImmunoResearch ) . Membranes were developed using Western Lightning Plus-ECL ( PerkinElmer ) followed by X-ray film ( Thermo Scientific ) detection . GST , GST–Rab1 , and GST–Rab11 were purified from BL21 pLys ( Invitrogen ) : 25 ml overnight cultures were diluted to 250 ml with LB medium and kept growing at 37°C until OD600 reached 0 . 5∼0 . 7 . GST , GST–Rab1 , and GST–Rab11 proteins were induced at 37°C for 3 h by adding IPTG at a final concentration of 0 . 1 mM . Bacteria were then lysed using CelLytic express ( Sigma-Aldrich ) according to the manufacturer's instructions . Supernatant was incubated with Glutathione Sepharose 4B ( GE healthcare ) at 4°C for 2 h and then the beads were washed with 50 mM HEPES pH 8 . 0 buffer by inversion for three times . GST fusion proteins were eluted with 10 mM glutathione in 50 mM HEPES pH 8 . 0 buffer . Protein concentration was measured using Bradford method ( BioRad ) . We used 0 . 3 µM GST–Rab1 and 3 µM GST–Rab11 as substrates in the prenylation assays with 0 . 3 µM and 3 µM GST as negative controls , respectively . On the other hand , all components of the RabGGT complex ( His–RabGGTβ–V5 , FLAG–REP , and HA , HA–Temp , or HA–RabGGTα ) were transfected to S2 cells as described in the previous section . Cells were lysed on ice in prenylation buffer [50 mM HEPES , pH 7 . 2 , 50 mM MaCl , 2 mM MgCl2 , 0 . 01% Triton X-100 , and EDTA-free cocktail complete protease inhibitor ( Roche ) ] [60] using syringes . Supernatant was then incubated with EZ red α-HA beads ( Sigma-Aldrich ) for 4 h at 4°C , and beads were washed with prenylation buffer . We added HA , HA–Temp , or HA–RabGGTα binding beads together with GST control or GST–Rab substrates , 5 µM biotin-labeled lipid precursor ( B-GPP; Jena Bioscience ) , 2 mM DTE , and 20 mM GDP . The prenylation assays were carried out on beads at 25°C for 1 h . Reactions were stopped by adding 2× Laemmli buffer . Western blot was performed as described in the previous section with 5% BSA as blocking solution and Streptavidin–HRP 1∶50 , 000 ( Jackson ImmunoResearch ) for Biotin detection . | Notch signaling is an evolutionarily conserved signaling pathway that regulates many developmental processes . Abnormal Notch signaling activity can lead to numerous diseases and developmental defects . To better understand the regulation of this pathway , we performed a forward genetic screen for Notch signaling components that have not been previously identified in Drosophila . Here , we report the identification of an evolutionarily conserved protein , Tempura , which is required for Notch signaling-mediated lateral inhibition and cell fate determination of external sensory organs . We show that loss of tempura leads to mistrafficking of Delta and Scabrous , two important Notch signaling components . In addition , Rab1 and Rab11 , two major coordinators of vesicular trafficking , are mislocalizaed in tempura mutants . We further show that Tempura functions as a subunit of a previously uncharacterized lipid modification complex to geranylgeranylate ( a type of prenylation ) Rab1 and Rab11 . This post-translational modification is shown to be required for the proper subcellular localization and function of these Rabs . We find that dysfunction of Rab1 causes an accumulation of Delta and Scabrous in the secretory pathway and dysfunction of Rab11 further interferes with the trafficking of Delta . In addition to the known Rab geranylgeranyltransferse , our data indicate the presence of another functionally nonredundant Rab geranylgeranyltransferse , Tempura . | [
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] | 2014 | Drosophila Tempura, a Novel Protein Prenyltransferase α Subunit, Regulates Notch Signaling Via Rab1 and Rab11 |
Cystic echinococcosis ( CE ) is endemic in Spain but has been considered non-endemic in the province of Álava , Northern Spain , since 1997 . However , Álava is surrounded by autonomous regions with some of the highest CE prevalence proportions in the nation , casting doubts about the current classification . The purpose of this study is to estimate the frequency of CE in humans and animals and to use this data to determine the societal cost incurred due to CE in the Álava population in 2005 . We have identified epidemiological and clinical data from surveillance and hospital records , prevalence data in intermediate ( sheep and cattle ) host species from abattoir records , and economical data from national and regional official institutions . Direct costs ( diagnosis , treatment , medical care in humans and condemnation of offal in livestock species ) and indirect costs ( productivity losses in humans and reduction in growth , fecundity and milk production in livestock ) were modelled using the Latin hypercube method under five different scenarios reflecting different assumptions regarding the prevalence of asymptomatic cases and associated productivity losses in humans . A total of 13 human CE cases were reported in 2005 . The median total cost ( 95% credible interval ) of CE in humans and animals in Álava in 2005 was estimated to range between €61 , 864 ( 95%CI%: €47 , 304–€76 , 590 ) and €360 , 466 ( 95%CI: €76 , 424–€752 , 469 ) , with human-associated losses ranging from 57% to 93% of the total losses , depending on the scenario used . Our data provide evidence that CE is still very well present in Álava and incurs important cost to the province every year . We expect this information to prove valuable for public health agencies and policy-makers , as it seems advisable to reinstate appropriate surveillance and monitoring systems and to implement effective control measures that avoid the spread and recrudescence of the disease .
Cystic echinococcosis ( CE or hydatid disease ) caused by the larval stage of the taeniid tapeworm Echinococcus granulosus is an important zoonotic disease with a worldwide distribution . Domestic transmission of the infection relies on dogs as definitive hosts and a range of livestock ungulate intermediate hosts , mainly sheep and cattle . The disease represents a serious human and animal health concern , causing important economic losses derived from the costs of medical treatment , morbidity , life impairment and fatalities in human cases and decreased productivity and viscera condemnation in livestock species [1]–[4] . Estimation of the CE monetary burden in humans and livestock quantifies the societal impact of the disease , which can aid policymakers in allocating financial and personnel resources . As such , these investigations should be an integral part of any decision regarding priorities for zoonoses control programs [4] . CE is considered endemic in Spain [5] , but this classification varies from region to region . Attempts to effectively control the disease began in 1986 with a pilot program in the Autonomous Region ( AR ) of Navarre in collaboration with the Spanish Ministry of Health and the Mediterranean Zoonoses Control Centre . This initiative was soon after extended to the ARs of Aragon , Castile-La Mancha , La Rioja and Madrid [6] , with some other ARs ( Castile-León and Extremadura ) developing independent control programs . Because of this concerted effort , there has been a marked reduction in the overall incidence rate of human CE and in the prevalence of animal CE over the past 20 years [7] , [8] . However , despite the program's success , CE remains a serious health and economic problem in the North-eastern , Central and Western parts of the country [9] . The overall CE-associated economic losses in Spain were estimated to an average of €149 million ( 95% credible interval , CI: €22 to €394 million ) for the year 2005 , with human-associated costs constituting 89 . 1% of total losses [1] . Reporting of human CE is currently mandatory in nine of the 17 Spanish ARs . The Basque Country , to which Álava belongs , is part of the remaining eight ARs that were classified as non-endemic for CE in 1997 based on the initial success achieved after the implementation of control campaigns . This decision resulted in revoking an obligation to report new human CE cases , and henceforth the unavailability of human CE surveillance data from Álava since 1997 . The province of Álava is bordered by the ARs of Navarre , La Rioja , and Castile-Leon , which are considered among the most prevalent regions for CE in Spain [9] . In addition , the prevalence of infection has been estimated to 0 . 5% among kennel dogs and 8 . 0% among sheep dogs sampled in Álava in 1996–1999 and 1997–1998 , respectively [10] , [11] . Moreover , in a recent survey conducted in abattoirs of the Basque Country , the percentage of bovines presenting hydatid cysts at slaughter varied between 1 . 5% in 2006 and 4 . 1% in 2008 , with a clear increasing trend [12] . These data combined with the considerably large livestock population of 88 , 000 sheep , 4 , 800 goat , 40 , 000 cattle , and 15 , 000 pigs make it unlikely for Álava to be truly non-endemic for CE . The aim of this study was to estimate human and animal CE-associated economic losses in 2005 in the province of Álava . The year 2005 was chosen because it had the most complete and accurate set of epidemiological , clinical and economical parameters required for our analyses . It also allowed direct comparison with previous published data at the national scale [1] . To achieve this aim , the annual incidence rate of human CE between 1991 and 2007 and the prevalence of ovine and bovine CE infection at slaughter between 2000 and 2006 were also estimated .
Hospital medical records ( HMR ) used in this survey to gather clinical and socio-demographic data were anonymized prior to any review or analysis in order to preserve the identity of the affected patients . This study was been approved by the Research Ethics Committee of the Health Institute Carlos III . The official number of reported CE cases in the province of Álava between 1982 and 1996 and in Spain between 1982 and 2007 were obtained from the national epidemiological surveillance network [13] . Since reporting of CE cases in Álava was discontinued in 1997 , we reviewed the HMRs of all patients who were diagnosed with CE in the two main hospitals of the province ( Txagorritxu Hospital and Santiago Apóstol Hospital ) from 1991 to 2007 . In Álava , all patients suspected of having hydatid cysts ( e . g . positive serology ) are referred to one of these hospitals for confirmation and treatment . The population sizes for 1982 to 2007 in Álava were taken from the Basque Statistics Institute [14] . The annual incidence rate ( per 100 , 000 person-years ) was estimated by dividing the total number of cases residing in Álava at the time of diagnosis by the estimated population size for that year , and then multiplying by 100 , 000 . Socio-demographic ( age group , gender , place of birth and residence at the moment of diagnosis , urban/rural environment ) and clinical ( methods of diagnosis , cyst type and location , complications presented , treatment adopted , follow-up , re-operations , re-infection , mortality rate ) parameters were retrieved from the HMR of each individual CE case attended in the two hospitals between 1991 and 2007 . The numbers of sheep and cattle found to have CE lesions at slaughter in the Basque country during the period 2000–2006 were provided by the Department of Health of the Basque Country . Equivalent figures at the national level between 1998 and 2006 were obtained from the Spanish reports on Trends and Sources of Zoonoses and Zoonotic Agents in Humans , Animals and Foodstuff submitted to the European Food Safety Authority [15] . Regional and national data do include home slaughtered animals infected with CE since by law , every killed animal destined to human consumption must be examined by a veterinarian . To estimate the annual overall cost of human CE we included both health provider ( direct ) costs and non-health provider ( indirect ) costs . The health provider costs were obtained from the Txagorritxu Hospital financial department for the year 2008 and adjusted the values for 2005 , accounting for inflation . Since the health system is public in Spain , the costs in all hospitals in Álava are similar . The cost of each diagnostic test , surgical intervention , and medical treatment reported to have been used for the CE cases treated in 2005 was obtained . Health provider cost estimates included separate calculations for surgical and non-surgical patients . The non-health provider costs only included productivity losses during hospitalisation for inpatients and productivity losses due to the disease itself for all CE cases . No productivity losses were attributed to medical visits since no information was available on the duration of each visit , and that the work or activities lost during the visit could be completed when the patients returned to their work or home ( for retired people ) . Average wages according to sex and age for the Basque Country , including average pensions for retired people , were obtained from the 2005 Wage Distribution Survey of the National Statistics Institute [20] . We assumed equivalent wages for the province of Álava . The itemised cost menu is presented in Table 3 . Production animal species considered in the analyses included sheep and cattle . Goats and pigs were not considered because none were identified with CE-infection during the study period . Due to the lack of active abattoirs in Álava in 2005 , most of the animals from this province were slaughtered somewhere else in the AR of the Basque Country . Therefore , CE infection prevalence proportions in sheep and cattle in Álava were assumed to be the same as those globally reported for the AR of the Basque Country . Final prevalence estimates excluded data from non-autochthonous CE cases ( 91 . 1% of infected sheep and 79 . 2% of cattle were raised and slaughtered in the AR of the Basque Country ) [9] . The number of sheep and cattle infected in Álava was estimated by multiplying the prevalence of CE among autochthonous animals slaughtered in the AR of the Basque country by the total population of each species in Álava . Estimates for autochthonous livestock life expectancy and reproductive rates were provided by Carlos Marín Ruiz ( Department of Livestock , Regional Government of Álava ) . Official figures for annual livestock meat and milk production were obtained from the Spanish Ministry of Agriculture , Food and Environment [21] . Data stratified by age ( young lambs: <1 . 5 months; lambs: 1 . 5 months to 1 year; adult sheep: >1 year old; calves: <1 year old; young cattle: >1 and <2 year old; adult cattle: >2 year old ) ) were included when available . Productivity losses associated with CE – including reduction in growth , reduction in milk production and decrease in fecundity – were estimated from the published literature available [7] , [22]–[25] . All productivity losses estimates were attributed a uniform distribution to reflect their uncertainty ( Table 4 ) . The costs of fecundity and growth losses were only estimated for meat animals up to 1 year for sheep ( lamb ) and 2 years for cattle ( beef cattle ) . The animal epidemiological parameters used in this study are reported in Table 4 . Direct costs ( loss of revenue through offal condemnation ) and indirect costs ( reductions in growth , fecundity , and milk production ) were the parameters we used to estimate the overall losses associated with bovine and ovine CE in Álava province in 2005 . The itemised cost menu for animal losses is presented in Table 5 . We generated five different scenarios to assess the impact of undiagnosed or asymptomatic CE cases in the province of Álava and the annual productivity losses due to CE infection in humans given the variability of both parameters in the available literature [17] , [18] ( Table 6 ) . Scenario 1 includes wages lost during hospitalization for inpatients but excludes productivity losses and asymptomatic cases . Scenario 2 includes annual productivity losses ( uniform distribution between 0% and 4% per year ) and applied to both diagnosed cases and estimated number of asymptomatic cases based on a uniform distribution between the Turkey and Uruguay estimates [18] . Scenario 3 uses the same method except that a triangular distribution is applied to estimate the number of asymptomatic cases centred around the Uruguay estimate [17] , with minimum prevalence set to zero and using the upper limit of the Turkey estimate as a maximum value [18] . Scenarios 4 and 5 use the same models to estimate the number of asymptomatic cases , but using a Beta distribution ascribed to the reduction in annual productivity . For each scenario , 10 , 000 iterations were generated using Latin hypercube random sampling of the input parameter values and based on their assigned distributions . The 50th percentile of this distribution represents the median , and the 2 . 5th and 97 . 5th percentiles represent the 95% credible intervals ( CIs ) for the total cost of CE per year . A stepwise linear regression of the estimated costs against the input parameter values ( i . e . the parameters with a distribution ) was performed to assess the impact of each uncertain parameter on the overall cost estimate . The estimates from models with and without asymptomatic cases and the resulting figures illustrating the impact of input parameters were generated using @Risk Version 5 . 5 . 0 software ( Palisades Corporation , Ithaca , New York , USA ) , running as an add-in to Microsoft Excel .
Figure 1 shows available historical annual incidence rate of human CE in the province of Álava obtained from different sources , including revised HMR ( 1991–2007 ) and the official incidence of the disease ( 1991–1996 ) provided by the Compulsory Notifiable Diseases system ( CND ) of the national epidemiological surveillance network [13] . National official incidence rates ( 1982–2007 ) have also been included for reference [13] . Not surprisingly , HMR evidenced remarkably higher human CE rates than those reported by the CND , with an average 6-fold increase for the common period between 1991 and 1996 . Similarly , a 4-fold increase was observed when the HMR figures were compared to the national official incidence rated for the period between 1997 and 2007 . One hundred and fifty-four ( 154 ) patients were diagnosed with human CE during 1991–2007 in the two main hospitals of the province of Álava ( Table 7 ) . The male/female ratio was 1 . 05 and the age of cases ranged from 13 to 91 years ( mean: 61 . 5; SD: 17 . 8 ) . Only three subjects ( 1 . 9% ) were of paediatric age ( defined as ages from birth to 15 years of age ) , while patients over 60 years old accounted for 61 . 7% of cases . Most ( 57 . 8% ) diagnosed cases were native to Álava . Only 4 individuals ( 2 . 6% ) were born outside of Spain . The vast majority ( 144 cases , or 93 . 5% ) of cases resided in the province at the moment of diagnosis , with 81 . 2% living in urban settlements in or around the capital Vitoria-Gasteiz . CE patients residing in rural areas were exclusively distributed alongside the eastern , southern , and western boundaries of the province bordering the ARs of Navarre , La Rioja , and Castile-León , respectively ( Figure 2 ) . Economic losses for CE in humans in 2005 were based on the 13 cases diagnosed at Txagorritxu Hospital ( 7 cases ) and the Santiago Apóstol Hospital ( 6 cases ) . In order to demonstrate the representativeness of the data obtained from the HMR of these 13 patients , we carried out a direct comparison of the average figures of different clinical and epidemiological parameters in 2005 and those corresponding for the whole period 1991–2007 . Compared variables included the type of method used to reach a diagnosis , the specific treatment ( if any ) followed , and also relevant socio-demographic parameters . As clearly shown in Table 1 , Table 2 , and Table 7 , average figures obtained for the years 2005 are in close agreement with those for 1991–2007 , demonstrating that the set of data we have used in our analyses did not have unexpected or out of range values and providing evidence of the robustness and accuracy of our results . CE prevalence in livestock species ( sheep and cattle ) for the period 1998–2006 in Spain and the Autonomous Region of the Basque Country were obtained from official sources . Both epidemiological series showed a sustained , declining trend with average ovine and bovine prevalence 2- and 4-fold higher in the Basque Country than in Spain , respectively ( data not shown ) . In the AR of the Basque Country ovine and bovine infections were 2 . 4% and 6 . 1% in 2000 and 0 . 1% and 1 . 7% in 2006 , respectively . The five scenarios run to estimate the total cost of CE in Alava for 2005 showed considerable variation , depending on assumptions made on the prevalence of asymptomatic cases and their associated productivity losses ( Table 8 and Figure 3 ) . When asymptomatic cases and productivity losses were excluded , the median cost was estimated at €61 , 864 ( 95%CI: €47 , 304–€76 , 590 ) . All other scenarios had a lower credible interval superior to €61 , 000 , but the lower whiskers ( 25th percentile minus 1 . 5 times the interquartile range ) were similar among the models , with a value similar to the median under scenario 1 ( see Figure 3 ) . Scenarios 2 and 3 , where productivity losses were assumed to follow a uniform distribution between 0% and 4% , resulted in the largest and most uncertain estimates . Total livestock losses were estimated at €26 , 425 ( 95%CI 11 , 911–40 , 522 ) , with indirect costs representing 93% of the total costs . Compared to human monetary losses , livestock costs represented 43% of the total costs in scenario 1 whereas they represented only 7% of the total costs in scenario 2 , where the total median cost was estimated at €360 , 466 ( 95%CI: €76 . 424–€752 , 462 ) . Figure 4 shows that most of the variation in the total costs in scenario 1 could be attributed to animal productivity estimates . In scenarios 2–5 , the total costs depended largely on the percentage of productivity losses ( normalized regression coefficients ranging from 0 . 82 in scenario 3 to 0 . 98 in scenario 4 ) , followed by the estimated prevalence of asymptomatic cases in the population ( normalized regression coefficients ranging from 0 . 43 in scenario 5 to 0 . 53 in scenario 3 ) . The next parameter to contribute to the overall variation was the reduction in milk production with normalized regression coefficients of 0 . 03 in scenario 2 and 0 . 06 in scenario 5 .
This comprehensive assessment of human and animal CE in Álava demonstrates that CE remains a veterinary public health concern in this Spanish province . Active search of patients diagnosed with CE in HMR demonstrated that the incidence of the disease during the study period is 4- to 6-fold higher than the figures reported in official sources , both at regional and national levels . These results are in-line with recent reports that the CND system underestimates the true incidence of CE disease in Spain [9] , [29]–[31] . It should be noted that of the three paediatric patients identified one was born in Mauritania ( a 13 years-old male ) , whereas the other two subjects ( two females aged 13 and 14 years , respectively ) were born and grew up in the province of Álava . Although acquisition of the infection in other regions/countries cannot be entirely ruled out ( e . g . no records of travelling abroad were available ) , these two patients were almost certainly autochthonous cases . This finding , together with the previous detection of adult E . granulosus worms in both urban [10] and rural [11] dog populations , strongly suggests that an active transmission cycle of the parasite is being maintained in Álava . It has been recently reported that between 21–60% of CE cases from Spanish HMR correspond to immigrants from endemic countries [31] , [32] . This is not the case in Álava , where only 4 patients ( 2 . 6% ) were born abroad ( one in France , one in Portugal and two in Mauritania ) . Comparing the proportion of cases among immigrants is difficult , since immigrants in the AR of the Basque Country represent a low percentage of the total population compared to other Spanish ARs . Regarding livestock CE , the prevalence of the infection in production animal species in the province of Álava was assumed identical to that of the AR of the Basque Country . This assumption is reasonable because i ) no operational abattoirs were running in the province during 2005 ii ) animals raised in Álava were almost exclusively slaughtered in abattoirs of the AR of the Basque Country , and iii ) due to the small size of this AR , the environmental and biological characteristics and the livestock management techniques are homogeneous across the region . The trend of our CE prevalence data series is in agreement with those reported at national level showing slow but sustained reduction in animal infection and ultimately reaching a plateau [9] . This may suggest that CE infection has diminished during the transition from ‘attack phase’ to the ‘consolidation phase’ of the control programs implemented in Spain in the 1980's . Consistent with the human incidence data , average ovine and bovine CE prevalence for the 2000–2004 period were 2- and 4-fold higher in the Basque Country than in Spain , respectively . In this regard , it is worth mentioning that CE bovine infection proportions in the Basque Country have increased from 1 . 5% in 2006 to 4 . 6% in 2007 and 4 . 1% in 2008 [12] . Whether this finding indicates a true re-emergence will require further investigation . Taking together our epidemiological data clearly indicate that both human and animal CE infection rates in the province of Álava/Basque Country are well above the national average figures . The preferred way to capture both the human and agricultural effect of a zoonosis is to estimate its economic impact [33] . Indeed , to determine the economic burden of human and animal CE is recently identified by international experts as one of the key research priorities in the study of echinococcosis [34] . The aid of computer-based models have been increasingly used in recent years to estimate the socioeconomic burden of CE globally [4] and in a number of endemic regions including Iran [3] , Peru [2] , Spain [1] , the Tibetan plateau [16] , [35] , and Tunisia [36] . In line with these studies , we present here data on the economic impact of human and animal CE infection in the province of Álava for 2005 based on a Latin Hypercube design . By representing the uncertainty inherently associated to input parameters , this analytical tool is particularly suited for estimating indirect costs where accurate epidemiological information is scarce . Our project design benefits from the incorporation of data at the regional level . For instance , we used the annual CE incidence based on an active search of hospital records rather than the official , potentially underreported figures provided by the national surveillance system . Similarly , CE prevalence proportions in sheep and cattle were obtained from regional abattoir records and appropriately adjusted to exclude non-autochthonous CE infections . In addition , we used stratified rates of human CE infection and average wages by age and gender . For livestock , we used age-stratified prevalence proportions where available . Our estimates of the monetary burden associated with CE in the province of Álava in 2005 were between €0 . 06 and €0 . 36 million . These results reflect the range of variation observed between epidemiological scenarios where productivity losses associated to undiagnosed/asymptomatic infections were excluded , to a worse case situation in which the highest proportion of undiagnosed/asymptomatic CE were considered . All scenarios where asymptomatic cases were included depended largely on the estimated productivity losses and the prevalence of asymptomatic cases in the population . The costs associated with human cases represented from 83% to 94% of the total costs . These results are consistent with our previous estimates for Spain where asymptomatic cases represented 89 . 1% of the total cost in 2005 [1] . The upper and lower credible intervals of the “worst case scenario” ( scenario 2 ) differed by a factor of ten . This large uncertainty calls for urgent need for more studies of the prevalence of asymptomatic cases in this region and for research on how asymptomatic CE may affect productivity in humans . The economic burden associated with animal CE was also consistent with our previous estimates for Spain in 2005 [1] . Estimates associated with productivity losses contributed to the largest proportion of the total costs . This demonstrates , as with humans , the urgent need for studies to better estimate productivity losses associated with CE in livestock . In addition , our findings are in line with the fact that E . granulosus has been ranked second in the FAO/WHO list of foodborne parasites using a multi-criteria based approach reflecting the number and distribution of global illnesses , morbidity , mortality , the potential for an increased burden , trade relevance , and socio-economic impact [37] . Compared to other zoonotic agents in Spain , our estimated human cost of CE in Álava in 2005 ( €35 , 590–€335 , 380 ) is similar to that reported in the province of Málaga , Southwest Spain , for human brucellosis ( €347 , 569/year for the period 1984–1986 , projected to 2005 values ) [38] . In conclusion , our findings support the idea of an active transmission cycle of E . granulosus in the province of Álava primarily maintained between farm dogs acting as definitive host and mainly sheep as intermediate host species . This situation implies that domestic dogs have access to raw viscera of infected animals , most likely by the occasional feeding on carcasses abandoned in the fields . Parasite infection pressure appears sufficient even to induce disease in humans , as demonstrated by the detection of two allegedly autochthonous cases . Documented human and animal CE infection rates are well above national averages , providing strong evidence that the disease is underreported in Spain . Moreover , CE has a significant economic burden in this area , particularly due to indirect costs associated to losses attributed to undiagnosed/asymptomatic cases in humans and reduced productivity in livestock speciesThe results of our analysis also suggest that public health agencies and policy-makers maintain and/or intensify the surveillance and monitoring systems currently in place in order to decrease the prevalence of E . granulosus in this area and to avoid the recrudescence of the infection . Ultrasound-based epidemiological surveys in Spanish regions endemic for CE would allow far more accurate estimations on the actual proportions of undiagnosed/asymptomatic human cases . In addition , molecular studies aiming to investigate the frequency of Echinococcosis genotypes currently circulating in the province of Álava would be also useful to ascertain the transmission dynamics of the parasite . | Historically , cystic echinococcosis ( CE ) is one of the most important zoonotic diseases in Spain . The initiation of a number of control campaigns in the second half of the 1980s has led to a substantial reduction of the number of CE infections both in humans and livestock species . As a consequence of this initial success , obligation to report human CE cases was revoked in 1997 in a number of Spanish autonomous regions that were consequently considered non-endemic for CE . This policy has not been translated to livestock species , where the identification of hydatid cysts and the reporting of prevalence data remain compulsory in all national slaughterhouses . We present here an estimation of the human and animal CE associated monetary losses in the province of Álava , North Spain , for the year 2005 . Obtained economic data corroborate the epidemiological findings and demonstrate that CE has important socio-economic consequences in Álava . Taking together , our data suggest that surveillance status of CE in Álava ( and very likely in other Spanish regions currently classified as non-endemic areas ) should be reassessed . The situation also indicates that more accurate and effective methods for detecting and reporting the disease at the regional and national level are greatly needed . | [
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"d... | 2014 | Cystic Echinococcosis in the Province of Álava, North Spain: The Monetary Burden of a Disease No Longer under Surveillance |
The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function . In a manner sensitive to underlying structural brain architecture , we examine the extent to which such patterns of correlated activity systematically vary across cognitive states . Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent ( BOLD ) signals . Functional connectivity is separately measured at rest , during an attention task , and during a memory task . We assess these structural and functional measures within previously-identified resting-state functional networks , denoted task-positive and task-negative networks , that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes . We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong , task-dependent correlations in neural activity . The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting , attention , and memory states . We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance . These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function .
The brain is continually active , whether in a state of rest or during the performance of task-directed function . Despite predictions that resting-state neural activity would be noisy and unconstrained , the human brain has been shown to exhibit patterns of correlated neural activity even in the absence of any task-directed function [1] , [2] . Such correlations in “default mode” activity , which have been consistently identified within a diffuse network of brain regions [3]–[6] , are thought to support the functional organization of the brain [2] . Signatures of task-related function have similarly been identified in task-free states based on anticorrelations in spontaneous neural activity between default mode and task-related brain regions [7]–[10] . Together , these sets of brain regions have widely been associated with two functional networks , denoted task-positive and task-negative , composed of regions known to become more ( task-positive ) and less ( task-negative ) active during the task performance relative to their behavior at rest [7] . Correlations within these networks have been shown to support attention [11] and memory [12] , [13] processes , and disruptions to these networks have been implicated in neurological disorders [14]–[17] . While such studies have characterized individual functional networks within single task domains , recent studies suggest that interactions between functional networks are important for shaping attention [18] , memory [19] , and motor learning [20] , [21] performance . Anatomical studies have additionally shown that structural measures , such as the length and number of white matter tracts linking brain regions , play important roles in distinguishing global task-dependent changes in functional correlations [22] . Together , these findings suggest that anatomy may be important for shaping task-dependent interactions between functional networks , and these interactions may in turn be important for shaping behavior . Such relationships , however , are not understood . Does functional connectivity between networks vary systematically across across resting and task-driven cognitive states ? To what extent is such variation differentially supported by underlying anatomical organization ? How do such relationships between anatomy and function shape behavior ? To address these questions , we examine whether patterns of anatomical connectivity relate to the task-dependent strength of correlated neural activity within and between task-positive and task-negative networks , and we examine the extent to which such relationships are linked to behavioral differences in attention and memory performance . Structural and functional connectivity are estimated in 71 subjects using noninvasive neuroimaging techniques , where functional connectivity is separately estimated in three cognitive states: ( i ) at rest , ( ii ) during an attention task , and ( iii ) during a memory task . Behavioral performance is assessed in the same subjects during both the attention and memory tasks . In what follows , we uncover task-dependent links between human brain anatomy , function , and behavior both within individual subjects and across groups of subjects . We group brain regions based on their involvement in the task-positive and task-negative networks defined in [7] , and we show that the strength of anatomical connectivity within versus between these networks differentially supports strong , task-dependent functional correlations . The space mapped out by the observed structure-function relationships can be used to quantify a measure of separation between cognitive states , and we show that individual variability in this separation is linked to behavioral performance during both attention and memory tasks . Together , these results reveal that cognitive states are differentially supported by specific patterns of anatomical connectivity , and the observed association between anatomy and function is predictive of behavior .
Informed written consent was obtained from each subject prior to experimental sessions . All procedures were approved by the University of California , Santa Barbara Human Subjects Committee . We consider a complex network description of the human brain in which localized brain regions are represented as nodes , and the strengths of structural or functional connectivity between brain regions are represented as weighted , undirected connections between nodes [23] , [24] . A set of 600 cortical and subcortical regions roughly equal in size are chosen by upsampling anatomical regions within the Automated Anatomical Labeling ( AAL ) Atlas [25] ( Text S1 ) . We identify a total of 368 regions in our atlas that overlap wholly or partially with regions in task-positive and task-negative networks ( Text S1 ) , and we refer to the remaining regions as “other” regions . We focus on three of the six possible couplings between these three region types: couplings between two task-positive regions ( ) , two task-negative regions ( ) , and one task-positive and one task-negative region ( ) . We then compare these couplings to the remaining set of couplings between task-positive and other regions ( ) , between task-negative and other regions ( ) , and between two other regions ( ) . Figure 1a shows a schematic of possible couplings . To construct brain networks from this set of regions , we weight connections between regions by measures of structural and functional connectivity . Structural connectivity ( SC ) is obtained from diffusion tensor imaging ( DTI ) measurements via a tractography algorithm used to identify the number of white matter streamlines linking two regions [22] . Functional connectivity ( FC ) is obtained from functional magnetic resonance imaging ( fMRI ) measurements by computing Pearson's correlations between regional mean blood oxygen level dependent ( BOLD ) time series . FC is separately estimated ( i ) at rest ( ) , ( ii ) during the performance of an attention task ( ) , and ( iii ) during the performance of a memory task ( ) [26] . As task-based fluctuations in BOLD signals are small in comparison to resting-state values [27] , task-based FC is computed in deviations ) from rest [22] . See Text S1 and [22] for details regarding task design , connectivity estimates , and methodological considerations . Each subject is described by a structural and functional brain network whose connections are weighted by subject-specific values of SC and FC . Group-level properties can similarly be described by a “representative” brain network that combines information across all subjects . We construct two representative networks , one structural and one functional , by averaging the corresponding sets of connection weights across subject-specific networks , such that the representative connection weights correspond to the group mean values of SC and FC , consistent with previous studies [22] , [28] . Note that alternative techniques for constructing group-based connectivity networks may capture slightly different aspects of subject-specific network topology [29] . The assessment of group-level properties requires that we consider the degree to which SC is reliably present across subjects . While FC is typically non-sparse , SC can be both sparse and variable across subjects [22] , [30] . We therefore restrict all subsequent analyses to the subset of region pairs that are consistently linked by one or more white matter streamlines in at least 80% of subjects . We confirm that the observed structural and functional properties of the set of thresholded connections are robust to our specific choice of thresholding values ( Text S1 ) .
Functional correlations within task-positive and task-negative networks have been separately linked to attention and memory processes , but the integrated function of these networks is , as of yet , unclear . Should we view these networks as distinct modules that compartmentalize function , or as integrated networks that couple together in a task-dependent manner to shape cognitive function ? To address this question , we examine the extent to which couplings within and between task-positive and task-negative networks differentially shape task-dependent distributions of functional connectivity . We first assess the distributions of resting- , attention- , and memory-state functional connectivity within the sets of , , and couplings . These distributions exhibit features that are consistent with known properties of task-positive and task-negative networks . In the resting state , both and couplings exhibit stronger correlations than couplings ( inset of Figure 1c ) , consistent with the definition of task-positive and task-negative networks based on strong correlations within each network and anticorrelations between networks [7] ( note that anticorrelations are manifested here as weaker correlations between relative to and region pairs ) . Similarly , in the attention state , and couplings respectively exhibit larger increases and decreases in FC relative to the distribution of couplings ( Text S1 ) , consistent with known attention-driven increases and decreases in task-positive versus task-negative network activity . Given that strong functional correlations are hypothesized to support state-dependent cognitive function , we focus our analysis on the strong values of FC within the leading edges of the resting- , attention- , and memory-state distributions . To isolate strong correlations in each cognitive state , we apply a variable threshold to each distribution of FC . The thresholding process , which selects connections above a specified threshold value of connectivity strength , is common to graph theoretic analyses [24] and can be used to assess network properties at a fixed threshold value ( e . g . [28] , [31] ) or across variations in threshold values ( e . g . [30] , [32] , [33] ) . Here , we apply a threshold to the distribution of FC , and we examine changes in network connectivity across variations in this threshold value . The use of a variable threshold enables us to isolate structural network properties that support increasingly strong functional correlations . We characterize changes in network connectivity by examining the distribution of , , and couplings that support functional correlations above a variable threshold . A given threshold value will select the set of region pairs with . Within this set of region pairs , we measure the fractional number density of all structural connections that couple a given pair of regions and , with region labels . This process can be viewed as assessing the FC-dependent connectivity of a weighted structural network in which connections , defined by the reliable presence of SC in 80% of subjects , are weighted by the number of fiber tracts linking a given pair of regions . Qualitatively similar results are achieved by analyzing the connectivity of an unweighted structural network . However , we find that weighted networks better distinguish strong FC between different cognitive states than do unweighted networks , suggesting that both the presence and degree of structural connectivity play important roles in supporting strong state-dependent FC ( see Text S1 for comparison of weighted and unweighted network analyses ) . We vary the functional threshold and compute the change in number density relative to baseline ( with baseline computed in the absence of any threshold ) . We find that the relative changes in , , and densities vary systematically across resting , attention , and memory states . In the resting state , for example , we find that strongly-correlated region pairs are supported by a high density of connections ( positive ) and a low density of connections ( negative ) relative to their baseline values ( Figure 1c ) . To compare these relationships across cognitive states , we examine two quantities: the change in density ( ) and the relative changes in versus densities ( ) . Both quantities are evaluated as a function of the task-dependent thresholds , , and . The quantity measures the degree of coupling between task-positive and task-negative networks , with positive ( negative ) values of indicating increased ( decreased ) coupling relative to baseline . In comparison , the quantity measures the degree of localized coupling within either the task-positive or task-negative network , with positive ( negative ) values indicating localized coupling within the task-positive ( task-negative ) network . Figure 1d illustrates the relationship between these two quantities in the resting state ( note that this is a condensed representation of the information shown in Figure 1c ) . We refer to this representation as a “state-space” mapping , as it enables us to isolate the structure-function relationships that characterize each cognitive state . Comparison across cognitive states reveals that resting , attention , and memory states occupy distinct regions of this state space , differing from one another in the types of connection densities that support strong functional correlations ( Figure 2 ) . Strong resting-state correlations are supported by a decreased density of connections and an increased density of relative to connections , reflecting strong localization within the task-negative network and weak coupling between the task-positive and task-negative networks . Strong attention-state correlations , in comparison , are supported by an increased density of connections and an increased density of relative to connections , reflecting strong localization with the task-positive network and strong coupling between the task-positive and task-negative networks . Finally , strong memory-state correlations share features of both attention and rest , as they are supported by an increased density of relative to connections and an increased density of connections . These findings are consistent with known properties of task-positive and task-negative networks . The anticorrelation between the task-positive and task-negative networks manifests here as a decreased density of connections supporting strong resting-state correlations . Similarly , the known importance of task-positive brain regions in attention tasks manifests here as an increased density of relative to connections supporting strong attention-state correlations . Lastly , memory-state functional networks are known to overlap with both resting- and attention-state functional networks [13] . We similarly find that the types of connection densities that support strong memory-state FC are similar to those connection densities that support strong resting- and attention-state FC . Together , these results show that structural connections between task-related functional networks distinguish strong functional correlations measured in different cognitive states . The state-space description shown in Figure 2 reveals that cognitive states differ from one another in the structural features that support strong functional correlations . We investigate whether the observed separation between cognitive states , as quantified by differences in such structure-function relationships , is a general feature of subject-specific networks . Each subject-specific brain network can be remapped onto a state-space , analogous to that shown in Figure 2 , that compares subject-specific values and across resting , attention , and memory states . To compare across subjects , we compactly represent each subject by a triad of resting- ( ) , attention- ( ) , and memory-state ( ) distribution averages of and ( Figure 3a ) . The degree of separation between cognitive states can then be quantified by the angular separation between distribution averages . To isolate inter-subject variations in angular separation , we perform a remapping of the state space in which we represent each individual by a triangle whose vertices are defined by cyclical permutations of , where quantifies the angular separation between states and ( Figure 3b ) . In this representation , the size of a given triangle captures the degree of symmetric separation between cognitive states , with smaller triangles indicating that states are separated from one another by nearly equal angular distances . Similarly , the rotation of a given triangle captures the rank-ordering of angular separations . This remapping , shown in Figure 3b–c , reveals that the degree of separation between cognitive states is highly consistent across subjects . This consistency is illustrated by the clustering of points of the same color , and similarly by the largely overlapping sets of triangles that link these clusters . In a majority of subjects , attention and memory occupy similar regions of state space , as quantified by small values of and as illustrated by the clustering of green points near the center vertical axis . In these same subjects , rest occupies a distinct region of state space far from both attention and memory , as quantified by large values of and and as illustrated by the clustering of blue and red points near the left and right vertical axes . The observed organization is not an artifact of our analysis techniques , as confirmed via comparison with a null model in which “task-positive , ” “task-negative , ” and “other” region labels are randomly reassigned ( Text S1 ) . This representation naturally organizes subjects into three distinct groups based on the relative degree of separation between cognitive states ( Figure 3d ) . The primary group ( 66% of subjects ) exhibits less separation between the two task states than between task and resting states . These separations indicate that similar structural connections support large changes in both attention and memory FC , and these structural connections differ from those that support strong resting-state FC . The remaining subjects comprise two secondary groups , the first exhibiting the least separation between resting and memory states ( 23% of subjects ) , and the second exhibiting the least separation between resting and attention states ( 11% of subjects ) . Small separations between resting and task states indicate that task-dependent changes in FC , measured either during attention or memory tasks , are supported by similar structural connections as those that support strong resting-state FC . Importantly , the primary and secondary groups identified here are statistically similar to those groups identified from clustering algorithms ( Text S1 ) , confirming that the separation between cognitive states captures communities of subjects with similar structure-function relationships . However , the methodology developed here differs from such data-driven clustering algorithms in that it provides an intuitive framework for understanding relationships between cognitive states based on similarities in the underlying structural features that support these states . The organization of subjects based on the separation between cognitive states raises two important questions about the potential relationships between structure , function , and behavior . First , do primary and secondary groups , as identified by the structure-function relationships that distinguish between cognitive states , show absolute differences in attention and memory performance ? Second , is the degree of separation between attention and memory states , being the property that distinguishes between primary and secondary groups , indicative of relative differences between attention and memory performance ? We address both questions by comparing the behavioral performance of subjects within the primary versus secondary groups . We first assess whether the secondary groups , being outliers in the state-space mapping of structure-function relationships , are also outliers in absolute measures of attention and memory performance . We then assess whether the secondary groups , showing larger separations between attention and memory states in the state-space mapping , also show larger differences in attention versus memory performance . Both the attention and memory tasks were designed to measure subjects' ability to flexibly switch between decision strategies based on probabilistic information about the ( i ) likely position of a visual cue to be identified during the attention task , or ( ii ) the likelihood that a visual cue had been previously presented during a memory task [22] , [26] . Based on the design of these tasks , we focus on three relevant measures of task performance: the criterion switch score ( CS ) , the d-prime ( ) score , and the average reaction time ( RT ) . CS measures strategic flexibility in switching between decision making strategies , with higher values indicating the ability to more readily switch strategies . The measure assesses perceptual ( attention task ) or mnemonic ( memory task ) sensitivity as related to accuracy , with higher values indicating higher sensitivity and therefore higher accuracy . Lastly , average RT measures the average time between the appearance of a stimulus and a subject's response ( via a button press ) to that stimulus . A more detailed discussion of these performance measures can be found in [26] . We assess absolute performance by computing the deviation of a given measure from the group average value . We find that the secondary groups , being outliers by the measure of relative separation between cognitive states , are also outliers in attention and memory RT ( Figure 4a ) . The observed difference between primary and secondary groups is statistically significant ( a one-tailed -test of gives , ) . We similarly assess relative performance by computing the relative difference between a given measure assessed during attention ( ) versus memory ( ) tasks . We find that the secondary groups , exhibiting larger separations between attention and memory states , also show larger differences between the values of and CS measured during attention versus memory tasks ( Figure 4b ) . The observed difference between primary and secondary groups is again statistically significant ( a one-tailed -test of gives , ) . Note that and CS have previously been shown to be strongly correlated with one another [26] , such that relative differences in one measure may drive relative differences in the other . A repeated measures analysis of variance ( ANOVA; with two state-space groupings , primary and secondary , as categorical measures and with nine behavioral variables [ and for {RT , CS , }] as repeated measures ) further confirms that the observed differences between primary and secondary groups are statistically significant , with a main effect of grouping of and ( see Text S1 for full table of ANOVA results ) . These results show that the observed separations between cognitive states , which arise from differences in the patterns of structural connectivity that support strong functional correlations , are linked to both absolute differences in overall performance and relative differences between attention and memory performance . Interestingly , the primary and secondary groups differ from one another in absolute measures of RT but relative measures of and CS . Given that RT is a more general performance measure , while and CS are targeted by the attention and memory tasks under consideration , this suggests that individual differences in the rank-ordering of separations between cognitive states are manifested in general measures of performance , while individual differences in the degree of separation between two states are manifested in task-specific measures pertaining to those states .
This work examined couplings within and between the task-positive and task-negative networks identified in [7] . However , a wide range of different functional networks have been identified in the human brain and have been linked to both resting-state and task-driven neural activity ( e . g . [10] , [35] , [36] ) . A more detailed comparison of such functional networks could provide further insight into the structure-function relationships that distinguish between different cognitive states . Furthermore , interactions within and between these networks have been shown to change throughout development [37] , [38] and aging [39] , suggesting that the state-space of relationships found here could exhibit different characteristics across subjects of different ages . We demonstrated that patterns of structural connectivity within and between task-positive and task-negative networks differentially support task-dependent FC . The observed separation between cognitive states was measured as a function of the relative number of structural connections linking a given region pair , but qualitatively consistent results were observed when structural connectivity was defined in a binary manner based on the reliable presence of structural connections across a majority of subjects ( Text S1 ) . Probabilistic tractography algorithms ( e . g . [40] ) , which can identify crossing or branching fibers that would not be identified by the deterministic tractography algorithms used here , could improve estimates of structural connectivity and are therefore expected to further strengthen these results . Recent advances in computational platforms ( e . g . [41]–[43] ) provide additional model-based approaches for simulating brain dynamics using subject-specific patterns of anatomical connectivity . These platforms enable the identification of spatiotemporal motifs that support cognitive activity , as well as the biophysical parameters that constrain these motifs . Such anatomically-informed modeling approaches might help isolate features of structural brain architecture that shape the state-space mapping described here , such as transmission delays induced by long fiber tracts , or signal amplification due to large fiber bundles . Furthermore , such approaches might identify additional network motifs that distinguish state-dependent cognitive function . In combination with subject-specific anatomical constraints , these methods could help elucidate how individual variability in anatomical connectivity constrains functional interactions to ultimately shape behavioral performance . In assessing state-space relationships , resting , attention , and memory functional scans were taken to represent individual cognitive states . However , there is evidence of spatial and temporal variability within single functional domains [44]–[47] , suggesting an interplay between multiple cognitive states that each become more or less active throughout the duration of a given scan . The framework presented here , when combined with approaches for assessing nonstationary correlational structure [48] , could help uncover structural features that distinguish different dynamical patterns of activity observed within a given functional domain . The observation that structure-function relationships between cognitive states exhibit common state-space features suggests that these features may reflect general organizational principles of the brain . The state-space representation may therefore be useful for defining normative bounds on large-scale patterns of brain organization . When the features of this space are probed using suitably large numbers of subjects , regions of this space not occupied by healthy individuals could be predictive of disrupted structural or functional connectivity . A further characterization of the observed structure-function relationships across different behavioral and genetic measures could potentially be used to develop objective diagnostic measures of disrupted functionality . | Human cognitive function is thought to be supported by patterns of correlated neural activity . While recent work has shown that such functional correlations are differentially supported by specific properties of anatomical brain connectivity , the extent to which brain anatomy shapes cognition is not understood . In this study , we develop new network-based approaches for relating anatomical connectivity , correlations in neural activity ( functional connectivity ) , and behavioral task performance . We use noninvasive neuroimaging techniques to measure whole-brain connectivity in 71 subjects across three cognitive states: at rest , during an attention task , and during a memory task . By associating anatomical and functional connectivity with known functional brain networks , we show that the relative strength of inter- versus intra-network connectivity distinguishes between resting , attention , and memory states . When compared across subjects , we further show that the observed relationship between brain anatomy and function is predictive of individual differences in attention and memory task performance . | [
"Abstract",
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] | 2014 | Structurally-Constrained Relationships between Cognitive States in the Human Brain |
The prospect of eliminating onchocerciasis from Africa by mass treatment with ivermectin has been rejuvenated following recent successes in foci in Mali , Nigeria and Senegal . Elimination prospects depend strongly on local transmission conditions and therefore on pre-control infection levels . Pre-control infection levels in Africa have been mapped largely by means of nodule palpation of adult males , a relatively crude method for detecting infection . We investigated how informative pre-control nodule prevalence data are for estimating the pre-control prevalence of microfilariae ( mf ) in the skin and discuss implications for assessing elimination prospects . We analyzed published data on pre-control nodule prevalence in males aged ≥20 years and mf prevalence in the population aged ≥5 years from 148 African villages . A meta-analysis was performed by means of Bayesian hierarchical multivariate logistic regression , accounting for measurement error in mf and nodule prevalence , bioclimatic zones , and other geographical variation . There was a strong positive correlation between nodule prevalence in adult males and mf prevalence in the general population . In the forest-savanna mosaic area , the pattern in nodule and mf prevalence differed significantly from that in the savanna or forest areas . We provide a tool to convert pre-control nodule prevalence in adult males to mf prevalence in the general population , allowing historical data to be interpreted in terms of elimination prospects and disease burden of onchocerciasis . Furthermore , we identified significant geographical variation in mf prevalence and nodule prevalence patterns warranting further investigation of geographical differences in transmission patterns of onchocerciasis .
In 1995 , the World Health Organization launched the African Programme for Onchocerciasis Control ( APOC ) . At that time , APOC aimed to control morbidity due to onchocerciasis ( river blindness ) in Africa , with a focus on those countries not covered by the previous Onchocerciasis Control Programme in West Africa ( OCP ) . Since 1995 , APOC has successfully coordinated mass treatment with ivermectin in sixteen onchocerciasis-endemic African countries [1] . Until recently , elimination of onchocerciasis from African foci was deemed to be not achievable by means of mass ivermectin treatment alone , considering the large size of the transmission zones , the mobility of the insect vectors and human populations , and poor compliance with mass treatment in some areas [2] . However , following the first reports of elimination of onchocerciasis from foci in Mali , Senegal , and Nigeria by mass treatment alone [3] , [4] , [5] , there is renewed interest in elimination of onchocerciasis from Africa [6] . Pre-control infection levels are an important predictor of morbidity levels [7] , [8] , [9] and the duration of onchocerciasis control programs required to achieve elimination of infection [10] , [11] . High pre-control levels of infection indicate circumstances that are favorable for intense transmission in terms of vector abundance , proximity to vector breeding sites , high vectorial capacity and competence , etc . In such circumstances , mass treatment with a drug such as ivermectin , which is predominantly microfilaricidal , but has a lesser impact on adult worm survival , needs to be continued for a long time and at high therapeutic and geographical coverage before it can be stopped without considerable risk of recrudescence of infection . Progress towards elimination of onchocerciasis from APOC areas is currently being evaluated by means of ongoing skin snipping surveys that measure levels of infection in terms of presence and density of microfilariae ( mf ) in the skin of the general population [5] . In contrast , pre-control levels of infection in APOC areas have been quantified by the REMO method ( rapid epidemiological mapping of onchocerciasis ) , which is based on the palpation of subcutaneous nodules containing adult Onchocerca volvulus worms in a sample of 30–50 males aged ≥20 years in villages selected using a standardized selection procedure [12] , [13] . Results from pre-control and ongoing surveys will have to be compared , even though the REMO method is much cruder for detecting presence and intensity of infection than skin snipping . Therefore , it is important to assess how informative pre-control nodule palpation data are , and when and whether they can be reliably translated to equivalent measures of skin microfilariae . In other words , there is need for a quantitative model describing the association between pre-control nodule prevalence and pre-control presence of skin microfilariae , which takes into account the differences between the two methods as well as other covariates . Such a model would also allow estimates of pre-control nodule prevalence to be related to the large body of literature on the correlation between mf prevalence and prevalence of onchocercal morbidity , allowing better estimation of the disease burden of onchocerciasis . We present a statistical model describing the association between pre-control nodule prevalence in adult males and pre-control mf prevalence in the general population . Quantitative relationships for this association have been previously described , but were based on smaller number of surveys , did not provide estimates of uncertainty around parameter estimates and model predictions , and did not account for geographical variation or the relatively small sample sizes routinely used for the nodule palpation method , resulting in attenuation bias ( due to measurement error in nodule prevalence ) [14] , [15] , [16] , [17] . In this study , we analyzed original pre-control data , accounting for these factors , and using Bayesian statistical methods , well known for providing robust uncertainty estimates around model parameters .
We analyzed original data on pre-control nodule prevalence in adult males ( N = 7 , 525 individuals ) and mf prevalence in the population aged five years and above ( N = 29 , 775 individuals ) from 148 villages in seven geographical areas including countries in the former OCP area , and foci in Cameroon , Nigeria , and Uganda , which are part of APOC ( Table 1 , Figure 1 ) . Most of these data have been previously published [9] , [14] , [18] , [19] , except for part of the data from Cameroon . The simuliid vectors responsible for transmission in each area have been described previously ( Table 1 ) [9] , [19] , [20] , [21] , [22] , [23] . In all areas , data on nodule and mf prevalence had been collected simultaneously ( except for Nigeria , where nodule palpation took place six to twelve months after skin snipping , though still before the start of control interventions ) . All data on mf prevalence were based on taking two skin snips ( one from each iliac crest ) from each individual examined , which were incubated in saline for 24 hours , and village-level prevalence values were age- and sex-standardized according to the reference OCP population ( direct standardization , supplementary Table S1 ) . Then , we calculated the standardized number of mf positive persons in a village by multiplying the standardized prevalence with the sample size , and rounding to the nearest integer . Nodule prevalence was based on palpation-based detection of nodules that could be attributed to onchocerciasis with reasonable certainty , similar to the methodology used for mapping of infection in APOC areas; i . e . nodules of uncertain etiology ( e . g . possible enlarged lymph nodes ) were excluded [12] . All data were used with permission of the authors who originally collected such data , and were analyzed anonymously . The association between village-level mf prevalence and nodule prevalence was quantified in a meta-analysis by means of hierarchical multivariate logistic regression , i . e . logistic regression where the predicted outcome is a set of correlated binary random variables rather than a single binary random variable . A hierarchical approach was taken to account for unmeasured sources of variation between geographical areas . A multivariate approach was taken to account for measurement error in each measure of infection . This approach prevents regression of model coefficients towards zero ( attenuation bias ) as we do not have to assume that there is no measurement error in the explanatory variable ( e . g . either nodule or mf prevalence ) , an assumption inherent to univariate regression [24] . We extended the ordinary hierarchical logistic regression model to a multivariate model simultaneously predicting m binary outcomes:where is the probability of finding cases of the m-th binary outcome ( m = 1: presence of microfilariae in the skin; m = 2: presence of nodules in adult males ) among observed individuals from the i-th unit ( village ) and the j-th cluster ( geographical area ) . The error terms and ( each consisting of m components ) represent the variation ( random effects ) in infection levels within and between the j geographical areas , respectively . For each village there is a set of observed covariates , and for each of the m predicted binary outcomes there is a set of parameters ( fixed effects ) , where the intercepts and represent the mean log odds of presence of mf in the general population ( all those aged ≥5 years ) and nodules in adult males . To explain possible large differences between geographical areas related to bioclime , parasite strains and clinical manifestations in onchocerciasis [25] , we included a set of coefficients for bioclimatic zone in the model . Here , the parameters and represent the log odds ratio of observing microfilariae in the skin and subcutaneous nodules in forest areas ( including degraded forest and forest-savanna mosaic areas ) , relative to savanna areas . Correlation between nodule and mf prevalence was modeled by assuming a multivariate normal distribution for the m components of the error term at each level of analysis . See supplementary Text S1 , section “Model description” for a more detailed description of the model . To account for measurement error due to misclassification of nodules ( e . g . classifying lymph nodes as onchocercal nodules due to imperfect specificity; or failing to detect at least one subcutaneous onchocercal nodule when one or more are present , due to imperfect sensitivity ) , we added parameters to the model for specificity and sensitivity of nodule palpation , allowing these to be estimated from the data . Prior information for parameter values was based on the literature . A wide range of values is reported for specificity ( 60%–99% ) , based on various definitions [15] , [19] , [26] , [27] . We assumed that when performed by physicians experienced in recognizing onchocercal nodules , specificity of nodule palpation is between 98% and 100% , based on the report of finding only four non-onchocercal nodules among 312 extirpated nodules [19] . Further , we assumed that sensitivity increases with level of infection , reflecting the notion that detection of at least one nodule is more likely in a person with many onchocercal nodules than in a person with few or only one [27] . In literature , no values for sensitivity of nodule palpation as a method for detecting onchocercal nodules are reported . In the current study , sensitivity was assumed to increase linearly from some unknown minimum sensitivity ( value between 60% and 100% ) for nodule prevalences close to zero ( when persons with nodules have few nodules ) to 100% for nodule prevalence of 100% . The choice of a linearly increasing pattern was based on a simulation exercise in which we examined the association between the proportion of the nodule carriers that is detected and the ‘true’ nodule prevalence , given simulated true nodule counts ( assuming a negative binomial distribution of counts within a village ) and some probability to detect each nodule ( minimum sensitivity ) . A sensitivity analysis showed that the model fit and model predictions did not change when assuming different values for minimum sensitivity of nodule palpation at low infection levels ( 60% , 80% , or 100% ) . This is explained by the fact that sensitivity is most important for high prevalence settings ( for which we assume sensitivity is high anyway ) , and far less important in low prevalence settings ( where misclassification is largely governed by specificity ) . Therefore , we simplified the final model by leaving out the parameter for sensitivity , effectively assuming 100% sensitivity of nodule palpation for all infection levels . Based on the model described above , we estimated the conditional distribution of mf prevalence in a hypothetical village outside the dataset , given an estimate of the ‘true’ nodule prevalence in adult males ( i . e . corrected for misclassification of nodules ) . We assumed that nodule prevalence estimates were based on a sample of 30 adult males , the minimal sample size used in REMO surveys [12] , [13] . See Text S1 , section “Model application” for a more detailed description of the methods for predicting mf prevalences in hypothetical villages . The model was fitted to the data in a Bayesian framework . Posterior distributions of parameters and predictions were simulated in JAGS ( see Text S1 , section “Model specification in JAGS” for code ) , a program for analysis of Bayesian models using Markov Chain Monte Carlo ( MCMC ) simulation based on the Gibbs sampling algorithm ( version 3 . 2 . 0; Martyn Plummer , 2012 , http://mcmc-jags . sourceforge . net ) . Simulations in JAGS were set up and analyzed in R ( version 2 . 14 . 2 ) [28] , using packages rjags ( version 3–5 , Martyn Plummer , 2011 , http://CRAN . R-project . org/package=rjags ) and R2jags ( version 0 . 03-06 , Yu-Sung Su , 2011 , http://CRAN . R-project . org/package=R2jags ) . Improvements in model fit by addition of parameters were assessed via the deviance information criterion ( DIC ) , a generalization of Akaike's information criterion for hierarchical models ( lower values indicate better fit , taking into account model deviance and the effective number of parameters in the model ) [29] . See Text S1 , section “Parameter estimation” for further details about model fitting and checking of model convergence . The final fit of the model to the data was evaluated by means of mixed posterior predictive checks [30] , [31] . In this procedure , the number of individuals positive for mf and nodules in each village was resampled 40 , 000 times from the estimated joint posterior distribution of model parameters , including resampling of all random effects , and the resulting replicate dataset was compared to the original data .
The median nodule prevalence in males aged ≥20 years was 58% ( range: 2%–100% ) , and the median mf prevalence in the population aged five years and above was 74% ( 4%–99% ) . The median sample size for nodule prevalence in a village was 42 ( range: 9–181 ) . The median sample size for mf prevalence in a village was 167 ( 33–727 ) . Nodule prevalence in adult males was strongly positively correlated with mf prevalence in the general population ( Table S2 ) . There was significant geographical variation in patterns of nodule and mf prevalence; in a model without any coefficients for bioclime , the DIC increased from 1918 to 1920 when error term was omitted . Point estimates of were very similar for savanna and forest areas , with the exception of Mbam , Cameroon ( forest-savanna mosaic ) , for which mf prevalence was relatively high compared to other areas . In line with this , the model fit did not improve when a fixed effect parameter for bioclime was added to the model . However , the model fit improved significantly when modeling the difference between Mbam and all other areas as a fixed effect ( DIC 1913 vs . DIC 1918 ) , indicating that mf prevalences in Mbam were significantly higher than those in other areas ( Table S2 , Figure 2 ) . After this adaptation of the model , there was still significant variation in patterns of nodule and mf prevalence between geographical areas due to other , unmeasured variables ( the DIC increased to 1921 when error term was omitted ) . Further , there was considerable uncertainty in the predictions for mf prevalence , based on nodule prevalence in a sample of 30 males from a hypothetical village outside the dataset ( Figure 3 ) . Mixed posterior predictive checks showed that the model fitted well to the data ( Figure 4 ) . Only three villages – all from different regions , and all with relatively low infection levels compared to other villages from the same region – deviated significantly from the model predictions .
We investigated the association between pre-control nodule prevalence in adult males ( aged ≥20 years ) and pre-control mf prevalence in the general population ( aged ≥5 years ) . Our model is the first to examine geographical variation due to bioclime and other unmeasured variables , and to take account of measurement error in nodule prevalence . Our results show that there is a strong positive correlation between nodule and mf prevalence , but also significant variation between geographical regions , which should be taken into consideration when evaluating the prospects of elimination and the burden of disease . Our analysis showed significant geographical variation in patterns of nodule and mf prevalence , though not related to bioclimatic zones according to the classic forest vs . savanna classification of onchocerciasis . In ‘forest’ areas – Lekié , Cameroon ( degraded forest ) and Kigoyera parish , Uganda ( forest ) – the patterns in nodule and mf prevalences did not differ much from the pattern in savanna areas . Yet , we found that mf prevalence levels in the general population were relatively higher in the only forest-savanna mosaic area ( Mbam , Cameroon ) , while nodule prevalence in adult males levels were not significantly different . There are several possible explanations for this pattern . Most likely , the pattern in Mbam is explained by a different pattern in age-dependent exposure to black flies' bites . Both mf and nodule prevalences in individuals under the age of twenty years were relatively high in Mbam compared to the other areas in Cameroon , especially in villages with relatively low nodule prevalence in adult males ( data not shown ) . This indicates that individuals in Mbam experience relatively high exposure levels at a young age . This might be explained by the presence of dense forest in this region with relatively few narrow open spaces , which is associated with higher dispersal of flies around the breeding sites [32] . Therefore , exposure may not be concentrated near the breeding sites , but may extend over the whole village . Related to this , exposure may be less concentrated in adults ( who frequently spent time near the breeding sites , forest galleries for fishing , etc . ) , but may be more equally distributed over all age groups . However , dense forest may not be unique for Mbam , and may also be present in other forest areas in our data set . Therefore , we can only say that it may be important to consider age-dependent patterns in exposure to black flies' bites and their effect on transmission when translating nodule prevalence data to mf prevalence . We rule out demography and survey methods , as all mf prevalences were standardized , the mean age of the sampled men from Mbam was similar to that of men from the other Cameroonian areas , methods for skin snipping and mf enumeration were the same as in other Cameroonian areas and , in addition , even conducted by the same person ( MB performed all skin snipping in Faro , Lekié , and Mbam , and 50% of skin snipping in Vina valley ) . Furthermore , it is also unlikely that the forest sites other than Mbam – Lekié and Kigoyera parish – harbor a savanna parasite strain ( instead of the assumed forest parasite strain ) as this is inconsistent with observed patterns of blindness in these areas ( forest pattern ) [33] , [34] . Lastly , variation might have been caused by parasite characteristics not related to the classic subdivision into forest and savanna strains . Herder [35] concluded that the parasite strains circulating in the Faro and Mbam areas were related but distinct from the strains from Vina and Lekié , based on phylogenetic linkage patterns . However , this pattern was not confirmed by our analysis as the association between nodule and mf prevalence in Faro was very similar to the other areas but Mbam . Our model could be used as a tool for assessing the prospects of elimination of onchocerciasis or the burden of onchocercal disease when pre-control nodule prevalence in adult males is the only measure of infection available ( as is the case for most of Africa ) . With our model , an estimate of pre-control mf prevalence may be derived from pre-control nodule prevalence data . Such an estimate may be helpful for program planning , providing an indication of minimum program duration ( with regard to prospects of elimination ) , and could be helpful in the interpretation of ongoing epidemiological parasitological surveys that rely on the skin snipping method ( in terms of progress towards elimination ) . Prospects of elimination may be evaluated by comparing the model-derived estimate of mf prevalence to known trends of infection levels in other foci with a similar history of mass treatment , or by means of dynamic modeling of the effect of mass treatments with ivermectin using onchocerciasis transmission models such as ONCHOSIM [10] , [11] , [36] and others [37] , [38] , [39] . Progress towards elimination could be evaluated by comparing current mf prevalences with model-derived estimates of pre-control mf prevalence and predicted trends in infection levels based on dynamical modeling . Likewise , the pre-control burden of ocular and dermal morbidity in endemic areas may be estimated based on literature data on the association between mf and disease prevalence [7] , [8] , [9] . This would further allow assessment of the impact of control activities on population health , especially when combined with aforementioned dynamic models . If pre-control mf prevalence were to be severely underestimated or overestimated when derived from nodule prevalence data ( due to measurement error and geographical variation ) , this may have important repercussions for the number of treatment rounds that is thought to be required to reach elimination , or the estimated burden of disease . Therefore , it is crucial to consider variation due to sample size and geographical variation in patterns of nodule and mf prevalence when doing this kind of assessment . Given the high level of variation and consequent uncertainty in the association between nodule and mf prevalence , translations should be made carefully and critically evaluated . We recommend that translations of village-level REMO data ( based on samples of about 30 adult males ) to mf prevalence are made based on the black lines in Figure 3 ( which include uncertainty due to measurement error and geographical variation ) . In case of suspected high exposure of children to flies' bites , it may be more appropriate to apply the part of the model that mimics the observations in Mbam , Cameroon ( grey lines in Figure 3 ) . For areas where infection prevalence is known to be homogeneously distributed , REMO samples from multiple villages could be pooled into a more precise estimate of pre-control nodule prevalence in the area , allowing more precise prediction of the pre-control mf prevalence . In Text S1 , section “Model application” , we explain in more detail how our model should be applied to convert nodule prevalence to mf prevalence ( e . g . how to make predictions for a group of villages ) . In conclusion , we provide a tool to convert nodule prevalence in adult males to mf prevalence in the general population , which accounts for uncertainty due to measurement error and geographical variation . This tool allows interpretation of a large amount of pre-control data on levels of infection in Africa which may a ) be combined with information on coverage of mass treatment to assess the feasibility of elimination of onchocerciasis and b ) enable estimation of disease burden . Furthermore , we identified significant geographical variation in mf prevalence and nodule prevalence patterns that warrants further investigation of age-dependent transmission patterns of onchocerciasis . | Until recently , elimination of onchocerciasis ( river blindness ) from Africa by mass treatment with ivermectin alone was deemed impossible . However , recent reports of elimination of onchocerciasis from various African foci have stimulated renewed interest . An important determinant of achieving elimination is the pre-control microfilarial ( mf ) prevalence , i . e . the percentage of people with larval stages of the Onchocerca volvulus worm in the skin , which can be detected in a skin snip ( a small skin biopsy ) . Because this method is considered invasive , pre-control infection levels in Africa have been mapped mostly by means of palpation of subcutaneous nodules ( protuberances under the skin where the adult worms live ) in adult males , a relatively crude but non-invasive method of detecting infection . We developed a tool to derive estimates of pre-control mf prevalence from available pre-control nodule prevalence estimates . This tool can help evaluate ongoing control programs , help assess local elimination prospects , and help estimate levels of disease due to onchocerciasis by linking pre-control nodule palpation data to the large body of literature on the association between mf prevalence and disease . | [
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"... | 2013 | Onchocerciasis: The Pre-control Association between Prevalence of Palpable Nodules and Skin Microfilariae |
Persistent infections with hepatitis C virus ( HCV ) may result in life-threatening liver disease , including cirrhosis and cancer , and impose an important burden on human health . Understanding how the virus is capable of achieving persistence in the majority of those infected is thus an important goal . Although HCV has evolved multiple mechanisms to disrupt and block cellular signaling pathways involved in the induction of interferon ( IFN ) responses , IFN-stimulated gene ( ISG ) expression is typically prominent in the HCV-infected liver . Here , we show that Toll-like receptor 3 ( TLR3 ) expressed within uninfected hepatocytes is capable of sensing infection in adjacent cells , initiating a local antiviral response that partially restricts HCV replication . We demonstrate that this is dependent upon the expression of class A scavenger receptor type 1 ( MSR1 ) . MSR1 binds extracellular dsRNA , mediating its endocytosis and transport toward the endosome where it is engaged by TLR3 , thereby triggering IFN responses in both infected and uninfected cells . RNAi-mediated knockdown of MSR1 expression blocks TLR3 sensing of HCV in infected hepatocyte cultures , leading to increased cellular permissiveness to virus infection . Exogenous expression of Myc-MSR1 restores TLR3 signaling in MSR1-depleted cells with subsequent induction of an antiviral state . A series of conserved basic residues within the carboxy-terminus of the collagen superfamily domain of MSR1 are required for binding and transport of dsRNA , and likely facilitate acidification-dependent release of dsRNA at the site of TLR3 expression in the endosome . Our findings reveal MSR1 to be a critical component of a TLR3-mediated pattern recognition receptor response that exerts an antiviral state in both infected and uninfected hepatocytes , thereby limiting the impact of HCV proteins that disrupt IFN signaling in infected cells and restricting the spread of HCV within the liver .
Hepatitis C virus ( HCV ) is an hepatotropic , positive-strand RNA virus classified within the Flaviviridae family [1] . It is an important human pathogen since most individuals fail to eliminate the virus when first infected . This results in persistent infection and a chronic inflammatory state within the liver that leads over time to clinically significant complications including progressive liver fibrosis , cirrhosis , and hepatocellular carcinoma . The mechanisms underlying these events are only partially understood . The single-stranded RNA ( ssRNA ) HCV genome encodes a large polyprotein precursor of approximately 3000 amino acid residues . This is cleaved co- and post-translationally into at least 10 mature proteins , at least 3 of which contribute to the virus structure ( core , and two envelope proteins , E1 and E2 ) , with the remaining 7 proteins generally considered to be nonstructural in nature ( p7 , NS2 , NS3 , NS4A , NS4B , NS5A , and NS5B ) . NS5B is an RNA-dependent RNA polymerase and the catalytic core of a large macromolecular , membrane-bound replicase complex that directs replication of the viral RNA , producing double-stranded RNA ( dsRNA ) replication intermediates as well as new viral genomes . These viral RNAs are recognized as pathogen-associated molecular patterns ( PAMPs ) by innate immune sensors in host cells , but exactly which sequences and how these RNAs are sensed remains only partly elucidated [2] . In general , dsRNAs produced by viruses are recognized by several classes of cellular pattern recognition receptors , including retinoic acid-inducible gene I ( RIG-I ) -like helicases that are expressed within the cytoplasm , or Toll-like receptors ( TLRs ) , such as TLR-3 that is expressed within and signals from a late endosomal compartment in several different cell types , including hepatocytes [3] , [4] , [5] , [6] . The engagement of these receptors by viral RNAs results in the induction of interferon ( IFN ) -β synthesis through separate downstream signaling pathways that utilize unique adaptor molecules but result in the common activation of the transcription factors NF-κB and IFN regulatory factor 3 ( IRF-3 ) [reviewed in 7] . This leads in turn to the expression of numerous IFN-inducible genes , including IFN-stimulated gene ( ISG ) 15 , ISG56 , and others , through both autocrine and paracrine signaling involving the Janus kinase/signal transducer and activator of transcription ( JAK/STAT ) pathway . RIG-I , and the related cytosolic helicase , myeloid differentiation-associated gene 5 ( MDA5 ) and TLR3 differ substantially in their ligand specificities . RIG-I recognizes short dsRNA molecules and ssRNA with a 5′-triphosphate , while MDA5 recognizes lengthy dsRNA [5] , [8] . In addition , the roles played by RIG-I ( which is generally constitutively expressed ) and MDA5 ( IFN-inducible ) in recognition of RNA viruses appear to vary by virus type . RIG-I is primarily responsible for innate immune recognition of paramyxoviruses , influenza virus and Japanese encephalitis virus , while MDA5 is thought to be critical for the recognition of encephalomyocarditis virus and possibly other picornaviruses [5] . TLR3 recognizes dsRNA molecules greater than 40–50 bp in length and also mediates the induction of antiviral responses in cells infected with a wide variety of RNA viruses , including West Nile virus , rhinovirus , respiratory syncytial virus , vesicular stomatitis virus , lymphocytic choriomeningitis virus and influenza virus [9] , [10] . However , unlike the cytosolic RIG-I-like helicases , TLR3 initiates signaling from within the endosome in a process that is dependent upon acidification of the endosome [11] , [12] . In HCV-infected cells , the 5′-triphosphate of genomic RNA is recognized by RIG-I , as is a poly-U/UC segment near the 3′ end of the genome that serves as a PAMP [13] , [14] . This results in the induction of an IFN response shortly after infection of cells [14] . However , RIG-I-initiated signaling is subsequently blocked since NS3/4A , the major HCV protease , proteolytically cleaves the essential RIG-I adaptor protein , mitochondrial antiviral signaling protein ( MAVS ) [14] , [15] , [16] . The only continuous cell lines that support robust HCV replication are derived from Huh-7 human hepatoma cells , which like many transformed hepatocytes are TLR3 null [17] . However , HCV infection similarly induces an early antiviral response in RIG-I-defective Huh-7 . 5 cells in which TLR3 expression has been reconstituted [4] . As with RIG-I signaling , TLR3 signaling is also blocked as viral proteins accumulate and NS3/4A mediates cleavage of an essential adaptor protein , in this case Toll-like receptor adaptor molecule 1 ( TICAM-1 , a . k . a . TRIF ) [4] , [18] . How TLR3 is able to sense HCV infection is uncertain , since double-stranded HCV RNA is detected primarily adjacent to ER membranes in association with the nonstructural protein , NS5A , or juxtaposed to cytoplasmic lipid droplets [19] , while TLR3 , as described above , senses dsRNA and initiates signaling only from within late endosomes [11] , [12] . One possibility is that dsRNA replication intermediates are released into the extracellular milieu by HCV-infected cells and , like poly ( I:C ) added to the medium of cultured cells , subsequently sensed by TLR3 . Since TLR3 is unable to bind extracellular dsRNA on the cell surface [12] , this would require a receptor molecule to direct the endocytosis and trafficking of dsRNA to the late endosome . Scavenger receptors of several different classes , including C-type lectin domain-containing receptors such as the oxidized low density lipoprotein ( LDL ) receptor ( ORL-1 ) , or class A scavenger receptors such as macrophage scavenger receptor 1 ( MSR1 ) , have been suggested to fulfill this function , transporting dsRNA from the surface of cells to the endosome where it is sensed by TLR3 [20] , [21] , [22] . The epidermal growth factor receptor ( EGFR ) also plays an essential role in TLR3 signaling , but this appears to occur at a later step , following the engagement of TLR3 by dsRNA within the endosome [23] . Here , we show that MSR1 is essential for TLR3 sensing of HCV infection in cells derived from human hepatocytes , and that it mediates the establishment of a localized antiviral response in neighboring , uninfected cells that restricts the replication of virus in cell culture . Since uninfected hepatocytes are not subject to the myriad mechanisms that virally-encoded HCV proteins have evolved to disrupt the induction of IFN responses [2] , these observations reveal a mechanism that may explain both the significant levels of ISG expression that typify the HCV-infected liver [24] , as well as the fact that only a small minority of hepatocytes are infected by HCV in human liver [25] .
While TLR3 is expressed and functional in primary cultures of human hepatocytes , Huh-7 cells and their derivatives have a TLR3-null phenotype [4] , [17] . Thus , to establish an HCV-permissive cell line in which we could study viral interactions with the TLR3 signaling pathway , we reconstituted TLR3 expression in Huh-7 . 5 cells , which are also RIG-I-deficient [4] , [26] . Using this , and related cell lines , we demonstrated previously that HCV infection is sensed by TLR3 , resulting in an early antiviral response [4] . However , HCV ultimately restricts TLR3 signaling as the NS3/4A protease mediates cleavage of TICAM-1 as viral proteins accumulate in abundance [4] , [18] . To further confirm that TLR3 induces a functional antiviral response against HCV , we compared the number of colonies of viable cells originating from TLR3-competent Huh7 . 5-TLR3 cells transfected with a dicistronic , genome-length HCV replicon that expresses neomycin phosphotransferase [pTat2ANeo/H77S , 27] versus similarly transfected TLR3-null Huh-7 . 5 cells reconstituted with non-functional TLR3 mutants . As expected , the number of G418-resistant colonies arising from Huh7 . 5-TLR3 cells was significantly lower than from several related , TLR3-null Huh-7 . 5 cell lines: Huh-7 . 5-Vect ( empty vector ) , −ΔTIR ( TLR3 lacking the TIR domain required for downstream signaling ) , and −H539E and −N541A ( point mutations in the ectodomain of TLR3 that restrict its ability to bind dsRNA ) ( Supplementary Fig . S1 ) . Consistent with these results , quantitative real-time PCR demonstrated significant increases in the abundance of IFN-β and ISG56 mRNAs in Huh7 . 5-TLR3 cells following infection with a laboratory strain of HCV ( HJ3-5 virus ) ( Fig . 1A , left and center panels ) . Similar increases were not observed in Huh-7 . 5 cells expressing the signaling-incompetent TLR3 mutants H539E or N541A . Importantly , prior UV inactivation of the virus ( UV-HCV ) almost completely eliminated the increase in ISG56 mRNA ( Fig . 1A , right panel ) . On the basis of these and our previously published results [4] , we conclude that TLR3 senses HCV infection and induces the expression of a functional antiviral state in Huh7 . 5-TLR3 cells , and that this requires active replication of the virus . This is consistent with a recent report indicating that TLR3-mediated induction of proinflammatory cytokines by HCV also requires active replication of virus [28] . To better understand these results , we examined the ability of in vitro synthesized HCV RNAs to stimulate TLR3 signaling . Since replication of the virus is required to initiate signaling , we reasoned that TLR3 signaling follows its engagement by dsRNA molecules produced by transcription of negative-strand RNA intermediates , rather than multiple structured RNA elements that are known to exist within the 5′ and 3′ untranslated regions as well as in the core and NS5B protein-coding regions of the positive-strand genome [29] . To test this , we synthesized full-length positive- and negative-strand viral RNAs by in vitro transcription using as template an infectious molecular clone of the genotype 2a JFH1 strain ( Fig . 1B , upper panel , lanes 1 and 2 ) , and annealed these to produce HCV dsRNA ( Fig . 1B , lane 3 ) . As anticipated , the dsRNA products produced by annealing the two ssRNAs were highly heterogenous , resulting in a smear when separated by agarose gel electrophoresis . However , a small amount of what appeared to be full-length dsRNA was generated ( Fig . 1B , arrow ) . While the positive- and negative-sense ssRNAs were degraded by S1 nuclease , this dsRNA was S1 nuclease-resistant , confirming that the complementary in vitro synthesized HCV RNAs anneal to produce dsRNA ( Fig . 1B , lower panel ) . Importantly , only the nuclease-resistant dsRNA induced transcription of ISG56 mRNA when added to the medium of PH5CH8 cells , a T-antigen transformed , human hepatocyte line that naturally expresses TLR3 , or Huh7 . 5-TLR3 cells [30] ( Fig . 1C , left and right panels ) . HCV dsRNA also induced ISG15 protein expression in PH5CH8 and Huh7 . 5-TLR3 cells ( Fig . 1D ) . Prior transfection of PH5CH8 cells with shRNA targeting TLR3 eliminated the induction of ISG15 protein expression by HCV dsRNA ( Fig . 1D , left panel ) , confirming that ISG15 induction was mediated through TLR3 . Prior digestion of the dsRNA with S1 nuclease did not alter the pattern of ISG15 induction ( Fig . 1D right panel ) . Collectively , these data indicate that S1-resistant , double-stranded synthetic HCV RNA is capable of triggering an antiviral response through TLR3 , while highly structured RNA elements of the single-stranded genome are not sensed by TLR3 . In part , this may be due to the length of the synthetic dsRNAs , which mimic very lengthy dsRNA intermediates produced during replication of the 9 . 7 kb HCV genome ( Fig . 1B ) and are much longer than the stable helices present in the ssRNA . While the minimum length of dsRNA required for recognition by the TLR3 ectodomain is on the order of 40 to 50 base pairs [31] , we have observed that TLR3 is substantially more responsive to high-molecular weight ( 1 . 5–8 . 0 kb ) versus low molecular weight poly ( I:C ) ( 0 . 2–1 . 0 kb ) ( Supplementary Fig . S2 ) . Consistent with the induction of a TLR3-mediated antiviral response by HCV , we detected formation of a TLR3-HCV RNA complex in Huh7 . 5-TLR3 cells infected with HJ3-5 virus . This was accomplished by immunoprecipitating TLR3 from lysates of infected Huh7 . 5-TLR3 cells with an anti-Flag antibody , and subjecting RNA extracted from the precipitates to RT-PCR specific for HCV RNA . Viral RNA was readily detected in anti-Flag precipitates prepared from lysates of infected Huh7 . 5-TLR3 cells ( Fig . 1E , lane 2 ) . While somewhat less Flag product was evident in immunoblots of precipitates from cells expressing TLR3-H539E or TLR3-N541A , TLR3 mutants deficient in binding dsRNA [4] , HCV RNA was not detected in these precipitates ( Fig . 1E , lanes 4 and 6 ) . Taken together , these results indicate that dsRNA produced in membrane-bound replicase complexes during HCV replication is transported to the late endosome where it is engaged by TLR3 . Subsequent experiments focused on the mechanism by which dsRNA traffics to the endosome . Recent studies suggest that class A scavenger receptors , including “macrophage” scavenger receptor 1 ( MSR1 , class A scavenger receptor type 1 , transcript variant 1 ) , are expressed in a variety of cell types and serve as the dominant receptors mediating endocytosis of dsRNA in fibroblasts [20] , [22] . Class A scavenger receptors recognize a broad range of ligands including acetylated LDL , and lipopolysaccharide ( LPS ) produced by Gram-positive bacteria , and have been shown to mediate endocytosis of both ssRNA and dsRNA [22] , [32] . We thus hypothesized that dsRNA could be released into the extracellular milieu by hepatocytes infected with HCV , where it could be bound by scavenger receptors expressed on the cell surface and subsequently transported to the endosome for recognition by TLR3 . To confirm that MSR1 , and possibly other members of the class A scavenger receptor family , are expressed by human hepatocytes , we utilized RT-PCR to ascertain the presence of mRNA transcripts for these proteins in PH5CH8 cells . Transcripts encoding MSR1 ( SCARA1 , transcript variant 1 , referred to generally as “MSR1” ) , scavenger receptor class A member 3 ( SCARA3 , transcript variants 1 and 2 ) , SCARA4 ( a . k . a . collectin sub-family member 12 , COLEC12 ) , and SCARA5 ( a putative class A scavenger receptor ) transcripts were readily detected ( Fig . 2A ) . Transcripts for MSR1 transcript variant 2 ( referred to as “SR-AII” ) , and MARCO ( a . k . a . SCARA2 ) were not detected , or were present only in very low abundance . MSR1 and SCARA3 , variants 1 and 2 , expression were also confirmed in Huh-7 . 5 cells ( Fig . 2B , top panel ) . However , in contrast to PH5CH8 cells , SR-AII transcripts were also evident in Huh-7 . 5 cells , while SCARA5 transcripts were absent . Consistent with these results , immunoblots demonstrated expression of MSR1 in both Huh7 . 5-TLR3 and PH5CH8 cells ( Fig . 3A ) . To determine whether it plays a role in TLR3 signaling in these hepatocyte-derived cell lines , we depleted MSR1 by transducing Huh7 . 5-TLR3 cells ( Huh7 . 5-TLR3/shMSR1 cells ) and PH5CH8 cells ( PH5CH8/shMSR1 cells ) with lentiviruses expressing MSR1-specific shRNA . Immunoblots demonstrated modest depletion of MSR1 in both Huh7 . 5-TLR3/shMSR1 cells ( 63% depletion estimated by quantitation of the Odyssey infrared fluorescence signal against β-actin control ) and PH5CH8/shMSR1 cells ( 55% depletion ) . RT-PCR confirmed MSR1 knockdown in the PH5CH8 cells , and demonstrated that it was specific and without effect on SCARA3 , SCARA4 , or SCARA5 transcripts ( Fig . 2A ) . Additional studies showed that MSR1 knockdown had no reciprocal effect on the abundance of SR-AII transcripts ( data not shown ) . Although the MSR1 knockdown was relatively inefficient in both cell types , flow cytometry indicated that time-dependent uptake of FITC-labeled high molecular weight ( HMW ) poly ( I:C ) , a surrogate for HCV dsRNA , was functionally eliminated in MSR1-depleted cells compared to control cells ( Fig . 3B ) . The high degree of inhibition of poly ( I:C ) uptake was surprising , given the rather modest degree of MSR1 depletion evident in either cell type ( Fig . 3A ) . It suggests that a threshold level of expression is required for efficient dsRNA uptake , possibly because class A scavenger receptors function as multimeric complexes . IFN-β and NF-κB-responsive PRDII promoter activities were also decreased in MSR1-depleted cells compared to control cells when stimulated by the addition of poly ( I:C ) to the medium ( Fig . 3C ) , as was poly ( I:C ) induction of ISG56 ( Fig . 3D ) and ISG15 ( Fig . 3E ) expression . These results indicate that MSR1 is required for poly ( I:C ) uptake and optimal induction of TLR3-mediated signaling by poly ( I:C ) in human hepatocytes . Next , we examined whether MSR1 is required for the recognition of HCV-specific dsRNA through TLR3 . As with poly ( I:C ) , MSR1 depletion eliminated the induction of ISG15 or ISG56 by synthetic HCV dsRNA added to the medium bathing Huh7 . 5-TLR3 cells ( Fig . 3F and G ) . We also demonstrated that MSR1 functions in the induction of TLR3-mediated antiviral responses to HCV infection by monitoring viral replication in Huh7 . 5-TLR3/shNT cells and Huh7 . 5-TLR3/shMSR1 cells following infection with HJ3-5/GLuc2A virus [33] that expresses the secreted reporter protein , Gaussia princeps luciferase ( GLuc ) . Replication was assessed by measuring secreted GLuc activity and by qRT-PCR analysis of intracellular HCV RNA abundance . Both GLuc activity ( Fig . 3H ) and HCV RNA abundance ( Fig . 3I ) were significantly increased in MSR1-depleted TLR3-expressing cells compared to control cells transduced with shNT , suggesting at least partial release from a TLR3-mediated antiviral response in these cells . Consistent with a role for MSR1 in transporting HCV RNA to the endosome where it can engage TLR3 , HCV RNA was no longer detected in immunoprecipitates of TLR3 prepared from lysates of infected , MSR1-depleted cells ( Fig . 3J , lane 4 vs . lane 2 ) . To exclude the unlikely possibility that these results might in some way reflect an off-target effect of shMSR1 , we reconstituted MSR1 expression in Huh7 . 5-TLR3/shMSR1 cells by ectopic expression of Myc-tagged MSR1 using an expression vector lacking the 5′ untranslated RNA ( UTR ) segment of the endogenous MSR1 mRNA targeted by shMSR1 ( Fig . 4A ) . As we anticipated , poly ( I:C ) stimulation of IFN-β promoter activity was substantially restored by stable expression of Myc-MSR1 in Huh7 . 5-TLR3/shMSR1 cells ( “Myc-MSR1 cells” ) ( Fig . 4B ) . In addition , HCV RNA replication levels were reduced in HCV-infected Myc-MSR1 cells compared to Huh7 . 5-TLR3/shMSR1 cells ( Fig . 4C ) . Co-immunoprecipitation experiments using an anti-Myc antibody also demonstrated an association between Myc-MSR1 and HCV RNA in HCV-infected Myc-MSR1 cells ( Fig . 4D , lane 3 ) . Collectively , the results shown in Figs . 3 and 4 demonstrate that MSR1 plays an essential role in the transport of double-stranded HCV RNA to TLR3 , which engages this ligand in endosomes [4] , [11] , [12] to initiate signaling triggered by HCV infection . The domain architecture of MSR1 ( Fig . 5A ) includes a conserved collagen superfamily domain with approximately 20 Gly-X-Y repeats that are predicted to form a collagen-like , triple-helical structure [34] ( Fig . 5A ) . This domain is required for the binding of acetylated LDL to the bovine homolog of MSR1 [29] , but its role in MSR1 recognition of dsRNA is unknown . We postulated that a series of positively-charged residues located within the carboxy terminus of the collagen superfamily domain , between amino acids ( a . a . ) 325–338 of human MSR1 , could provide for interactions with the negatively-charged sugar-phosphate backbone of dsRNA , and thus be important for MSR1-mediated transport of dsRNA to TLR3 . To test this hypothesis , we constructed a series of Myc-MSR1 expression vectors with deletion of a . a . 321–339 ( Myc-MSR1/Δ321–339 ) , or alanine substitutions at one or more of the following positively-charged residues: Arg325 , Lys332 , Lys335 and Lys338 ( Fig . 5B ) . With the exception of Lys338 , that is Glu in the chimpanzee ( Pan troglodytes ) , a positively-charged side chain is conserved at each of these positions in MSR1 homologs from a wide variety of mammalian species ( Fig . 5A ) . Consistent with an essential role for the carboxy terminus of the collagen superfamily domain in dsRNA trafficking , transient expression of Myc-MSR1/Δ321–339 failed to rescue poly ( I:C ) induction of IFN-β promoter activity in the MSR1-depleted Huh7 . 5-TLR3/shMSR1 cells ( Fig . 5C , left panel ) . While Ala substitutions at Lys332 , Lys335 and Lys338 ( Myc-MSR1/3KA ) or at Arg325 ( Myc-MSr1/R325A ) caused only a modest reduction in the ability of the wt Myc-MSR1 to rescue signaling ( Fig . 5C , center and right panels ) , Ala substitutions at all 4 positions ( Myc-MSR1/R3KA ) resulted in a nearly complete loss of the ability to rescue signaling in Huh7 . 5-TLR3/shMSR1 cells exposed to poly ( I:C ) ( Fig . 5C , right panel ) . The mutants were expressed at high levels ( Fig . 5D ) , and flow cytometry indicated that the Myc-MSR1/R3KA and Myc-MSR1/Δ321-229 mutants traffic to the cell surface ( Fig . 5E ) . Thus , the loss of signaling cannot be explained by the MSR1 mutants being improperly processed , aberrantly degraded , or not transported to the cell surface . The results are consistent with a loss of dsRNA-binding capacity by Myc-MSR1/Δ321–339 and Myc-MSR1/R3KA , and suggest that the positively-charged residues in the MSR1 collagen superfamily domain are involved in dsRNA transport and required for TLR3 signaling . To confirm that these mutations do in fact ablate the ability of MSR1 to bind HCV RNA replication intermediates , we assessed the association of wt Myc-MSR1 , Myc-MSR1/Δ321–339 and Myc-MSR1/R3KA with HCV RNA in co-immunoprecipitation experiments ( Fig . 5F ) . HJ3-5 virus-infected Huh-7 . 5 cells were co-cultured with MSR1-depleted PH5CH8/shMSR1 cells in which stable expression of each of these Myc-MSR1 mutants ( or empty vector ) had been established . Cell lysates were immunoprecipitated with anti-Myc antibody , and RNA extracted from the immunoprecipitates assayed by HCV-specific RT-PCR . The anti-Myc immunoprecipitate prepared from cells expressing wt Myc-MSR1 was significantly enriched in HCV RNA ( Fig . 5F , lane 2 ) compared to immunoprecipitates prepared from cells expressing either the Δ321–339 or R3KA mutants ( lanes 3 and 4 ) , or empty vector ( lane 1 ) . Collectively , these results reveal that conserved positively-charged residues within the carboxy terminus of the collagen superfamily domain of MSR1 are essential for TLR3-mediated responses to dsRNA , and that they contribute to a dsRNA-binding domain in MSR1 . Since infected Huh-7 . 5 cells served as the source of viral RNA bound by Myc-MSR1 expressed within the PH5CH8/shMSR1 cells in the co-culture experiment shown in Fig . 5 , these results suggest that TLR3 , through the dsRNA-scavenging functions of MSR1 , is capable of sensing the presence of HCV infection in adjacent cells . This is an important observation , as previous studies demonstrating TLR3 sensing of HCV infection by hepatocytes [4] have not distinguished between the sensing of replication intermediates produced within the same cell versus those released into the extracellular milieu from neighboring infected cells . To further explore this phenomenon , we generated cell lines that could serve as either infected “inducer” cells or TLR3-competent “sensor” cells in co-culture experiments ( Fig . 6A ) . The inducer cells were Huh-7 . 5 cells infected with HJ3-5/5A-YFP virus , which expresses a fusion of the NS5A protein with YFP [35] , thereby allowing infected cells to be identified by fluorescence microscopy . These inducer cells are not competent for either RIG-I or TLR3 signaling [17] , [26] . The sensor cells were 293FT/IFN-β-mCherry cells , human embryonic kidney cells expressing endogenous TLR3 and the fluorescent reporter protein , mCherry , under the control of the IFN-β promoter . These 293FT/IFN-β-mCherry cells express a low , but readily detectable level of MSR1 transcripts ( Fig . 2B , lower panel ) . Importantly , they are nonpermissive for HCV replication . As expected , co-culture of mock-infected Huh-7 . 5 cells and the 293FT/IFN-β-mCherry cells resulted in no detectable YFP or mCherry signal ( Fig . 6B , left set of panels ) . However , mCherry expression could be induced by the addition of poly ( I:C ) to the medium bathing these co-cultured cells ( Fig . 6B , second set of panels from the left ) . Infection of the co-cultured cells with HJ3-5/5A-YFP virus also resulted in the induction of mCherry in the 293FT/IFN-β-mCherry cells ( Fig . 6B , right panels and merged image ) . The cells expressing mCherry lacked detectable YFP fluorescence , confirming that they were not infected . Similar results were obtained with 293/hTLR3-IFN-β-mCherry cells , which are engineered to overexpress TLR3 , and also express mCherry under control of the IFN-β promoter ( Supplementary Fig . S3 ) . Prior transfection of 293FT/IFN-β-mCherry cells with siRNA targeting TLR3 significantly reduced poly- ( I:C ) -induced IFN-β promoter activity ( Fig . 6C ) and mCherry expression ( Fig . 6D ) , confirming that dsRNA sensing by TLR3 activates the IFN-β promoter ( and mCherry expression ) in these cells . Similarly , siRNA-mediated depletion of TLR3 in 293FT/IFN-β-mCherry cells largely eliminated mCherry expression when the cells were co-cultured with HCV-infected Huh-7 . 5 cells ( Fig . 6E ) . These results indicate that TLR3 is capable of sensing HCV infection in neighboring cells , presumably via MSR1-mediated transport of extracellular dsRNA to the endosome where TLR3 is expressed . In separate experiments , HCV RNA co-immunoprecipitated with Flag-TLR3 when Huh7-TLR3 cells were co-cultured with Huh-7 . 5 cells containing an HCV replicon RNA ( Fig . 6F and G ) , providing additional evidence that HCV RNA produced in adjacent cells may serve as ligand for TLR3 . Consistent with the fact that TLR3 signaling is initiated only in the endosome , not at the cell surface [12] , and more specifically that acidification of the endosome is required for TLR3 signaling in Huh7 . 5-TLR3 cells [4] , treatment of these co-cultured cells with bafilomycin ( 1 nM ) or chloroquine ( 5 µM ) substantially reduced the amount of HCV RNA that co-immunoprecipitated with TLR3 ( Fig . 6G ) . To demonstrate that TLR3 signaling induced within cells adjacent to those infected results in functional antiviral activity , as well as to formally demonstrate a role for MSR1 in this process , we replaced the 293 sensor cells with PH5CH8 cells ( Fig . 7A , left ) . These T antigen-transformed , TLR3-competent human hepatocytes [17] are nonpermissive for HCV infection due in part to a lack of expression of miR-122 ( D . Yamane and S . M . Lemon , unpublished data ) , an essential host factor for HCV replication [36] . We infected Huh-7 . 5 cells with HJ3-5/GLuc2A virus , and 6 hrs later split the culture to create co-cultures of HCV-infected Huh-7 . 5 cells and either MSR1-depleted PH5CH8 cells or PH5CH8 cells transduced with the non-targeting control , shNT ( Fig . 2A ) . HCV replication in the co-cultures was monitored over time by measuring GLuc activity in the culture supernatant fluids ( Fig . 7A , right ) . Our expectation was that TLR3-dependent signaling should be triggered in PH5CH8 cells when placed in co-culture with infected Huh-7 . 5 cells ( that lack both TLR3 and RIG-I-mediated signaling ) [17] , [26] , and that this would result in an antiviral response capable of restricting HCV replication in the Huh-7 . 5 cells through paracrine signaling . This was precisely what we observed . Secreted GLuc activity , which is proportionate to the replication of the reporter virus , was consistently reduced by 50% or more when the infected cells were co-cultured with PH5CH8/shNT versus PH5CH8/shMSR1 cells ( Fig . 7A , right ) . Since some laboratory strains of HCV induce apoptosis in Huh-7 . 5 cells [37] , we determined the proportion of Huh-7 . 5 cells undergoing apoptosis after 4 days of infection with HJ3-5/GLuc2A virus . These studies demonstrated no increase in the proportion of Huh-7 . 5 cells expressing detectable cleaved caspase 3 protein or demonstrating TUNEL fluorescence ( Supplementary Fig . S4 ) . Thus , apoptosis of infected cells is not required for sensing of infection by adjacent , TLR3 competent hepatocytes . To gain further insight into the MSR1-mediated restriction of HCV replication in neighboring cells , we assessed the replication of HJ3-5/GLuc2A virus in Huh-7 . 5 cells that were separated from PH5CH8/shMSR1 ( or PH5CH8/shNT ) cells by a permeable membrane in Transwell culture dishes ( Fig . 7B , left ) . Under these conditions , MSR1 depletion in the TLR3-competent PH5CH8 cells had no impact on the rate of HCV replication in the Huh-7 . 5 cells ( Fig . 7B , right ) . These results indicate a need for close positioning and possibly direct cell-cell contact for MSR1-dependent sensing of infection in neighboring hepatocytes . Despite the restriction imposed on virus replication in Huh-7 . 5 cells by PH5CH8 cells , we were unable to detect IFN-β in media from the direct co-cultures ( Fig . 7A ) using an ELISA with a level of detection of approximately 10 pg/ml . Collectively , these data show that the antiviral response induced by MSR1-dependent recognition of viral RNA produced in neighboring cells is functional and restricts replication in the infected cell , but that this effect is limited in magnitude and highly localized .
The class A scavenger receptors comprise a diverse family of 5 homotrimeric , single-pass type II membrane proteins that bind to and facilitate the cellular import of a broad range of ligands , including acetylated LDL , bacterial cell wall constituents , and both ssRNA and dsRNA [20] , [22] , [32] . While their expression has been considered previously to be restricted to cells of myeloid origin , primarily macrophages ( hence the name , “macrophage scavenger receptor” ) , more recent data suggest that members of this receptor family are expressed more ubiquitously and are present on the surface of a variety of cell types [22] . The various members of the family differ in length , domain architecture , ligand specificity and function , but have been recognized increasingly to play important roles in innate immune signaling . Several class A scavenger receptors , in particular MARCO ( or SCARA2 ) , and SCARA4 ( collectin-12 ) , function in innate immune recognition of bacterial infections , while MSR1 ( SCARA1 , SR-AI , or CD204 ) has been shown recently to contribute to antiviral responses evoked by extracellular dsRNA [reviewed in 22] . Mice with genetic deficiency of the homolog of MSR1 demonstrate increased susceptibility to infection with herpes simplex virus [38] , while MSR1 is required for induction of TLR3-mediated signaling in monocytes exposed to human cytomegalovirus [39] . DeWitte-Orr et al . [22] have recently suggested that the family of class A scavenger receptors represent the major receptors for dsRNA on the surface of fibroblasts , and that they act in a cooperative fashion to deliver dsRNA to both endosomal TLR3 as well as RIG-I-like helicases expressed within the cytoplasm of these cells . The data we present here provide additional support for this general conclusion , but show with greater specificity that MSR1 is the dominant surface receptor for dsRNA in human hepatocyte-derived cell lines . While DeWitte-Orr et al . [22] found that selective knockdown of any one member of the class A scavenger receptor family ( including MSR1 ) had no effect on dsRNA uptake or poly ( I:C ) -stimulated ISG expression , we found that a relatively low efficiency knockdown of MSR1 only profoundly disrupts the ability of extracellular poly ( I:C ) to stimulate IFN-β promoter activity and ISG expression in both Huh7 . 5-TLR3 cells and PH5CH8 cells ( Fig . 3 ) . In all of our studies , these two cell lines behaved similarly . This is important , as Huh-7 cells , from which Huh7 . 5-TLR3 cells are derived , originate from an hepatocellular carcinoma . In contrast , the PH5CH8 cell line was established by transformation of non-neoplastic human hepatocytes with the large T antigen of simian virus 40 [30] . These cells , like primary hepatocytes , express TLR3 endogenously and are stimulated to produce an IFN response when exposed to extracellular poly ( I:C ) [4] . Although hepatocytes express several members of the class A scavenger receptor family ( Fig . 2 ) , the fact that depletion of MSR1 alone disrupts this response suggests that MSR1 is uniquely required for the uptake and transport of extracellular dsRNA so that it may be sensed by TLR3 in these cells . In addition to showing that MSR1 expression is required for TLR3-mediated responses to poly ( I:C ) or infectious challenge with HCV in hepatocyte-derived cells , our data show that MSR1 is physically associated with viral RNA , even when it is produced by ongoing RNA replication in neighboring cells ( Fig . 6F and G ) , and that MSR1 expression is required for TLR3 to bind HCV RNA as ligand ( Fig . 3J ) . We show directly that MSR1 forms a complex with HCV RNA ( Fig . 4D ) , and identify several conserved basic residues within the carboxyl terminus of the collagen superfamily domain that are required for dsRNA uptake by MSR1 and TLR3-mediated signaling in hepatocytes ( Fig . 5 ) . There are three alternatively spliced isoforms of MSR1 in humans , only two of which ( type I and type II ) are expressed on the plasma membrane and facilitate endocytosis of ligands [40] . While the type I isoform is 451 a . a . in length , type II MSR1 is only 358 a . a . However , the amino-terminal 343 residues of these isoforms are identical in sequence , and both isoforms contain the collagen superfamily domain and RNA-binding subdomain we have identified between residues 321–339 . Importantly , although we found a low abundance of isoform II transcripts in Huh-7 . 5 cells ( Fig . 2B ) , only isoform I ( 49 . 7 kDa ) was detected in immunoblots of Huh7 . 5-TLR3 and PH5CH8 cells , and not isoform II ( 39 . 6 kDa ) or III ( 42 . 9 kDA ) . The positively-charged carboxy-terminal region of the collagen superfamily domain is required for the association of MSR1 with acetylated LDL [41] . Its sequence is highly conserved among mammalian species with the exception , interestingly , of the chimpanzee ( Fig . 5A ) . In addition to the 4 positively charged residues ( Arg325 , Lys332 , Lys335 and Lys338 ) present in human MSR1 , this subdomain contains a conserved negatively-charged residue ( Glu337 ) . Previous studies suggest this domain assumes a collagen-like , triple-helical conformation at pH>4 . 5 , stabilized in part by electrostatic interactions of Glu337 with one of the conserved Lys residues [34] . This leaves the remaining unpaired basic residues available for intermolecular interactions with ligands , including association with the negatively-charged sugar-phosphate backbone of dsRNA . MSR1 bound to acetylated LDL is internalized through receptor-mediated endocytosis , dissociating under acidic conditions within the endosome due to the loss of ion pairing between Glu337 and the conserved Lys residues within the collagen-like domain [34] , [42] . We presume that MSR1 functions similarly in the uptake of dsRNA from the extracellular milieu . This may explain why inhibitors of endosomal acidification block TLR3-mediated antiviral responses , as we have shown previously for Huh7 . 5-TLR3 cells [4] , and inhibit the co-immunoprecipitation of HCV RNA with TLR3 in infected Huh7 . 5-TLR3 cells ( Fig . 6D ) . An important observation to emerge from these studies is that hepatocytes are capable of sensing HCV infection in adjacent cells , and that MSR1 mediates this response by acting as a carrier of replication intermediates ( presumably dsRNA ) from the extracellular milieu to endosomally expressed TLR in uninfected cells . While it is often assumed that TLR3 expressed within parenchymal cells such as hepatocytes may sense virus infection in neighboring cells , we demonstrated this formally in co-cultures of HCV-nonpermissive , TLR3-competent cells ( 293FT or PH5CH8 cells ) and infected Huh-7 . 5 cells that are deficient in both TLR3 and RIG-I sensing of HCV infection [17] , [26] ( Figs . 6 and 7 ) . We show that this results in a localized antiviral effect , restricting the replication of virus in the co-cultured cells , and that it is dependent upon MSR1 expression in the uninfected cells since it can be blocked by RNAi-mediated depletion of MSR1 ( Fig . 7B ) . These observations have important implications for the pathogenesis of chronic hepatitis C . For reasons that are unclear , only a small fraction of hepatocytes appear to be infected with HCV in these patients [25] . Two-photon immunofluorescence microscopy of frozen sections of infected human liver tissue has revealed clusters of infected cells , identified either by detection of HCV-specific antigens or dsRNA replication intermediates , typically surrounded by greater numbers of uninfected cells [25] . The presence of these discreet foci of infection suggests that the spread of virus is actively restricted within the liver . The data we present here suggest a model in which TLR3 mediates the establishment of an antiviral state in uninfected cells adjacent to those that are infected in a process that is facilitated by the dsRNA-scavenging actions of MSR1 . Such a model also explains why HCV infection induces ISG expression within the liver , despite its ability to disrupt both RIG-I and TLR3 responses by NS3/4A-mediated cleavage of the RIG-I adaptor molecule , MAVS [15] , [16] , and the TLR3 adaptor molecule , TICAM-1 ( TRIF ) , within infected cells [4] , [18] . TLR3 sensing of HCV infection is not likely to be restricted to neighboring hepatocytes , as we have demonstrated here , but may also occur in tissue-resident macrophages ( Kupffer cells ) or monocyte-macrophages recruited to the site of infection . TLR7 expressed within plasmacytoid dendritic cells ( pDCs ) may also sense infection in other cells [43] . While less robust on a single cell level than in these “professional” innate immune cells , TLR3-mediated antiviral responses in the very large number of parenchymal hepatocytes exposed to HCV may nonetheless make a substantial contribution overall to the induction of intrahepatic ISG responses observed in patients with chronic hepatitis C [24] .
Huh-7 . 5 cells [44] were a gift from Charles Rice ( Rockefeller University , NY ) . Huh-7 . 5 cells engineered to express either TLR3 or the TLR3 mutants ΔTIR , H539E or N541A have been described previously [4] . 293FT cells , human embryonic kidney cells transformed with SV40 T antigen , were purchased from Invitrogen ( Carlsbad , CA ) . 293-hTLR3 cells ( engineered to over-express human TLR3 ) were purchased from InvivoGen . The non-neoplastic T-antigen immortalized hepatocyte cell line PH5CH8 has been described previously [30] , [45] . These cells were cultured in Dulbecco's modified Eagle's medium ( Invitrogen ) supplemented with 10% fetal bovine serum . Blasticidin ( 2 µg/ml ) or G418 ( 0 . 3 mg/ml ) was added for the selection of cells exogenously expressing TLR3-Flag , Myc-MSR1 and related mutants . G418 ( 0 . 3 mg/ml ) was added for the selection of HCV RNA replicon colonies . Two strains of HCV were used in these studies: the genotype 2a JFH-1 virus [46] , and HJ3-5 , a cell culture-adapted genotype 1a/2a chimeric virus containing the structural proteins of the genotype 1a H77 virus placed within the background of JFH-1 virus [47] , [48] . HJ3-5/GLuc2A is a derivative of HJ3-5 containing the Gaussia princeps luciferase ( GLuc ) coding sequence fused to the foot-and-mouth disease virus ( FMDV ) 2A sequence and inserted between p7 and NS2 of HJ3-5 virus [49] . Cells were infected at an m . o . i . of 1 . GLuc activity in supernatants was measured by BioLux Gaussia Luciferase Assay Kit ( New England Biolabs , Ipswich , MA ) using a Synergy2 multi-mode microplate reader ( BioTek , Winooski , VT ) . HJ3-5/5A-YFP is another derivative of HJ3-5 containing yellow fluorescent protein ( YFP ) coding sequence fused to NS5A sequence [35] . ptat2ANeoH77S [27] contains the tat protein , 15 amino acids of the FMDV 2A protein and neomycin phosphotransferase ( NeoR ) downstream of HCV internal ribosome entry site ( IRES ) and the full-length H77S ( genotype 1a ) polyprotein-coding sequence downstream of the encephalomyocarditis virus IRES . pIFN-β-Luc and pPRDII-Luc have been described previously [50] , [51] . pIFN-β-mCherry , which expresses the mCherry fluorescent protein under transcriptional control of the IFN-β promoter , was constructed by replacing the firefly luciferase sequence in pIFN-β-Luc with the mCherry sequence . pJFH1-T3 was constructed by introducing a T3 promoter downstream of the HCV 3′UTR in pJFH1 [46] . pCX4neo/Myc-MSR1 and pCX4bsr/Myc-MSR1 were constructed from the retroviral vectors pCX4neo and pCX4br [52] , which contain the resistance gene for neomycin and blasticidin respectively . A DNA fragment encoding MSR1 ( accession no . NM_138715 ) was amplified from cDNA obtained from Huh-7 cell DNA by PCR using PrimeSTAR HS DNA polymerase ( TaKaRa ) and primers with SphI ( forward ) and the NotI ( reverse ) recognition sites that were designed to enable expression of the MSR1 ORF . The DNA was cloned into the SphI and NotI sites of pCX4neo/Myc and pCX4bsr/Myc , fusing MSR1 sequence to Myc . Mutations within the Myc-MSR1 sequence were subsequently constructed by PCR mutagenesis as previously described [53] . The nucleotide sequences of these vectors were confirmed by DNA sequencing . Cells stably expressing Myc-MSR1 were prepared as previously described [54] . pJFH1-T3 was linearized by either XbaI or EcoRI to provide templates for synthesis of positive- or negative-stranded HCV RNA using T7 or T3 MEGAscript kits ( Ambion , Austin , TX ) . Positive- and negative-stranded HCV RNA products were annealed to produce dsRNA by heating at 70°C for 10 minutes followed by slow cooling to room temperature . The annealed product was assayed for sensitivity to S1 nuclease ( Promega , Madison , WI ) to confirm that it was double-stranded . High molecular weight ( HMW ) poly ( I:C ) was purchased from Invivogen ( San Diego , CA ) . Cells were exposed to a concentration of 50 µg/ml for 6 hrs unless otherwise stated . Fluorescein-labeled HMW poly ( I:C ) ( Invivogen ) was used to monitor dsRNA uptake by cells . Cells were mock-exposed or exposed to 10 µg/ml fluorescein-labeled poly ( I:C ) for 8 , 16 or 24 hrs , then harvested by trypsinization , washed twice in phosphate buffered saline ( PBS ) and fixed for 15 minutes in 4% paraformaldehyde . After additional washing in 1× PBS , the fluorescence intensity of cell populations was analyzed using a Beckman Coulter ( Dako ) CyAn flow cytometer . IFN-β and NF-κB-dependent promoter activities were assayed using firefly luciferase reporters , pIFN-β-Luc or pPRDII-Luc , with the reporter plasmid pRL-CMV used as an internal control for transfection efficiency as previously described [55] . A Turner Designs Luminometer Model TD-20/20 ( Promega , Madison , WI ) was used to measure luciferase activity . Data shown represent means ± s . d . from three independent transfection experiments . Preparation of cell lysates and SDS-PAGE were carried out as previously described [56] . Total protein was transferred to Immobilon-psq PVDF membranes ( Millipore , Billerica , MA ) using a Trans-blot SD semi-dry transfer cell ( Bio-Rad , Hercules , CA ) . Primary antibodies included anti-Flag ( M2; Sigma , St Louis , MO ) , anti-Myc ( 9B11; Cell Signaling , Danvers , MA ) , anti-ISG15 ( H-150; Santa Cruz Biotechnology Inc . , Santa Cruz , CA ) , anti-MSR1 ( H-190; Santa Cruz Biotechnology Inc . ) , and anti-β-actin antibody ( AC-15; Sigma ) . Secondary antibodies were IRDye-conjugated anti-mouse IgG and anti-rabbit IgG ( LI-COR Biosciences , Lincoln , NE ) . Immunocomplexes were detected with an Odyssey infrared imaging system ( LI-COR Biosciences ) . Total cellular RNA was isolated using the RNeasy mini kit ( Qiagen , Valencia , CA ) . The iScript one-step RT-PCR kit with SYBR Green and CFX96 real-time system ( Bio-Rad ) were used to quantify the abundance of IFN-β , ISG56 , GAPDH mRNA or HCV RNA . We used the following forward and reverse primer sets: IFN-β , 5′-GTGCCTGGACCATAGTCAGAGTGG-3′ ( forward ) , 5′-TGTCCAGTCCCAGAGGCACAGG-3′ ( reverse ) ; ISG56 , AAGCTTGAGCCTCCTTGGGTTCGT-3′ ( forward ) , 5′-TCAAAGTCAGCAGCCAGTCTCAGG-3′ ( reverse ) ; GAPDH [53] , HCV , 5′-CATGGCGTTAGTATGAGTGTCGT-3′ ( forward ) , 5′-CCCTATCAGGCAGTACCACAA-3′ ( reverse ) . IFN-β , ISG56 and HCV RNAs were normalized to GAPDH mRNA . Results shown represent means ± s . d . from three independent experiments . Total cell lysates were prepared using lysis buffer ( PBS containing 0 . 2% Triton X-100 , RNase inhibitor and protease inhibitor cocktail ) , followed by immunoprecipitation with anti-Flag or anti-Myc antibodies using protein G sepharose ( GE healthcare ) . RNAs were extracted from the immunoprecipitates using Trizol ( Invitrogen ) , and assayed for HCV RNA by RT-PCR using the Superscript III One-step RT-PCR system ( Invitrogen ) followed by agarose gel electrophoresis . Short hairpin RNA ( shRNA ) targeting MSR1 ( shMSR1 , 5′-GCATTGATGAGAGTGCTATTG-3′ ) or non-targeting control shRNA ( Sigma; Mission shRNA SHC-002 ) were introduced into Huh7 . 5-TLR3 or PH5CH8 cells by lentiviral transfer . MSR1-depleted Huh7 . 5-TLR3/shMSR1 and PH5CH8/shMSR1 and related control cells , Huh7 . 5-TLR3/shNT or PH5CH8/shNT cells , were selected by addition of puromycin ( 5 µg/ml ) to the cell culture medium . MSR1 expression was reconstituted in MSR1-depleted cells by retroviral transfer of the Myc-MSR1 sequence in pCX4neo Myc-MSR1 , which lacks the shMSR1 target sequence within the 5′UTR of MSR1 mRNA [57] . Cells stably expressing Myc-MSR1 were selected by growth in G418 ( 0 . 3 mg/ml ) . For analysis of the RNA-binding domain in MSR1 , pCX4neoMyc-MSR1 was subjected to PCR-based mutagenesis using standard methods , with the sequence of the manipulated regions of the plasmid confirmed by DNA sequencing . Cell surface expression of MSR1 and related mutants was analyzed by flow cytometry . MSR1 has a transmembrane domain between aa 51–73 , with its carboxyl terminus exposed to the extracellular environment . For detection of MSR1 on the cell surface , non-permeabilized cells were fixed with 2% paraformaldehyde followed by incubation with anti-MSR1 antibody ( Santacruz , H-190 ) for 1 h at room temperature . Cells were washed three times with PBS , and incubated with R-phycoerythrin-conjugated anti-rabbit IgG secondary antibody ( Jackson ImmunoResearch ) for 30 min at room temperature . Fluorescent intensity of cells was determined using a FACScan ( Becton Dickinson ) flow cytometer . Huh-7 . 5 cells were infected with HJ3-5/GLuc2A virus at an m . o . i . of 0 . 03 , or mock-infected , and cultured for 4 days . As a positive control , cells were treated with 1 µM staurosporine for 6 hrs . Cells were harvested by trypsinization , washed twice in PBS and fixed in 4% paraformaldehyde , then stained for cleaved caspase 3 and HCV core protein as described previously [37] . DNA fragmentation was analyzed by terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end-labeling ( TUNEL ) system ( Promega , Madison , WI ) . Positive cells were quantified by flow cytometry as described previously [37] . Statistical comparisons were carried out using Student's T test unless otherwise noted . Calculations were made with Excel 2008 for Mac ( Microsoft ) or Prism V for Mac OS X ( GraphPad Software ) . | Persistent hepatitis C virus ( HCV ) infection is an important cause of fatal cirrhosis and liver cancer in humans . While viral disruption of interferon ( IFN ) signaling pathways may contribute to the persistence of HCV , IFN-stimulated gene ( ISG ) expression is often prominent within the infected liver . We show here that this is due , at least in part , to Toll-like receptor 3 sensing of HCV mediated by class A scavenger receptor type 1 ( MSR1 ) -dependent endocytosis and transport of extracellular viral double-stranded RNA ( dsRNA ) allowing it to be engaged by TLR3 in the late endosome . TLR3 expressed within uninfected cells is capable of sensing HCV infection in neighboring infected cells in a process that is dependent upon the dsRNA-scavenging activity of MSR1 , resulting in the induction of a localized functional antiviral response . This contributes to the ISG expression that typifies the chronically-infected liver , as it occurs within cells that do not express HCV proteins that disrupt IFN signaling . TLR3 signaling thus limits the spread of virus within the liver , potentially explaining why only a small fraction of hepatocytes are infected with HCV in vivo . | [
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"diseases"
] | 2013 | Class A Scavenger Receptor 1 (MSR1) Restricts Hepatitis C Virus Replication by Mediating Toll-like Receptor 3 Recognition of Viral RNAs Produced in Neighboring Cells |
Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks . Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes . In four independently acquired data sets with repeated scans ( total N = 213 ) , we show that the local connectome fingerprint is highly specific to an individual , allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17 , 398 identification tests . The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months . The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time , whereas this change was not observed in the diffusivity measures . Moreover , the local connectome fingerprint can be used as a phenotypic marker , revealing 12 . 51% similarity between monozygotic twins , 5 . 14% between dizygotic twins , and 4 . 51% between none-twin siblings , relative to differences between unrelated subjects . This novel approach opens a new door for probing the influence of pathological , genetic , social , or environmental factors on the unique configuration of the human connectome .
The specific brain characteristics that define an individual are encoded by the unique pattern of connections between the billions of neurons in the brain [1] . This complex wiring system , termed the connectome [2 , 3] , reflects the specific architecture of region-to-region connectivity [4] that supports nearly all complex brain functions . Yet to date , quantifying the difference between connectomes of two or more individuals remains a major challenge , as it requires a reliable characterization of white matter architecture that is also sensitive to microscopic variability . To this end , studies have used diffusion MRI ( dMRI ) to measure the architecture of white matter pathways using the diffusion properties of water molecules[5 , 6] . This allows for the mapping of white matter trajectories in the human brain and defining the graph structure of region-to-region connectivity [7 , 8]; however , while the reliability of diffusion MRI scans has improved substantially by new acquisition approaches [9 , 10] , the efficiency and accuracy of tractography approaches have recently come into question [11 , 12] . Thus , instead of mapping region-to-region connectivity , the concept of the local connectome was proposed as an alternative measure to overcome the limitations of diffusion MRI fiber tracking [11–13] . The local connectome is defined as the degree of connectivity between adjacent voxels within a white matter fascicle measured by the density of the diffusing water . A collection of these density measurements provides a high dimensional feature vector that can describe the unique configuration of the structural connectome within an individual , providing a novel approach for comparing differences and similarities between individuals as pairwise distances . In this study , we used this local connectome feature vector as a fingerprint to quantify similarities and difference between two white matter architectures . To evaluate the performance of our approach , we used four independently collected dMRI datasets ( n = 11 , 25 , 60 , 118 , see Methods ) with repeat scans at different time intervals ( ranging from the same day to a year ) to examine whether local connectome fingerprints can reliably distinguish the difference between within-subject and between-subject scans . We then examined whether the local connectome fingerprint is a unique identifier of an individual person by testing whether the fingerprint could determine if two samples came from the same person or different individuals . This uniqueness was compared with fingerprints derived from fractional anisotropy ( FA ) [14] , diffusivities , and conventional region-to-region connectivity methods . Follow-up analysis revealed how local connectome fingerprints can quantify the similarity between genetically related individuals as well as measure longitudinal changes within an individual .
We first illustrate how the local connectome fingerprint uses the density of diffusing spins to characterize white matter architecture within an individual . Fig 1A shows the spin distribution functions ( SDFs ) [15] estimated from dMRI scans at the mid-sagittal section of the corpus callosum . SDF measures the density of water diffusing at any orientation within a voxel and the SDF magnitude at the fiber directions can quantify the connectivity of local connectome ( see Methods ) . An example of the local connectome quantified at the corpus callosum is illustrated for three subjects in Fig 1B . Here the anterior and posterior portion of corpus callosum exhibit substantial diversity between these three subjects . A repeat scan several months later reveals a qualitative within-subject consistency . This high individuality appears to be specific to diffusion density estimates . Conventional FA measures calculated from diffusivity do not yield this qualitative between-subject diversity ( Fig 1C ) . To sample the local density measurements across all major white matter pathways , dMRI data was reconstructed into a standard space , and the fiber directions of a common atlas were used to sample an SDF value for each fiber direction ( see Methods and Fig 2A ) . This approach yielded , for each dMRI scan , a local connectome fingerprint consisting of a high-dimensional feature vector with a total of 513 , 316 density estimates ( Fig 2B ) . Fig 2C shows the fingerprints of the same three subjects in Fig 1B and the fingerprints from their repeat scans . Consistent with the qualitative measurements in Fig 1B , each local connectome fingerprint in Fig 2C shows , at a coarse level , a highly similar pattern for within-subject scans and also high variability across subjects , suggesting that the local connectome fingerprint may exhibit the unique features of the white matter architecture . To quantify how well the local connectome fingerprint captures between-subject difference , we used four independently collected dMRI datasets ( n = 11 , 24 , 60 , 118 ) with repeat scans for a subset of the subjects ( n = 11×3 , 24×2 , 14×2 , 44×2 , respectively ) . The Euclidian difference ( i . e . , root-mean-squared error ) was used as a single difference estimate between any two fingerprints . For each dataset , we computed within-subject differences ( n = 33 , 24 , 14 , 44 , respectively ) and between-subject differences ( n = 495 , 1104 , 2687 , 12997 , respectively ) . Fig 3 shows the within-subject and between-subject differences of the four datasets . All four datasets show a clear separation between the within-subject and between-subject difference distributions , with no single within-subject pair as large as any of the between-subject pairs . We used d-prime [16] to quantify the separation of between-subject and within-subject differences . The results showed d-prime values of 14 . 84 , 12 . 80 , 7 . 21 , and 8 . 12 , for dataset I , II , III , and IV respectively , suggesting a very high degree of separation between the two distributions . In order to understand what regions of the local connectome may be driving this within-subject uniqueness , we looked at the spatial distribution of both between-subject and within-subject differences ( Fig 4 ) . The absolute difference was averaged for each fingerprint entry to map its spatial distribution . Each voxel can have multiple local connectome fingerprint measurements . For visualization purposes we only calculated the difference of the first resolved fiber ( defined by the atlas ) . The first row of Fig 4 shows between-subject differences for datasets I , II , III , and IV . The largest between-subject differences are found in core white matter structures such as the corpus callosum and central semiovale . The corpus callosum is known to have commissural fibers connecting the cortical hemisphere , whereas the central semiovale has association pathways connecting frontal , parietal , and occipital regions as well as projection pathways connecting cerebral cortex and brainstem . The large differences found in these two regions suggest that the between-subject differences could be driven by a variety of different brain connections . The second row of Fig 4 shows within-subject differences for datasets I , II , III , and IV . Dataset I was acquired with the shortest time interval between repeat scans ( less than 16 days ) , whereas dataset II ( 1~3 months ) , dataset III ( 6 months ) and dataset IV ( a year ) were acquired with longer time intervals . As shown in Fig 4 , the within-subject differences are substantially lower than the between-subject differences , suggesting high uniqueness of the local connectome fingerprint to an individual . Substantial increase of within-subject differences could be observed in the corpus callosum at datasets with a longer time interval , suggesting the possibility of neuroplasticity over time . This within-subject consistency suggests that the local connectome fingerprint could be used as a unique subject identifier . To assess this , we used a linear discriminant analysis ( LDA ) classifier [17] to determine whether two fingerprints were from the same individual using only the Euclidian distance between fingerprints as a single classification feature . For each dataset , the classification error was estimated using leave-one-out cross-validation . We did not observe a single misclassification out of the 17 , 398 cross-validation rounds from four datasets ( 17 , 283 different-subject and 115 same-subject pairings ) . To approximate the classification error , we modeled the distributions of within-subject and between-subject differences by the generalized extreme value distribution [18] , a continuous probabilistic function often used to assess the probability of extreme values ( smallest or largest ) appearing in independent identically distributed random samples ( last row of Fig 3B ) . The classification error was quantified by the probability of a within-subject difference greater than a between-subject difference . Our analysis showed that the classification error was 4 . 25×10−6 for dataset I , 9 . 97×10−7 for dataset II , 5 . 3×10−3 for dataset III , and 5 . 5×10−3 for dataset IV . The larger error in dataset III and IV could be due to their longer scan interval ( 6 months and one year ) . For repeat scans acquired within 3 months , the probability of mistaking two samples of the same subject’s local connectome fingerprint as coming from two different individuals was low enough to consider the local connectome fingerprint a highly reliable measure of individual subject uniqueness . Since gross anatomical patterns such as gyral and sulcal folding can be highly specific to an individual , it is possible that the unique features we observed in the local connectome fingerprint reflected an artifact of the spatial normalization process . To evaluate this , we retested our uniqueness within a restricted white matter mask that only covered the median sagittal sections of the corpus callosum defined by the Johns Hopkins University white matter atlas [19] . This “corpus callosum fingerprint” should be free from all possible contributions of anatomical geometry such as gyral and sulcal folding . We applied the same analysis procedures to the corpus callosum fingerprint to examine whether it can reveal unique patterns specific to individuals within this area . The result showed d-prime values of 5 . 97 , 5 . 85 , 3 . 78 , and 4 . 08 , for dataset I , II , III , and IV , respectively . The leave-one-out cross-validation analysis showed that classification error was 0% , 0 . 089% , 1 . 26% , and 0 . 63% , for dataset I , II , III , and IV , respectively . The classification error modeled by the generalized extreme value distribution was 9 . 13×10−4 , 5 . 6×10−3 , 6 . 9×10−3 , and 7 . 2×10−3 , for dataset I , II , III , and IV , respectively . The corpus callosum fingerprint itself already achieved more than 99% accuracy in subject identification . This suggests that the high individuality of the local connectome fingerprint is due to the microscopic characteristics of the white matter architecture . Diffusivity-based metrics , such as FA , axial diffusivity ( AD ) , and radial diffusivity ( RD ) , also reveal microscopic structure of white matter systems . To compare these measures against SDF , we used the same analysis and replaced the SDF-based measures with FA , AD , and RD values of the corresponding voxels . Our analysis showed that the d-prime values of the FA-based fingerprint were 4 . 84 , 4 . 70 , 4 . 56 , and 3 . 60 , for dataset I , II , III , and IV , respectively . All values were substantially smaller than the local connectome fingerprint . The leave-one-out cross-validation analysis showed that classification error of the FA-based fingerprint was 0% , 0 . 18% , 0 . 22% , and 0 . 87% . While FA-based fingerprints also have high uniqueness with classification error less than 1% , the performance is not superior to the 0% leave-one-out cross-validation error achieved by the local connectome fingerprint We also analyzed the performance of AD-based fingerprints , producing d-prime values of 4 . 20 , 4 . 07 , 4 . 33 , and 3 . 68 , for datasets I , II , III , and IV , respectively . The performance was similar to FA-based fingerprints and substantially lower than those of the local connectome fingerprint . The leave-one-out cross-validation analysis showed a classification error of 0 . 15% in dataset IV . While no misclassification was found in dataset I , II , III , the generalized extreme value distribution showed a classification error of 0 . 18% , 0 . 29% , and 0 . 18% , respectively . The AD-based fingerprint was also inferior to the local connectome fingerprint . The analysis on RD showed a slightly different result . The d-prime values for RD were 7 . 87 , 9 . 10 , 8 . 79 , and 5 . 80 , which were substantially better than FA-based and AD-based fingerprints . Compared with the local connectome fingerprint , it is noteworthy that the local connectome fingerprint substantially outperformed RD for repeat scans within 3 months ( 14 . 84 and 12 . 80 versus 7 . 87 and 9 . 10 ) , but not for repeat scans with a longer time interval ( 7 . 21 , and 8 . 12 versus 8 . 79 , and 5 . 80 ) . The classification error also showed a similar pattern . In dataset I , which had the shortest time interval ( less than 16 days ) , the classification errors were 4 . 25×10−6 for the local connectome fingerprint and 0 . 28% for the RD-based fingerprint . By contrast , in dataset IV , which had the longest time interval ( around a year ) , the classification errors were 5 . 5×10−3 for the local connectome fingerprint and 3 . 1×10−3 for the RD-based fingerprint . The uniqueness of the local connectome fingerprint dropped substantially over time . The possible causes may include biological causes , such as neuroplasticity , or more systematic error , such as any change in data acquisition due to software updates or scanner resonance changes . This issue was further investigated in the “Neuroplasticity revealed by the local connectome fingerprint” section below . To summarize , compared with diffusivity-based fingerprints , the local connectome fingerprints exhibited the greatest reliability for repeat scans acquired within 3 months . We further compared the local connectome fingerprint with region-to-region connectivity estimates from diffusion MRI fiber tracking . The same analysis pipeline used for the local connectome fingerprint was used to calculate leave-one-out cross-validation error for the traditional connectivity matrix . The d-prime values for the region-to-region connectivity matrices in dataset I , II , III , and IV were at 3 . 44 , 2 . 06 , 2 . 41 , and 2 . 25 , respectively . The classification error for datasets I , II , III , and IV were 3 . 6% , 13 . 65% , 11 . 81% , and 9 . 48% , respectively ( estimated by leave-one-out cross validation ) . While the classification accuracy for the traditional connectivity matrices is still quite high and similar to what has previously been observed in resting state functional connectivity estimates [20] , it is clear from these results that the greatest reliability at characterizing connectomic uniqueness comes from local connectome measures . Our analysis of within-subject differences hinted at the possibility of changes in the local connectome over time , and thus we further examined how time impacts the uniqueness of local connectome fingerprints . If the local connectome fingerprint is sensitive to neuroplasticity , a longer interval should result in decreased similarity between repeat scans of the same individual . To test this , we calculated the similarity of within- subject local connectome fingerprints as a percentage of the mean between-subject difference ( see Methods ) . A similarity of 100% indicates that two fingerprints are identical , whereas a similarity of 0% indicates the magnitude of the differences between two fingerprints is the same as those between unrelated subjects . For this analysis , we calculated the similarity between repeat scans in dataset II ( n = 24 ) , which was acquired with the widest range of time interval between repeat scans ( 1~3 months ) . Fig 5A shows the scatter plot of the similarity against the time . A nonparametric , rank-based test ( the Mann-Kendall test ) showed a significant decreasing trend in the similarity over time ( p = 0 . 0023 ) . To further quantify the change of similarity in the local connectome fingerprint , we used linear regression to calculate the coefficient ( slope ) between the time interval and similarity . The results showed that the similarity dropped at a rate of 12 . 79% per 100 days . It is noteworthy that the identical analysis was applied to FA-based , AD-based , and RD-based fingerprints but none showed a significant trend ( p = 0 . 3092 , 0 . 4130 , and 0 . 0702 , respectively ) . To further illustrate how the local connectome fingerprint revealed neuroplasticity within individuals , we selected one subject in dataset IV that exhibited the greatest difference across time and visualized the spatial mapping of the local connectome fingerprints between repeat scans . This spatial mapping is shown in Fig 5B , whereas the FA map calculated from the same data are shown in Fig 5C . The upper row shows the midsagittal view at the corpus callosum , whereas the lower row shows an axial view at the splenium and genu of the corpus callosum . Each voxel may have multiple local connectome fingerprint measurements ( e . g . at the crossing fiber region ) , and for visualization purposes , only the one associated with the first resolved fiber ( defined by the atlas ) was calculated . All images are scaled by their maximum values to provide a fair comparison . Fig 5B shows substantial differences in several core white matter bundles between the repeat scan ( annotated ) , whereas Fig 5C shows no obvious difference . Since artifacts such as signal drift , coil degradation , and motion affect large regions of tissue spanning several centimeters , the fact that the differences in the local connectome fingerprint were observed in specific white matter bundles suggests that the finding is unlikely due to an artifact . The local connectome fingerprint opens the possibility for comparing not only differences but also the similarities between individuals . To further illustrate how the local connectome fingerprint can be used to quantify white matter architecture as a phenotypic marker , we used a publicly available dMRI dataset of 486 subjects from Human Connectome Project ( 2014 , Q3 release ) , including 49 pairs of monozygotic ( MZ ) twins , 43 pairs of dizygotic twins ( DZ ) twins , and 96 pairs of non-twin siblings . While the local connectome fingerprints of MZ twins show generally similar patterns at the coarse level ( Fig 6 ) , there are also substantial individual differences between the twins that can be observed along the fingerprints . Consistent with these qualitative comparisons , we found that MZ twins have smaller differences between fingerprints , followed by DZ twins , siblings , and unrelated subjects ( Fig 7A ) . It is noteworthy that all difference distributions have large overlapping regions ( Fig 7B ) , indicating that the difference between twins or siblings may often fall within the distribution of differences from genetically-unrelated subjects . We further compared the similarity between twins and siblings . On average , MZ twins have a similarity index of 12 . 51±1 . 09% , whereas similarity for DZ twins and siblings is 5 . 14±1 . 34% and 4 . 47±0 . 59% , respectively ( Fig 7C ) . The difference in similarity index was significant across MZ twins , DZ twins , non-twin siblings , and other genetically-unrelated subjects ( Kruskal-Wallis test , χ2[3 , 22895] = 165 . 43 , p < 0 . 0001 ) . Post-hoc comparisons using Scheffé's S procedure showed ( 1 ) significantly higher similarity in MZ twins compared with all other groups ( all p < 0 . 001 ) , ( 2 ) significantly higher similarity in DZ twins compared with unrelated subjects ( p = 0 . 001 ) , and ( 3 ) significantly higher similarity in non-twin siblings compared with unrelated subjects ( p = 0 . 0146 ) . There was no significant difference between DZ twins and non-twin siblings ( p = 0 . 9989 ) . To address the concern of data dependency , we conducted additional permutation tests to examine the difference in similarity across twins and unrelated subjects . A total of 10 , 000 permutations were calculated , and all χ2 statistics calculated from the Kruskal-Wallis test were smaller than the nonpermuted case ( p< 0 . 0001 ) . Thus the difference across groups was highly significant . Further permutation tests between pairs of subject groups were also conducted . The results showed ( 1 ) significantly higher similarity in MZ twins compared with DZ twins ( p < 0 . 0001 ) , non-twin siblings ( p < 0 . 0001 ) , and unrelated subjects ( p < 0 . 0001 ) , ( 2 ) significantly higher similarity in DZ twins compared with unrelated subjects ( p < 0 . 0001 ) , and ( 3 ) significantly higher similarity in non-twin siblings compared with unrelated subjects ( p < 0 . 0001 ) . There was no significant difference between DZ twins and non-twin siblings ( p = 0 . 2697 ) . This result is consistent with MZ twin sharing a higher genetic similarity , whereas DZ twins exhibit a similar genetic similarity on par with non-twin siblings .
Local white matter architecture is so unique and highly conserved within an individual that it can be considered a unique neural phenotype . Here we show that this phenotype can be quantified by measuring the density of microscopic water diffusion along major white matter fascicles and producing a high dimensional vector that can be used to compute the distance between two structural connectomes , i . e . , a local connectome fingerprint . The distance between two local connectome fingerprints reflects a low dimensional representation of both similarities and differences in whole-brain white matter pathways . Our analysis showed how the local connectome fingerprint exhibited unprecedentedly high between-subject distance , while generally low within-subject distances , allowing for it to be used as a reliable measure of the specific connective architecture of individual brains . This property paves the way for using the local connectome as a phenotypic marker of the structural connectome . The concept of local connectome is both conceptually and methodologically different from conventional connectomic measures . While most studies have emphasized on region-to-region connectivity [3] and ignored the rich information in the local white matter architecture , the local connectome reveals the connectivity at the voxel level and characterizes local white matter architecture to provide high dimensional data that may complement the region-to-region connectivity [13] . This local connectome mindset considers the fact that the difference between brain structures may be localized and thus may not be readily identified in the global connectomic pattern . We have previously shown that the local connectome can be used to localize the change of white matter structure due to physiological difference such as body mass index [13] . While any high dimensional representation of the human brain in a standard space has the potential to be used as fingerprint , we showed that the uniqueness of fingerprints generated from the local connectome was substantially higher than what was observed in diffusivity-based fingerprints as well as fingerprints derived from region-to-region connectivity reported by either dMRI or fMRI , as typically done in human connectomic studies [2 , 20 , 21] . For example , the region-to-region structural connectivity achieved a classification accuracy around 90~97% . This is very close to the accuracy of its functional counterpart [20] , that was recently reported to have an accuracy of 92–94% in whole brain identification and 98–99% in frontoparietal network . Although both region-to-region connectivity approaches have accuracy greater than 90% , the performance remains substantially lower than the perfect classification in 17 , 398 leave-one-out rounds and an estimated error of 10−6 achieved by local connectome fingerprint . At first glance , it may seem possible that the high degree of uniqueness exhibited by the local connectome fingerprint could be due to variability in the spatial normalization process between individuals driven by the unique gyral or sulcal folding patterns in gray matter . While we still cannot rule out the effect of misalignment , our comparison with the FA-based fingerprints showed that the spatial normalization process does not fully contribute to the uniqueness observed in the local connectome fingerprint . Both FA-based fingerprints and the local connectome fingerprints used an identical spatial normalization mapping process , but the FA-based fingerprints had a much higher error rate in leave-one-out cross-validation ( e . g . 0 . 87% for dataset IV ) than the zero cross-validation error achieved by the local connectome fingerprint . Obviously , a substantial portion of the uniqueness was due to the microstructural white matter characteristics quantified in the SDF . Moreover , we observed favorable characterization of white matter uniqueness even when our analysis was restricted to a small portion of white matter with minimal influence of sulcal and gyral folding ( i . e . , the mid corpus callosum ) . These two findings support our claim that the local connectome fingerprint can reveal the unique characteristics of the white matter architecture . Finally , the between-subject differences are mostly located within the deep white matter at the central semiovale and the corpus callosum . This spatial specificity suggests that the uniqueness of the local connectome fingerprint is mostly driven by mesoscopic or microscopic architectural properties , not due to an artifact of unique folding geometry or the spatial normalization process . It is important to point out that the local connectome fingerprint is based on a physical measurement that is different from diffusivity-based metrics such as FA , AD , and RD . To further compare their physical meanings , diffusivity quantifies how fast water diffuses in tissue [22] and is sensitive to the structural integrity of the underlying fiber bundles [14] , such as axonal loss and demyelination [23–26] . This may explain why the FA map appears similar across the normal population in which the axons have normal structure . By contrast , SDF quantifies how much water diffuses along the fiber pathways [15 , 27] and is sensitive to density characteristics of white matter such as the compactness of the fiber bundles [15 , 28 , 29] . As illustrated in our qualitative analysis ( Fig 1C ) , while the density characteristics vary substantially among normal populations , the FA measurements do not show obvious differences between subjects . This highlights how the local connectome fingerprint achieved a higher uniqueness profile than diffusivity-based metrics when they were used to characterize microstructural white matter patterns that reflect individuality . The results led us to hypothesize that the local connectome fingerprints may be more sensitive to axonal density or different levels of myelination that are unique to individuals . Future histology studies are needed to confirm this hypothesis . The high degree of uniqueness in the local connectome within an individual can be used to reflect a quantifiable phenotype of neural organization . As illustrated in our analysis of twins , the similarity in monozygotic twins was around twice as much of the dizygotic twins , whereas our post-hoc analysis did not find significant similarity difference between dizygotic twins and non-twins siblings . These results are highly suggestive that genetics contribute a substantial portion to the overall construction of the local connectome , which is consistent with previous studies showing high heritability in cortical connections [30 , 31] and white matter integrity [32–35] . Nevertheless , our results also showed that the monozygotic twins shared only 12 . 51% similarity in local white matter architecture . This indicates that a high heritability may not necessarily imply that most of the differences or similarity observed in phenotypes are due to genetic factors [36] . A considerable portion of the individuality in local connectome is likely driven by environmental factors such as life experience and learning . Thus monozygotic twins still exhibited high individuality in their connectome . In fact , our findings showed that the local connectome fingerprint is highly plastic over time , presented by a significant decreasing trend in the self-similarity caused by either an increase or decrease in the local connectome fingerprint measurements . This decreasing trend in the self-similarity raises many questions about which factors ( genomic , social , environmental , or pathological ) sculpt the local white matter systems . Of course , white matter integrity also varies with normative development [37–39] , a portion of which may be determined genetically . This warrants more longitudinal and genetic analysis to identify specific contributions of genetic and environmental factors on the uniqueness of connectomic structure , with an aim to understand how those factors interact with abnormal brain circuits in neurological and psychiatric disorders . It is important to point out that the highest similarity between repeat scans was around 70~80% in our study . This indicates that 20–30% of variability in the local connectome may arise from artifacts that decrease signal-to-noise ratio , such as cardiovascular and respiratory artifacts or computation error . This number reflects the limit of the local connectome fingerprint in detecting an anomaly in the individuals as well as finding differences in a group study . For example , we could not accurately identify whether two scans were from a twin pair because the similarity between twins was only around 12 . 51% . However , if a disease causes a white matter change with more than 30% difference in similarity , the local connectome fingerprint may be able to detect it . In a group study , increasing the number of subjects can average out the effect of noise and error on the similarity , allowing us to find a group difference that is substantially small . The similarity index from repeat scans allows us to gauge the strength and limitation of the local connectome fingerprint and prospectively , to develop a strategy to improve its performance .
The first dataset included a total of 11 subjects ( 9 males and 2 females , age 20~42 ) . Each subject had three diffusion MRI scans within 16 days on a Siemens Trio 3T system at the University of California , Santa Barbara . All methods were approved by the local institutional review board at the University of California , Santa Barbara . The diffusion MRI was acquired using a twice-refocused spin-echo EPI sequence . A 257-direction full-sphere grid sampling scheme was used . The maximum b-value was 5000 s/mm2 . TR = 9916 ms , TE = 157 ms , voxel size = 2 . 4×2 . 4×2 . 4 mm , FoV = 231×231 mm . The second set of data included a total of 24 subjects ( 8 males and 16 females , age 22 ~ 74 ) . All participants were scanned on a Siemens Tim Trio 3T system at National Taiwan University , and all subjects had their second scan at 1~3 months . All methods were approved by the local institutional review board at National Taiwan University . The diffusion MRI was also acquired using a twice-refocused spin-echo EPI sequence . The diffusion scheme is a 101-direction half-sphere grid sampling scheme with b-max = 4000 s/mm2 ( b-table available at http://dsi-studio . labsolver . org ) . TR = 9600 ms , TE = 130 ms , voxel size = 2 . 5×2 . 5×2 . 5 mm . The third set of data included a total of 60 subjects ( 30 males and 30 females , age 18 ~ 46 ) . All participants were scanned on a Siemens Verio 3T system at Carnegie Mellon University , and 14 of the 60 subjects had their second scan at 6 months . All methods were approved by the local institutional review board at Carnegie Mellon University . The diffusion MRI was also acquired using a twice-refocused spin-echo EPI sequence . A 257-direction full-sphere grid sampling scheme was used . The maximum b-value was 5000 s/mm2 . TR = 9916 ms , TE = 157 ms , voxel size = 2 . 4×2 . 4×2 . 4 mm , FoV = 231×231 mm . The fourth set of diffusion data included a total of 118 subjects ( 91 males and 27 females , age 22 ~ 55 ) that were also scanned on a Siemens Verio 3T system at the Carnegie Mellon University . All methods were approved by the local institutional review board at the University of Pittsburgh and Carnegie Mellon University . 44 of them had another scan after one year . The diffusion images were acquired on a Siemens Verio scanner using a 2D EPI diffusion sequence . TE = 96 ms , and TR = 11100 ms . A total of 50 diffusion sampling directions were acquired . The b-value was 2000 s/mm2 . The in-plane resolution was 2 . 4 mm . The slice thickness was 2 . 4 mm . The fifth dataset was from the Human Connectome Projects ( Q3 , 2014 ) acquired by Washington University in Saint Louis and University of Minnesota . The diffusion MRI data were acquired on a Siemens 3T Skyra scanner using a 2D spin-echo single-shot multiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse . A total of 486 subjects ( 195 males and 291 females , age 22 ~ 36 ) received diffusion scans . The spatial resolution was 1 . 25 mm isotropic . TR = 5500 ms , TE = 89 . 50 ms . The b-values were 1000 , 2000 , and 3000 s/mm2 . The total number of diffusion sampling directions was 90 , 90 , and 90 for each of the shells in addition to 6 b0 images . The total scanning time was approximately 55 minutes . The scan data included 49 pairs of monozygotic twin , 43 pairs of dizygotic twins , and 96 pairs of siblings . We used the pre-processed data provided by the consortium in our analysis . Carnegie Mellon University Institutional Review Board ( IRB ) reviewed the research protocol for the data analysis in accordance with 45 CFR 46 and CMU’s Federal-wide Assurance . The research protocol has been given approval as Exempt by the IRB on March 12 , 2014 , in accordance with 45 CFR 46 . 101 ( b ) ( 4 ) ( IRB Protocol Number: HS14-139 ) . All five datasets were processed using an identical processing pipeline implemented in DSI Studio ( http://dsi-studio . labslover . org ) , an open-source diffusion MRI analysis tool for connectome analysis . The source code is publicly available on the same website . As shown in Fig 2A , the diffusion MRI data of each subject were reconstructed in a common stereotaxic space using q-space diffeomorphic reconstruction ( QSDR ) [40] , a white matter based nonlinear registration approach that directly reconstructed diffusion information in a standard space: ψ ( u^ ) =|Jφ ( r ) |Z0∑iWi ( φ ( r ) ) sinc ( σ6Dbi<g^i , Jφ ( r ) u^‖Jφ ( r ) u^‖> ) ( 1 ) ψ ( u^ ) is a spin distribution function ( SDF ) [15] in the standard space , defined as the density of diffusing spins that have displacement oriented at direction u^ . φ is a function that maps a coordinate r from the standard space to the subject’s space , whereas Jφ is the Jacobian matrix of φ , and |Jφ| is the Jacobian determinant . Wi is the diffusion signals acquired by a b-value of bi with diffusion sensitization gradient oriented at g^i . σ is the diffusion sampling ratio controlling the displacement range of the diffusing spins sampled by the SDFs . Lower values allow for quantifying more from restricted diffusion . D is the diffusivity of free water diffusion , and Z0 is the constant estimated by the diffusion signals of free water diffusion in the brain ventricle [40] . A σ of 1 . 25 was used to calculate the SDFs , and 1 mm resolution was assigned to the output resolution of the QSDR reconstruction . A common axonal directions atlas , derived from the Human Connectome Project ( HCP ) dataset ( this HCP-488 atlas is freely available at http://dsi-studio . labsolver . org ) , was used as a common SDF sampling framework to provide a consistent set of sampling directions u^ to sample the magnitude of SDFs along axonal directions in the cerebral white matter . Gray matter was excluded using the ICBM-152 white matter mask ( MacConnel Brain Imaging Centre , McGill University , Canada ) . The cerebellum was also excluded due to different slice coverage in cerebellum across subjects . Since each voxel in the cerebral white matter may have more than one axonal direction , multiple measurements can be extracted from the SDF of the same voxel . The density measurements were sampled by the left-posterior-superior voxel order and compiled into a sequence of scalar values ( Fig 2B ) . Since the density measurement has arbitrary units , the local connectome fingerprint was scaled to make the variance equal to 1 . For each dMRI dataset , the root-mean-squared error between any two connectome fingerprints was calculated to obtain a matrix of paired-wise difference . The calculated difference was used as the feature to classify whether two connectome fingerprints are from the same or different person . The default linear discriminant analysis ( LDA ) classifier provided in MATLAB ( MathWorks , Natick , MA ) was used , and for each dataset , the classification error was estimated using leave-one-out cross-validation . We also used a modeling method to calculate the classification error if the leave-one-out cross-validation did not yield any classification error . The histograms of the within-subject and between-subject differences were fitted by the generalized extreme value distribution using the maximum likelihood estimator ( gevfit ) provided in MATLAB . To consider the non-negativity of the distribution , the estimated k parameter of the generalized extreme value distribution was set to be greater than 0 . The classification error was estimated by the probability of a within-subject difference greater than a between-subject difference estimated using the generalized extreme value distribution . To compare local connectome fingerprint with region-to-region connectivity matrix , deterministic fiber tracking[28] was applied using a 100 , 000 uniform white matter seeding points , a maximum turning angle of 60 degrees , and a default anisotropy threshold determined using Otsu’s threshold [41] . The cortical regions were defined through a nonlinear registration between the subject anisotropy map and the HCP-488 anisotropy map in DSI Studio and parcellated using the Automated Anatomical Labeling ( AAL ) atlas . The matrix entries were quantified by the number of tracks ending in each of the region pairs . The root-mean-squared error can also be calculated from any two connectivity matrices . The classification error was also estimated and compared with local connectome fingerprint . The similarity index between two local connectome fingerprints was calculated by 100%× ( 1-d1/d0 ) , where d1 was the difference between two fingerprints , and d0 was the expected value of the differences between unrelated subjects scanned by the same imaging protocol . The similarity between MZ twins , DZ twins , non-twin siblings , and repeated scans was calculated and compared . The Kruskal-Wallis test was applied to four groups ( MZ and DZ twins , siblings , and unrelated subjects ) with independent samples . To further study the similarity between repeat scans , the similarity indices were tested against their scanning time intervals by the Mann-Kendall test to study the effect of time interval on the local connectome fingerprints . | The local organization of white matter architecture is highly unique to individuals , making it a tangible metric of connectomic differences . The variability in local white matter architecture is found to be partially determined by genetic factors , but largely plastic across time . This approach opens a new door for probing the influence of pathological , genetic , social , or environmental factors on the unique configuration of the human connectome . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"genetic",
"fingerprinting",
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
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"molecular",... | 2016 | Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints |
There is evidence that biological synapses have a limited number of discrete weight states . Memory storage with such synapses behaves quite differently from synapses with unbounded , continuous weights , as old memories are automatically overwritten by new memories . Consequently , there has been substantial discussion about how this affects learning and storage capacity . In this paper , we calculate the storage capacity of discrete , bounded synapses in terms of Shannon information . We use this to optimize the learning rules and investigate how the maximum information capacity depends on the number of synapses , the number of synaptic states , and the coding sparseness . Below a certain critical number of synapses per neuron ( comparable to numbers found in biology ) , we find that storage is similar to unbounded , continuous synapses . Hence , discrete synapses do not necessarily have lower storage capacity .
Memory in biological neural systems is believed to be stored in the synaptic weights . Numerous computational models of such memory systems have been constructed in order to study their properties and to explore potential hardware implementations . Storage capacity and optimal learning rules have been studied both for single-layer associative networks [1] , [2] , studied here , and for auto-associative networks [3] , [4] . Commonly , synaptic weights in such models are represented by unbounded , continuous real numbers . However , in biology , as well as in potential hardware , synaptic weights should take values between certain bounds . Furthermore , synapses might be restricted to have a limited number of synaptic states , e . g . the synapse might be binary . Although binary synapses might have limited storage capacity , they can be made more robust to biochemical noise than continuous synapses [5] . Consistent with this , experiments suggest that synaptic weight changes occur in steps . For example , putative single synapse experiments show that a switch-like increment or reduction to the excitatory post-synaptic current can be induced by pairing brief pre-synaptic stimulation with appropriate post-synaptic depolarization [6] , [7] . Networks with bounded synapses have the palimpsest property , i . e . old memories decay automatically as they are overwritten by new ones [8]–[15] . In contrast , in networks with continuous , unbounded synapses , storing additional memories reduces the quality of recent and old memories equally ( see section Comparison to continuous , unbounded synapses ) . Forgetting of old memories must in that case be explicitly incorporated , for instance via a weight decay mechanism [16] , [17] . The automatic forgetting of discrete , bounded synapses allows one to study learning in a realistic equilibrium context , in which there can be continual storage of new information . It is common to use the signal-to-noise ratio ( SNR ) to quantify memory storage in neural networks [2] , [18] . The SNR measures the separation between responses of the network; the higher the SNR , the more the memory stands out and the less likely it will be lost or distorted . When weights are unbounded , each stored pattern has the same SNR . Storage capacity can then be defined as the maximum number of patterns for which the SNR is larger than some fixed , minimum value . However , for discrete , bounded synapses performance must be characterized by two quantities: the initial SNR , and its decay rate . Ideally , a memory has a high SNR and a slow decay , but altering learning rules typically results in either 1 ) an increase in memory lifetime but a decrease in initial SNR [18] , or 2 ) an increase in initial SNR but a decrease in memory lifetime . Optimization of the learning rule is ambivalent because an arbitrary trade-off must be made between these two effects . In this paper we resolve this conflict between learning and forgetting by analyzing the capacity of synapses in terms of Shannon information . We describe a framework for calculating the information capacity of bounded , discrete synapses , and use this to find optimal learning rules . We model a single neuron , and investigate how information capacity depends on the number of synapses and the number of synaptic states . We find that below a critical number of synapses , the total capacity is linear in the number of synapses , while for more synapses the capacity grows only as the square root of the number of synapses per neuron . This critical number is dependent on the sparseness of the patterns stored , as well as on the number of synaptic states . Furthermore , when increasing the number of synaptic states , the information initially grows linearly with the number of states , but saturates for many states . Interestingly , for biologically realistic parameters , capacity is just at this critical point , suggesting that the number of synapses per neuron is limited to prevent sub-optimal learning . Finally , the capacity measure allows direct comparison of discrete with continuous synapses , showing that under the right conditions their capacities are comparable .
The single neuron learning paradigm we consider is as follows: at each time-step during the learning phase , a binary pattern is presented and the synapses are updated in an unsupervised manner with a stochastic learning rule . High inputs lead to potentiation , and low inputs to depression of the synapses . Note that if we assume that the inputs cause sufficient post-synaptic activity , the learning rule can be thought of as Hebbian: high ( low ) pre-synaptic activity paired with post-synaptic activity leads to potentiation ( depression ) . After the learning phase , the neuron is tested with both learned and novel patterns , and it has to perform a recognition task and decide which patterns were learned and which are novel . Alternatively , one can do a ( supervised ) association task in which some patterns have to be associated with a high output , and others with a low output . This gives qualitatively similar results ( see Associative learning below ) . More precisely , we consider the setup depicted in Figure 1 . A neuron has n inputs , with weights wa , a = 1 , … , n . At each time-step it stores a n-dimensional binary pattern with independent entries xa . The probability of a given entry in the pattern being high is given by the sparseness parameter p . We set the value of x for the low input state equal to −p , and the high state to q = ( 1−p ) , so that the probability density for inputs is given by P ( x ) = qδ ( x+p ) +pδ ( x−q ) . Note that 〈x〉 = 0 . Using the expression for the SNR below , it can be shown that this is optimal , c . f . [2] . We assume that , as the case is fully analogous . Each synapse occupies one of W states . The corresponding values of the weight are assumed to be equidistantly spaced around zero , and are written as a W–dimensional vector , e . g . for a 3-state synapse s = {−1 , 0 , 1} , while for a 4-state synapse s = {−3 , −1 , 1 , 3} . In numerical analysis we sometimes saw an increase in information by varying the values of the weight states , although this increase was always small . The state of any given synapse at a given time is described stochastically , by a probability vector π . Each entry of π is the probability that the synapse is in that state ( and hence the weight of the synapse takes the corresponding value in the weight look-up table s ) . Finally , we note that this setup is of course an abstraction of biological memory storage . For instance , biological coding is believed to be sparse , but the relation between our definition of p and actual biological coding sparsity is likely to be complicated . Our model furthermore assumes plasticity at each synapse and for every input . In some other models it has been assumed that only a subset of the inputs can cause synaptic changes [14] . Our model could in principle include this by defining null inputs that do not lead to plasticity at all . This would lead to two sparsity parameters: the proportion of inputs that induce plasticity and the proportion of plasticity-inducing inputs that lead to actual strengthening of the synapse . Storage capacity depends on the W×W learning matrices M+ and M− . To find the maximal storage capacity we need to optimize these matrices , and this optimization depends on sparseness , the number of synapses , and the number of states per synapse . Because these are Markov transition matrices , their columns need sum to one , leaving W ( W−1 ) free variables per matrix .
We have studied pattern storage using discrete , bounded synapses . Learning rules for these synapses can be defined by stochastic transition matrices [18] , [19] . In this setup an SNR based analysis provides two contradictory measures of performance: the quality of learning ( the initial SNR ) , and the rate of forgetting [18] . With our single measure of storage capacity based on Shannon information , learning rules can be optimized . The optimal learning rule depends on the number of synapses n and the coding sparseness p , as well as on the number of states W . Our analysis was restricted to about 8 states per synapse , although we have no reason to believe that extrapolation to larger numbers would not hold . Given optimal learning we find two regimes for the information storage capacity: 1 . When the number of synapses is small , information per synapse is constant and approximately independent of the number of synaptic states . 2 . When the number of synapses is large , capacity per synapse increases linearly with W but decreases as . The critical n that separates the two regimes is dependent on sparseness and the number of weight states . The optimal learning rule for regime 2 has band-diagonal transition matrices , and in the dense case ( p = 1/2 ) , these take a particularly simple form , see Equation 16 . Capacity of order in the large n limit has been reached in other studies of bounded synapses [10] , [21] , but has not been exceeded to our knowledge . It remains a challenge to construct a model that does better than this . The implications for biology depend on the precise nature of single neuron computation . If a neuron can only compute the sum of all its inputs then we might conclude the following . As synapses are metabolically expensive [22] , biology should choose parameters such that the number of synapses per neuron does not exceed the critical number much . Although there are currently no accurate biological estimates for either the number of weight states , or the sparsity , for binary synapses with p = 0 . 005 , the critical number of synapses is close to the number of synapses ( ∼10 , 000 ) per neuron in the hippocampus ( see Figure 2 ) . However , if the neurons can do compartmentalized processing so that the dendrite is the unit of computation [23] , then one could think of this model as representing a single dendrite , and we could conclude that the number of synapses per dendrite might be optimized for information storage capacity . For binary synapses with p = 0 . 005 choosing the number of synapses to be several hundred is also close to optimal . Furthermore , our results predict that when synapses are binary , coding is sparse , and learning is optimized , that at equilibrium about 67% of synapses should occupy the low state . This is not far off the experimental figure of 80% [7] . We have directly compared discrete to continuous synapses . For few synapses and dense coding , binary synapses can store up to 0 . 11 bits of information , which is comparable to the maximal capacity of continuous synapses . However , for sparse coding and many synapses per neuron , the capacity of binary synapses is reduced . Hence , if one considered only information storage , one would conclude that , unsurprisingly , unbounded synapses perform better than binary synapses . However , in unbounded synapses , weight decay mechanisms must be introduced to prevent runaway , so the information storage capacity is necessarily reduced in on-line learning [16] , [17] . In contrast , for bounded , discrete synapses with ongoing potentiation and depression , such as those considered here , old memories undergo “graceful decay” as they are automatically overwritten by new memories [8] , [9] , [12] , [13] , [15] . Thus discrete , bounded synapses allow for realistic learning with a good capacity . Finally , it is worth noting that although using Shannon information is a principled way to measure storage , it is unclear whether for all biological scenarios it is the best measure of performance , c . f . [24] . The information can be higher when storing very many memories with a very low SNR , than when storing just a few patterns very well . This might be undesirable in some biological cases . However , if many neurons work in parallel on the same task , it is likely that all information contributes to performance , and thus the total bits per synapse is a useful measure .
To obtain the information capacity numerically , we used Matlab and implemented the following process . For a given number of synaptic states , number of synapses and sparsity , we used Matlab's fminsearchbnd to search through the parameter space of all possible transfer matrices M+ and M− . That is , all matrix elements were constrained to take values between 0 and 1 , and all columns were required to sum to 1 . For each set of transfer matrices we first obtained the equilibrium weight distribution π∞ as the eigenvector with eigenvalue 1 of the matrix M . Then we computed the means and variances of the output for learned and unlearned patterns from Equations 2 and 1 , and further used that . Equations 6 and 3 then gave the information stored about the pattern presented at each time-step . To calculate the total information , this was summed over sufficient time-steps . In particular , in the case of many weight states ( large W ) and sparse patterns , the maximization would sometimes get stuck in local maxima . In those cases we did multiple ( up to 50 ) restarts to make sure that the solution found was truly optimal . Our results can also be compared to the so-called cascade model , which was recently proposed to have high SNR and slow memory decay [10] . In order to compare the cascade model to our results , we created a six-state cascade model using learning matrices that only had transitions according to the state diagram in [10] . These transition rates were then optimized . We found that the information capacity of the optimized cascade model was always larger than a two-state model , but always lower than our six state model with transfer matrix Equation 16 . Only when the number of synapses was small ( and the information became equal to the integral over the SNR ) , did the two-state , six-state and cascade models give identical performance . For a higher number of states the results could be different , but this study suggests that , at least for a small number of states , the cascade model is sub-optimal with respect to Shannon information capacity . Finally , we explored how well the Gaussian approximation worked . We calculated the full multinomial distribution of the total input h and applied an optimal threshold . Because of a combinatorial explosion , this was only feasible for up to 100 synapses . When the information was maximized this way , the information increased to about 0 . 3 for n = 1 binary synapses storing dense patterns , but for more than n = 10 synapses the results were indistinguishable from the presented theory . | It is believed that the neural basis of learning and memory is change in the strength of synaptic connections between neurons . Much theoretical work on this topic assumes that the strength , or weight , of a synapse may vary continuously and be unbounded . More recent studies have considered synapses that have a limited number of discrete states . In dynamical models of such synapses , old memories are automatically overwritten by new memories , and it has been previously difficult to optimize performance using standard capacity measures , for stronger learning typically implies faster forgetting . Here , we propose an information theoretic measure of storage capacity of such forgetting systems , and use this to optimize the learning rules . We find that for parameters comparable to those found in biology , capacity of discrete synapses is similar to that of unbounded , continuous synapses , provided the number of synapses per neuron is limited . Our findings are relevant for experiments investigating the precise nature of synaptic changes during learning , and also pave a path for further work on building biologically realistic memory models . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/theoretical",
"neuroscience",
"computational",
"biology/computational",
"neuroscience"
] | 2008 | Optimal Learning Rules for Discrete Synapses |
Little information is available about infantile visceral leishmaniasis ( VL ) in Albania as regards incidence , diagnosis and management of the disease . Demographic data , clinical and laboratory features and therapeutic findings were considered in children admitted to University Hospital of Tirana from 1995 to 2009 , and diagnosed as having VL . The diagnosis was based on bone-marrow microscopy/culture in 77 . 5% of patients , serology in 16 . 1% , and ex juvantibus in 6 . 4% . A total of 1 , 210 children were considered , of whom 74% came from urbanized areas . All patients were in the age range 0–14 years , with a median of 4 years . Hepatosplenomegaly was recorded in 100% , fever in 95 . 4% and moderate to severe anemia in 88% of cases . Concomitant conditions were frequent: 84% had bronchopneumonia; diarrhea was present in 27% , with acute manifestations in 5%; 3% had salmonellosis . First-line therapy was meglumine antimoniate for all patients , given at the standard Sbv dosage of 20 mg/kg/day for 21 to 28 days . Two children died under treatment , one of sepsis , the other of acute renal impairment . There were no cases of primary unresponsiveness to treatment , and only 8 ( 0 . 67% ) relapsed within 6–12 months after therapy . These patients have been re-treated with liposomal amphotericin B , with successful cure . Visceral leishmaniasis in pediatric age is relatively frequent in Albania; therefore an improvement is warranted of a disease-specific surveillance system in this country , especially as regards diagnosis . Despite recent reports on decreased responses to antimonial drugs of patients with Mediterranean VL , meglumine antimoniate treatment appears to be still highly effective in Albania .
Zoonotic visceral leishmaniasis ( VL ) is a disseminated protozoan infection transmitted by phlebotomine sandflies , caused by Leishmania infantum in areas of the Old and New Worlds [1] . In Mediterranean countries , about 1 , 000 people are estimated to be affected by clinical disease annually [2] although asymptomatic or sub-clinical cases are by far more frequent [3]–[5] . Mediterranean VL affects primarily children as well as an increasing rate of immunocompromised and immunosuppressed adult individuals , such as HIV- infected [6] and patients under any immunosuppressive therapies [7]–[8] . The disease is known to occur in Albania since 1938 typically as a childhood disease [9]; however , despite being a notifiable disease in the country , VL case records and statistics have not been available to international health organizations ( such as World Health Organization ) nor to the scientific community for long time . Albania is a developing country that is progressively increasing its social , economic and sanitary relationships with western countries . However , being its health care system still in progress , there are incomplete data on the clinical epidemiology of some infectious diseases . In particular , little information is available about VL in children as regards disease diagnosis and management . Herein we present the data derived from an observational retrospective cohort study performed at the University Hospital “Mother Theresa” of Tirana ( UHT ) , aimed at the analysis of the epidemiological , clinical , diagnostic and therapeutic features of pediatric VL in Albania in the 1995–2009 period .
We evaluated the data regarding 1 , 210 children admitted from 1995 to 2009 to the Infectious Diseases ward of UHT , the largest pediatric hospital of Albania ( about 400 beds ) . The ward ( 45 beds ) is the national reference centre where any Albanian children suspected or diagnosed for VL in peripheral hospitals are referred for diagnosis and/or treatment . Demographic , clinical and laboratory findings were collected prospectively into a database and the data analyzed retrospectively by the UHT medical staff ( RP and LK ) . Diagnosis and therapy approaches followed systematically the guidelines for VL management approved by the Ministry of Health and adopted by the sanitary directorship of UHT . They include the following general examinations: full hematologic assessment , biochemical profile ( serum urea , creatinine , ALT , total proteins , protein electrophoresis and bilirubin ) , electrolytes ( K+ ) , hemoculture , urine examination , ECG , chest x-ray and abdominal sonography . Among infectious diseases , differential diagnosis includes brucellosis , abdominal typhus and HIV . First-line VL diagnosis was based on the microscopic demonstration of Leishmania amastigotes on Giemsa-stained smears of bone marrow aspirates . Aspirate material was also seeded in culture media for Leishmania ( NNN , Evans' Modified Tobie's or MEM media ) when available from the Tirana Institute of Public Health . If direct parasitological diagnosis proved negative but strong clinical suspicion of VL persisted , search for anti-Leishmania antibodies was performed using ELISA or IFAT commercial kits ( IgG and IgM , BioMerieux , France ) . This second-line serological approach has been in use starting from 1999 . When serology could not be performed because of shortage of kits due to financial constraints , patient's parents or guardians were recommended to send sera to private laboratories by their own . In case border-line serology results , a new serum sample was examined after one week by two different laboratories . In patients with negative bone marrow and negative or border-line serology , but showing persisting clinical and laboratory features suggestive of VL without evidence of other systemic diseases , presumptive treatment was administered and diagnosis performed ex juvantibus . Upon diagnosis , the first-line treatment was administered in hospital using meglumine antimoniate once daily , given intramuscularly , at the Sbv dosage of 20 mg/kg/day for 21 to 28 days . Patients relapsing following initial cure were re-treated using intravenous liposomal amphotericin B at the dose of 3 mg/kg/day in days 1–5 , 14 and 21 [10] . Treatment-associated adverse events of grade 2 or higher were considered . Clinical samples were collected from children suspected as having VL , following the national guidelines for laboratory diagnosis of the disease at UHT . Informed written consent was obtained from parents or guardians and recorded on the medical chart before performing bone-marrow aspiration . The study was approved by the National Committee of Bioethics , Ministry of Health , Republic of Albania .
The yearly number of admitted patients ranged from 63 to 146 , with a mean of admitted per-year of 93 patients . The monthly distribution of cases referred to the date of hospitalization and VL diagnosis is shown in the graph of Figure 1 . Cases were identified in all months , with a minimum in September ( 61 cumulative cases ) and a maximum in July ( 174 cases ) . The main cluster was in the May–July period ( 487 cases , 40% ) . Because over half of the patients referred onset of symptoms 1–3 months before hospital admittance ( see below ) , the cluster is consistent with the usual long incubation period ( around 6 months ) of L . infantum infections transmitted late in the preceding sand fly season ( May–October ) [9] . Seventy-four % of patients came from urbanized areas , mainly from peripheral districts of Shkodër and Lezhë in the north , Tirana in the centre and Lushnjë , Fier , Berat and Vlorë in the south , whereas 26% were from rural areas represented by coastal or lake territories . All districts of Albania were involved , with large variations in the distribution of cases . A map showing the stratification of cumulative cases by district is shown in Figure 2 . The age of patients ranged from 0 to 14 years ( median: 4 years ) . The majority of cases belonged to the 1–4 years age group ( 61% ) . Sixteen % were in 0–1 , 17% in 4–10 and 6% in 10–14 years age groups , respectively . Fifty-eight% were males . At the admittance , 42 . 2% of patients reported the onset of symptoms in the last 30 days or less , for a minimum of 10 days , and 57 . 8% since more than 30 days , for a maximum of 3 months . At presentation only 4 . 5% of the patients was afebrile; fever >38°C was recorded in 63 . 3% of patients . In Table 1 the main VL symptoms and signs of the patients and data from physical examination are summarized . Regarding hematological evaluation , the most frequent finding was anemia ( 99 . 8% ) with a mean value of hemoglobin 7 . 0 g/dl . Hematological and biochemical features recorded at admittance are shown in Table 2 . In addition , concomitant conditions were frequently recorded: 84% of patients had bronchopneumonia; diarrhea was present in 27% , with acute manifestations in 5%; 3% of patients had salmonellosis . In 938 patients ( 77 . 5% ) the bone-marrow aspirate resulted positive for the microscopic research of Leishmania amastigotes . Cultures of bone marrow aspirates , performed in one fourth of the patients , resulted positive in 65% of cases , all being positive also to microscopy . Serology was performed in a limited number of bone-marrow positive patients ( 40 ) , providing however some information on the validity of the commercial kits employed: 20 sera were examined by IFAT and 20 by ELISA ( a retrospective evaluation included also 27 patients without leishmaniasis but affected by other systemic pathologies ) . Among the 272 patients in whom bone-marrow smears did not reveal amastigotes , 203 ( admitted from 1999 ) have been examined serologically: 145 by IFAT and 58 by ELISA . Overall serology detected 195 clear positives in this subgroup , and 39/40 in the bone-marrow positive subgroup . Because there were 3 false weak positives among non-leishmaniasis patients , the mean sensitivity and specificity values of the combined IFAT and ELISA tests were estimated to be 96% and 92% , respectively . Finally , there were 69 patients with negative bone marrow that could not be examined serologically because admitted before 1999 . These patients , and the 8 bone-marrow negative patients with negative/border-line serology ( for a total of 77/1 , 210 , 6 . 4% ) have been diagnosed as affected by VL because they showed persisting clinical and laboratory features strongly suggestive of the disease , other diseases were excluded , and presumptive treatment administered resulted in clinical cure . All the patients were treated with meglumine antimoniate . Two children died in course of treatment ( 0 . 16% ) , one for sepsis the other for acute renal impairment . There were no cases of primary clinical unresponsiveness to the first-line therapy . By the end of treatment a second bone-marrow aspirate was performed and found negative in all patients . Eight patients ( 0 . 67% ) showed a clinical relapse of VL , confirmed parasitologically , within 6–12 months from therapy and needed re-treatment with liposomal amphotericin B , which successfully cured these patients as assessed by 1-year post-therapy follow-up . In 39% of the children treated with meglumine antimoniate at least one adverse event was recorded ( Table 3 ) . Patients treated with liposomal amphotericin B did not show adverse events .
Globally , there are an estimated 500 , 000 new cases of VL and more than 50 , 000 deaths from the disease each year , however both figures are approximations as VL is frequently not recognized or not reported [2] , [11] . Migration , lack of control measures and HIV co-infection are the three main factors reported for driving the increased incidence of VL [12] . Poverty and leishmaniasis are also strictly associated . Poor housing conditions and diet , poverty-related concurrent infections as well as proximity to infected dogs , are all specific factors related to zoonotic VL [13] . As regards the VL transmission potential , at least 3 proven L . infantum vectors are present Albania , Phlebotomus neglectus being the most widespread and showing the highest relative density among Phlebotomus species [9] . Although no systematic canine surveys have been performed throughout the country , a few studies suggest that canine infections are widespread , with seroprevalence ( IFAT ) rates of 16–17% recorded in several districts including Tirana ( Teita Myrseli , Institute of Public Health , Tirana , personal communication ) . Our experience shows that VL in the pediatric age occurs frequently in Albania , with a high number of per-year admissions relatively constant during the 15-year period of observation . VL is also recorded in adults [9]: from 1998 to 2008 , 126 cases diagnosed in the age groups 15–>60 years have been notified to the Control of Infectious Diseases Department , Institute of Public Health , Tirana . Furthermore , HIV-Leishmania co-infections have been diagnosed in 26 adult patients out of some 300 HIV/AIDS cases recorded from 1993 to 2010 . These figures indicate that VL is still largely an infantile disease in Albania , the children representing 87 . 7% of total VL cases . Being the surveillance system in Albania still in development we can infer that the number of actual cases including the undiagnosed or misdiagnosed ones could be even higher . In fact , a high proportion of patients came from urbanized areas , therefore small villages or farming areas might suffer from the phenomenon of under-diagnosis in rural territories with a lower degree of medical surveillance . According to the 2001 census , in Albania live about 330 . 000 children under 6 years ( www . instat . gov . al ) . Based on our VL figures ( 90% of cases belong to this age group ) we can estimate an yearly incidence of 25/100 . 000 of this at-risk population , which is much higher than commonly observed in southern European countries endemic for VL [2] , [14] . Clinical presentation and laboratory findings reflect classic features of a poverty-related disease . The relatively high number of co-morbidities among the patients is noteworthy , as it is the elevated prevalence of severe anemia; they may suggest poor hygiene and diet deficiency , respectively . As regards the high frequency of bronchopneumonia ( 84% ) , the Balkan continental climate which dominates the weather in Albania ( hot summer and very cold winter in the inland territories ) may have a role in respiratory trait pathologies in debilitated children under poor housing conditions . Sensitivity of bone marrow microscopy for Leishmania diagnosis ( 77 . 5% ) was considerably lower than figures reported in published cases series of Mediterranean VL , both in children ( 97 . 6% ) and adults ( 97 . 0% ) [15] , [16] . This may reflect a low degree of accuracy in performing the parasitological investigation . Starting from 1999 we could rely on commercial serological assays widely used in European hospital laboratories . Through serology , we could confirm the disease in 195/203 ( 96% ) bone-marrow negative patients with strong clinical evidence of VL and in whom other systemic pathologies had been excluded . Only 6 . 4% of our patients received presumptive treatment due to the lack of specific VL diagnosis; all of them were clinically cured following pentavalent antimony therapy . As regards the possibility that Leishmania asymptomatic infections detected by serology were present in children exhibiting VL-mimicking conditions , it should be pointed out that differently from DAT or Western blot serological assays , or PCR methods , IFAT and ELISA commercial kits hardly detect L . infantum infections in children without overt clinical VL , and when they do so antibody titers are around the cut-off [17] , [18] . Indeed , consultation with pediatric reference centres in developed Mediterranean countries disclosed the general consensus that presence of clinical signs and symptoms compatible with VL associated with the detection of serum Leishmania antibodies are established criteria for VL diagnosis alternative to Leishmania demonstration in bone-marrow aspirates [19] . Discovered 60 years ago , pentavalent antimonials remain the mainstay treatment of VL despite the long duration of administration and severe side effects which may be observed in some patients , especially adults with underlying conditions [20] . Meglumine antimoniate is still effective with more than 95% cure rate in those Mediterranean countries where the drug is of routine use for VL [21] , whereas Leishmania antimony resistance is increasing in regions where massive treatments of infected dogs with antimonials , such as Italy and southern France , are common [22] . Anti-leishmanial therapy of infected pets is not a usual practice in Albania , which may explain the very high therapeutic efficacy of pentavalent antimony exhibited in our patients ( 99 . 3% cure rate ) . Furthermore , it appears that the drug was well tolerated in children . There were no cases of primary unresponsiveness and the relapse rate was low ( 0 . 7% ) . The few relapsing cases were treated successfully with a 7-day course of liposomal amphotericin B without adverse events . Treatment costs associated with this drug are not affordable by the Albanian health system , therefore we had to rely on the contribution and assistance of foreign institutions ( University of Bari , Italy ) . In conclusion , our study disclosed a pattern of pediatric VL which is somehow different from that of neighboring developed Mediterranean countries , despite sharing common Leishmania agents , reservoir and vectors: incidence of clinical disease in childhood and frequency of co-morbidities are by far more elevated , thus reinforcing the concept that poverty and leishmaniasis are strictly associated [13] . On the other hand , the study showed also important limitations , first of all a low standard laboratory diagnostic capability ( as revealed by the scarce sensitivity of bone marrow microscopy and unsatisfactory border-line serological findings ) associated to an over-emphasized use of presumptive treatments and ex juvantibus diagnosis based on clinical experience . Furthermore , the studied population may represent a biased sample of patients living in urbanized areas where the passive medical surveillance is more efficient . Therefore an improvement is warranted of a disease-specific surveillance system in Albania which includes training of laboratory staff in peripheral hospitals , active search of the infection in febrile children resistant to antibiotic therapies and the use of specific and sensitive methods for diagnosis and follow-up . | Albania is a developing country that is rapidly improving in social , economic and sanitary conditions . The health care system in still in progress and the impact of some infectious diseases remains poorly understood . In particular , little information is available on incidence , clinical features and response to treatment of visceral leishmaniasis ( VL ) in childhood . We performed a retrospective analysis of data recorded from 1995 to 2009 at the national pediatric reference hospital of Tirana where any child suspected for VL is referred for specific diagnosis and treatment . Epidemiology , clinical features and management of the disease were considered . The main findings can be summarized as follows: i ) The incidence of the disease in Albanian children ( 25/100 , 000 in the age group 0–6 years ) is much higher than in developed Mediterranean countries endemic for VL; ii ) The disease is associated with poor sanitary conditions as suggested by the high rate of severe clinical features and frequency of co-morbidities; iii ) The cheapest drug available for Mediterranean VL treatment ( meglumine antimoniate ) is highly effective ( 99% full cure rate ) and well tolerated . Limitations were identified in the low standard laboratory diagnostic capability and unsatisfactory medical surveillance in less urbanized areas . An improvement is warranted of a disease-specific surveillance system in Albania . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/protozoal",
"infections"
] | 2010 | Pediatric Visceral Leishmaniasis in Albania: A Retrospective Analysis of 1,210 Consecutive Hospitalized Patients (1995–2009) |
For sexually reproducing organisms , production of male or female gametes depends on specifying the correct sexual identity in the germline . In D . melanogaster , Sex lethal ( Sxl ) is the key gene that controls sex determination in both the soma and the germline , but how it does so in the germline is unknown , other than that it is different than in the soma . We conducted an RNA expression profiling experiment to identify direct and indirect germline targets of Sxl specifically in the undifferentiated germline . We find that , in these cells , Sxl loss does not lead to a global masculinization observed at the whole-genome level . In contrast , Sxl appears to affect a discrete set of genes required in the male germline , such as Phf7 . We also identify Tudor domain containing protein 5-like ( Tdrd5l ) as a target for Sxl regulation that is important for male germline identity . Tdrd5l is repressed by Sxl in female germ cells , but is highly expressed in male germ cells where it promotes proper male fertility and germline differentiation . Additionally , Tdrd5l localizes to cytoplasmic granules with some characteristics of RNA Processing ( P- ) Bodies , suggesting that it promotes male identity in the germline by regulating post-transcriptional gene expression .
Sex determination is an essential process in sexually reproducing species , as the production of eggs and sperm depends on the sexual identity of the germ cells and somatic cells of the gonad . In some animals , such as the medaka fish and the house fly , the sexual identity of the soma determines the sexual identity of the germline . But in other animals , such as fruit flies and mammals , the intrinsic sex chromosome constitution ( XX vs . XY ) of the germ cells is also important for proper gametogenesis ( reviewed in [1] ) . In such cases , the “sex” of the germ cells must match the “sex” of the soma in order for proper gametogenesis to occur . While studies have revealed a great deal about how sex is determined in the soma , how germline sexual identity is determined by a combination of somatic signals and germline autonomous properties is much less well understood . In Drosophila , somatic sexual identity is determined by the X chromosome number [2] , ( reviewed in [3] ) , with two X’s activating expression of the key sex determination gene Sex lethal ( Sxl ) , promoting female identity . The Sxl RNA-binding protein initiates an alternative RNA splicing cascade to allow female-specific splicing of transformer ( tra ) and , subsequently , doublesex ( dsx ) and fruitless ( fru ) . dsx and fru encode transcription factors that control somatic sexual identity ( reviewed in [4] ) . Sxl is also the key gene controlling autonomous sex determination in the germline , as Sxl is expressed in the germline in females , and loss of Sxl causes female ( XX ) germ cells to develop as germline ovarian tumors [5] , similar to male ( XY ) germ cells transplanted into a female soma [6–8] . Further , expression of Sxl is sufficient to allow XY germ cells to make eggs when transplanted into a female soma [9] . However , how Sxl is activated in the female germline and how it regulates female germline identity remain unknown , except for the fact that both are different than in the soma [6 , 10–16] . To understand how Sxl promotes female germ cell identity , it is essential to discover its targets in the germline . In this work , we report an RNA expression profiling ( RNA-seq ) experiment conducted to identify genes regulated downstream of Sxl in the germline . We found that a previously uncharacterized tudor domain containing protein , Tudor domain protein 5-like ( Tdrd5l ) , is a target of Sxl in the germline . Tdrd5l is strongly expressed in the Drosophila early male germline and is repressed by Sxl activity in the early female germline . It promotes male identity in the germline , and its loss results in germline maintenance and differentiation defects in males , thus reducing their fertility . Tdrd5l protein localizes to cytoplasmic granules related to RNA Processing ( P- ) Bodies , suggestive of a function in post-transcriptional regulation of gene expression .
To investigate how Sxl acts to promote female identity in germ cells , we conducted an RNA-seq experiment comparing ovaries with and without Sxl function in the germline . To exclude the major gene expression changes that occur during the later stages of gametogenesis , the RNA-seq experiment was done in the bag of marbles ( bam ) mutant background . bam is essential for germline differentiation in both males and females; therefore , by using bam mutants we focus our experiment on gene expression changes in the early germline , where Sxl is expressed most robustly , instead of later stages of gametogenesis ( Fig 1A and 1B ) . The use of bam mutants also gives us the ability to compare similar tissue samples , since ovarian tumors from bam mutants and bam , Sxl double mutants are very similar [17] . Thus , the two genotypes used for the RNA-seq experiment are both in the bam-mutant background , with the experimental genotype knocking down Sxl in the germline using RNAi ( nanos-Gal4 , UAS-SxlRNAi , bam ) , and a control genotype expressing a control RNAi ( nanos-Gal4 , UAS-mCherryRNAi , bam ) . Sxl protein in the germline was dramatically reduced in the Sxl RNAi samples relative to controls ( Fig 1B and 1C ) . Additionally , we conducted RNA-seq on bam mutant males compared to females , similar to what has been done previously [18] in order to identify male-enriched vs . female-enriched RNAs in the undifferentiated germline . Libraries were prepared from three biological replicates of each genotype and sequenced with 100bp paired-end reads . The raw reads for all libraries were of very high quality—over 95% of the raw reads received a high quality score . In addition , for each library more than 85% of reads were uniquely mapped to the Drosophila genome , and all replicates had high replicate correlation . As Sxl can regulate alternative splicing in the soma , we analyzed our RNA-seq data for changes in exon usage using DEXSeq [19] . We filtered these results for statistical significance ( padj < 0 . 05 ) and for changes in exon expression that were 2-fold or greater ( all DEXSeq data is presented in S1 Table ) . This identified 44 exons from 34 independent genes . A similar number of exons were upregulated in Sxl- ( 20 , 45% ) vs . downregulated ( 24 , 55% ) . However , manual curation of these data did not reveal strong candidates for relevant biological regulation of alternative splicing by Sxl . The main exception was the Sxl RNA itself , which in Sxl RNAi samples exhibited the characteristic inclusion of the male-specific exon that requires Sxl protein in order to be excluded in the female soma . Thus , the residual Sxl RNA present after germline Sxl RNAi must be insufficient to produce enough Sxl protein for Sxl autoregulation , further reducing Sxl function in the germline and demonstrating the effectiveness of the germline Sxl RNAi approach . This analysis also identified the alternative , testis-specific promoter for the male germline sex determination factor Plant homeodomain containing protein 7 ( Phf7 , [20] as being regulated downstream of Sxl , as has previously been described [21] . However , based on this analysis , it does not appear that regulation of alternative splicing is a primary mechanism for Sxl regulation of gene expression in the germline . We also analyzed changes in overall gene expression levels in the presence of absence of Sxl function ( DE-Seq [22] ) . 94 genes were differentially expressed between the two samples ( with padj < 0 . 05 ) , with 40 being upregulated ( 43% ) and 54 being downregulated ( 57% ) in the Sxl RNAi samples ( S2 Table ) . 24 of these genes differed 4-fold or more between the two samples ( 6 upregulated in Sxl RNAi , 18 downregulated ) . We also observed a bias toward X chromosome localization for the differentially expressed genes , with 31 . 9% being present on the X chromosome compared to only 15 . 1% of all genes surveyed having X chromosome localization . We next asked whether Sxl acts as a global regulator of X chromosome gene expression/dosage compensation in the germline , as it does in the soma . Despite the X chromosome bias for differentially expressed genes , relatively few X chromosome genes overall ( 30 genes or 1 . 2% of X chromosome loci surveyed ) were called as differentially expressed in our analysis . Further , if Sxl was a global regulator of X chromosome gene expression , we would expect that the overall level of X chromosome gene expression , relative to autosomal gene expression , would be altered in Sxl loss of function . However , the overall ratio of average gene expression between the X chromosome and autosomes was little changed between control and Sxl RNAi samples ( X:A of 1 . 19 for controls and 1 . 24 for Sxl RNAi ) . We conclude that Sxl is not a global regulator of X chromosome gene expression in the germline . Lastly , we evaluated the extent to which loss of Sxl in the undifferentiated ( bam-mutant ) female germline leads to masculinization of these cells . We conducted principle component analysis on the individual replicates of all four of our experimental conditions ( Fig 1D ) . We find that bam Sxl-RNAi female samples are much more similar to bam female samples and control bam mCherry-RNAi samples than they are to bam male samples . Thus , this analysis does not support a strong masculinization of the bam-mutant female germline in the absence of Sxl . However , it is also known that bam does not affect males and females in exactly the same manner [23] and , in addition , the bam male samples also contain male somatic cells while the other samples contain female somatic cells . Both of these factors may contribute to the segregation of the bam male sample away from the others . To circumvent this problem , we analyzed the 94 genes differentially expressed between bam Sxl-RNAi and control samples to determine whether there was a “male” signature . We determined whether genes differentially expressed in Sxl-RNAi compared to controls were also differentially expressed in bam males compared to bam females . Of the 94 genes differentially expressed in Sxl RNAi , a high fraction ( 44 genes ) were also differentially expressed between bam male vs . female samples ( 47% , which is considerably higher than the fraction of total genes in the genome called as different between bam male and female , 13% ) . However , these genes did not always change in the expected direction; only 61% of genes altered in both Sxl RNAi and bam males changed in the same direction in both , while 39% changed in the opposite direction ( Fig 1E ) . Thus , the Sxl-RNAi sample does not appear globally “masculinized” relative to controls and it may be that Sxl’s role in repressing male identity in the early germline is restricted to a few specific targets that are important for the male germline . One such candidate is a previously uncharacterized gene , CG15930 , that was strongly upregulated in Sxl-RNAi ovaries and is normally enriched in testes . CG15930 exhibits strong homology to mouse Tdrd5 [24] , which is essential for male germ cell development and spermatogenesis in mice [25] . However , since CG15930 is not as similar to Tdrd5 as Drosophila tejas [26] , and therefore not a paralog of Tdrd5 , we named this gene Tudor domain containing protein 5- like ( Tdrd5l ) . We chose this gene for further study . The RNA-seq expression profiles show that Tdrd5l has a dynamic expression pattern characteristic of genes with sex-specific functions . Tdrd5l is 18-fold enriched in bam testes compared to bam ovaries , and is upregulated 17-fold in bam , Sxl-RNAi ovaries relative to bam , control-RNAi ovaries ( Fig 2A ) , and is clearly enriched in bam , Sxl-RNAi ovaries by RT-PCR ( Fig 2B ) . This is in contrast to nanos , which is expressed at similar levels in all of the genotypes in our RNA-seq experiment ( Fig 2A ) , consistent with its role in the germline of both sexes . Changes in Tdrd5l expression were restricted to total RNA levels , and no change in exon usage was detected . In situ hybridization to wild-type gonads revealed that Tdrd5l expression is highly enriched in the testis , particularly at the apical tip of the testis where the germline stem cells and proliferating gonial cells reside ( Fig 2C and 2D ) . The finding that Tdrd5l is expressed at high levels in testes relative to ovaries , and is repressed by Sxl in the ovary , suggests that it plays a role in male germline development or function . To determine the expression pattern of Tdrd5l protein , we generated a genomic transgene that includes a hemagglutinin ( HA ) epitope tag at the N-terminus of the Tdrd5l protein . The tag was inserted immediately following the start codon of the gene within a 20kb BAC that extends well upstream and downstream of the genes neighboring Tdrd5l ( S1 Fig ) , and is therefore likely to recapitulate endogenous expression . Anti-HA immunostaining shows that Tdrd5l protein is expressed in the germline of the testis , and is also present in the ovary at lower levels ( Fig 2E and 2F ) . HA::Tdrd5l is observed in male germline stem cells , spermatogonia and spermatocytes . The protein is seen in distinct foci that are smaller and more numerous in germline stem cells ( Fig 2G , arrows ) , but appear larger in spermatocytes ( Fig 2E arrows ) . The foci are predominantly cytoplasmic , with many abutting a perinuclear germline structure called the nuage . The accumulation of Tdrd5l into cytoplasmic punctae is characteristic of ribonucleoprotein complexes ( “RNA bodies” ) ( reviewed in [27] ) , and suggests that it may be involved in mRNA decay or translational repression . Interestingly , HA::Tdrd5l co-localizes with decapping protein 1 , ( YFP-DCP1 , Fig 2H , arrows ) , which plays a major role in mRNA degradation , and is also required for osk mRNA localization to the posterior of the oocyte [28] , ( reviewed in [29] ) . As DCP-1 is localized to “Processing bodies” ( P-bodies ) , Tdrd5l appears to be present in a subset of these structures . HA::Tdrd5l protein expression is upregulated in ovaries that are mutant for Sxl function in the germline ( Fig 3A and 3B ) . Note that the punctae seen in males are also present in Sxl RNAi ovaries , though they are fewer in number ( Fig 3D , arrows ) . Thus , the de-repression of Tdrd5l in Sxl RNAi ovaries is also detected at the protein level . Interestingly , the Tdrd5l mRNA has 2 putative Sxl binding sites [30 , 31] , one within the 3rd intron and the other in the 3’ UTR ( S1 Fig ) . This suggests that Sxl may directly regulate Tdrd5l expression by binding to one or both of these sites and influencing Tdrd5l RNA processing in the nucleus or translation in the cytoplasm . To assess Sxl’s direct regulation of Tdrd5l , we mutated both Sxl binding sites in the a HA::Tdrd5l transgene ( HA::Tdrd5lddel ) . We found HA::Tdrd5lddel flies show upregulation of HA::Tdrd5l in the female germline ( Fig 3C ) , similar to the upregulation caused by loss of Sxl from the germline . To quantify this regulation , we again took advantage of the bam-mutant background that enriches for Sxl-expressing cells and exhibits a similar tissue phenotype with and without Sxl function . We again found that the WT HA::Tdrd5l transgene exhibited higher protein expression when Sxl function was reduced by RNAi ( Fig 3E ) . Further , the HA::Tdrd5lddel transgene exhibited an increased protein expression in the presence of Sxl function , and removing Sxl function caused no further increase in expression ( Fig 3E & S2 Fig ) . These data indicate that Sxl directly regulates the expression of Tdrd5l protein in the female germline . The male-biased expression pattern of Tdrd5l suggests it may have an important function in the male germline . Knocking down Tdrd5l function in the germline by RNAi , however , produced no observable phenotype . To conduct a more comprehensive study of Tdrd5l function we generated Tdrd5l mutant alleles using CRISPR-Cas9 genome editing . We generated several independent predicted null alleles of Tdrd5l ( S1 Fig ) , and analyzed male fertility and testis morphology of both young males and aged males . We determined that young ( 5 days old ) Tdrd5l mutant males have a 50% reduction in fecundity compared to controls ( Fig 4F ) , suggesting that Tdrd5l is required for proper male fertility . To characterize the germ cell defects that may lead to decreased fecundity , we evaluated several aspects of germ cell differentiation in young and aged mutant males . While testes of newly eclosed males appeared similar to wild-type , testes of older males ( 15–20 days old ) exhibited a dystrophic “skinny testis” phenotype with a dramatic reduction in the germline ( Fig 4A–4C ) . This was observed in 7% of animals raised at 25°C and 18% of animals raised at 29°C . Additionally , 15% of males exhibited a displaced hub phenotype ( Fig 4E arrow ) , where the hub is no longer located at the apical tip of the testis as seen in wild-type ( Fig 4D arrow ) . These combined phenotypes suggest a defect in proper germline differentiation and maintenance . Analyzing the expression of critical germ cell differentiation genes such as bam and spermatocyte arrest ( required for meiotic cell cycle progression ) [32] did not shed further light on the germ cell defect . Therefore , while the morphological defects present at a low penetrance , their overall effects culminate into a substantial reduction in fecundity; a phenotype which supports Tdrd5l importance in male germline development . Tudor domain-containing proteins have well known functions in small RNA pathways , transcriptional regulation , and the assembly of snRNPs ( reviewed in [33] ) . The closest Drosophila homolog to Tdrd5l is tejas , and the closest mammalian homolog to Tdrd5l is mouse TDRD5 . Both tejas and TDRD5 have been shown to function in the piRNA pathway and to repress the expression of transposons in the germline [25 , 26 , 34] . Interestingly , we found no changes in transposon expression in Tdrd5l mutants in our RNA-seq analysis . We also analyzed the expression of a wide variety of transposons by quantitative rtPCR and found little to no difference between wt and Tdrd5l-mutant testes . Defects in the piRNA pathway can also lead to increased accumulation of the Stellate protein in the male germline [35 , 36] . We examined the expression of Stellate in Tdrd5l mutant males , as well as in Tdrd5l mutant males also heterozygous for mutant alleles of tejas , ago3 or aubergine ( two PIWI proteins with key functions in the piRNA pathway ) . None of these testes showed the increased Stellate expression observed in tejas homozygous mutant testes ( S3 Fig , [26] ) . Therefore , while Tdrd5l’s activity is important for the proper development of the male germline , it has a distinct function from regulation of transposon expression . The sex-specific nature of Tdrd5l’s expression and mutant phenotype suggests it may play a role in promoting male germline sexual identity . However , unlike the male-specific Phf7 gene [20] , expression of UAS-Tdrd5l in the female germline did not , by itself , result in defects in the female germline . To further investigate Tdrd5l’s role in sexual identity , we decided to conduct our experiments using the sensitized genetic backgrounds frequently used for the investigation of genes involved in sexual identity . Females mutant for transformer ( tra ) —a key player in the somatic sex determination pathway—undergo a transformation so that the somatic gonad of XX tra mutants develops as male instead of female . However , because the germline is XX , and therefore incompatible with spermatogenesis , the germline of these testes is highly undeveloped , causing these animals to be sterile ( Fig 5B ) . A strong test of a gene’s ability to promote male identity in the germline is to determine whether it is sufficient to induce XX germ cells to enter spermatogenesis in these animals . Indeed , expression of Tdrd5l in the germline of XX tra mutants resulted in a robust rescue of spermatogenesis . 18% of these animals had highly developed testes that were wild type in size , containing all of the stages of germ cell differentiation up to spermatocytes ( Fig 5C , note: since these animals lack a Y chromosome and the spermatogenesis genes located there , they were not expected to be fertile ) . This is strong evidence signifying that Tdrd5l promotes male identity in the germline . Similarly , if Tdrd5l promotes male identity in the germline , we would expect that it would be able to enhance the ability of other mutations to masculinize the female germline . Homozygous mutants of ovarian tumor ( otu ) and sans fille ( snf ) have been shown to masculinize the female germline and cause germline tumors in ovaries , similar to Sxl-RNAi [37–40] , while females heterozygous for mutant alleles of otu and snf are fully fertile and have normal ovary morphology . However , ectopic expression of Tdrd5l in the germline of females heterozygous for otu or snf resulted in the formation of ovarian tumors . 25% of snf/+; nos > Tdrd5l ovaries have large , pervasive germline tumors similar to the homozygous snf mutants ( Fig 5D–5F ) . Another 25% of these ovaries show a complete loss of the germline ( Fig 5G ) , which phenocopies strong sex determination mutants [37 , 41] . Additionally , 40% of otu/+; nos > Tdrd5l ovaries exhibit either ovarian tumors or complete loss of germline . This evidence supports Tdrd5l as a male-promoting factor in the germline . We therefore conclude that Tdrd5l functions in germline sexual identity; it promotes male identity in the germline and is repressed by Sxl in the female germline .
It has been known for many years that Sxl is necessary for female germline identity [7 , 8] , and Sxl has also been shown to be sufficient to allow XY germ cells to undergo oogenesis [9] . It is likely that Sxl plays multiple roles in the germline , both to promote female identity in the early germline , perhaps as early as in the embryonic germline [9] , and in regulating the differentiation of the germline during oogenesis [42] . Here we examined the role of Sxl more specifically in the undifferentiated germline through the use of bam mutants . Principle component analysis indicated that , under these conditions , samples with Sxl function reduced in the germline clustered close to control female samples , and far from male samples . While some of the male/female differences may be contributed by the somatic cells present in these samples , we conclude that reducing Sxl function in the undifferentiated germline does not lead to a dramatic masculinization at the whole-genome level . In contrast , we propose that the role for Sxl in the early germline may be restricted to a relatively small number of changes in sex-specific germline gene expression that are important for female vs . male germline function . Recently , a genomic analysis of ovaries mutant for the RNA splicing factor sans fille ( snf ) was conducted [21] . This is considered to also be a Sxl germline loss-of-function condition as one important change in snf-mutant ovaries is a loss of Sxl expression and an ovarian tumor phenotype that can be rescued by Sxl expression [17 , 42 , 43] . In contrast to our results , an increased expression of spermatogenesis genes was observed in snf tumorous ovaries compared to wild type ovaries . It is likely that changes in these “differentiation” genes were not observed in our bam-mutant samples since germline differentiation is arrested at an earlier stage in bam mutants , allowing us to focus on the undifferentiated germline . Thus , these two analyses can help separate the role of Sxl in regulating early germline sexual identity vs . later aspects of sex-specific germline differentiation . Interestingly , one “differentiation” gene was identified in both RNA-seq analyses: the testis-specific basal transcription factor TATA Protein Associated Factor 12L ( Taf12L or rye ) . This may indicate that Taf12L could play a role in the undifferentiated germline as well as the later stages of spermatocyte differentiation . In addition , both analyses found evidence for differential regulation of the important male germline identity factor Phf7 [20] , where an upstream promoter is utilized preferentially in the male germline and is repressed downstream of Sxl in females [21] . This indicates a role for Phf7 in both the early and differentiating germline . Finally , we did not observe strong candidates for targets of alternative RNA splicing regulated by Sxl . The only strong candidate for alternative RNA splicing was the Sxl RNA itself , where the male-specific exon was retained in the residual Sxl RNA from the Sxl RNAi samples . This provides further evidence that Sxl autoregulation occurs in the germline as it does in the soma , as has previously been proposed [44] . It is likely that Sxl may also act at the level of translational control in the germline , as our evidence indicates here for regulation of Tdrd5l . Future experiments to identify Sxl-associated germline RNAs will be important for investigating this mechanism of action , as has recently been conducted [9] . In addition to its role in sex determination in the soma , Sxl also acts to initiate global X chromosome gene regulation and dosage compensation through translational control of male-specific lethal-2 [45] , and it is possible that Sxl plays a similar role in the germline . Whether or not the germline even undergoes dosage compensation is controversial , and thoughtful work has led to opposite conclusions [46 , 47] . Further , if dosage compensation does exist in the germline , it must utilize a separate mechanism from the soma , as the somatic dosage compensation complex members msl1 and msl2 are not required in the germline [48 , 49] . However , Sxl could retain an msl-independent role to regulate global X chromosome gene expression in the germline . We did not observe evidence for this . First , few X chromosome genes were differentially expressed between Sxl- and control samples ( 30 genes or 1 . 2% of X chromosome genes tested ) . Second , the ratio of average gene expression between X chromsome genes and autosomes was very similar in Sxl- females compared to control females ( X/A for controls: 1 . 24 , Sxl-: 1 . 19 ) and this was similar when considering only genes in particular expression categories ( e . g . all genes with some expression in both samples ) . Thus , it appears likely that Sxl’s role in the germline is distinct from that in the soma; it acts to control sex-specific gene regulation and sexual identity in both the germline and the soma , but acts as a general regulator of X chromosome gene expression and dosage compensation only in the soma . We show here that Tdrd5l is both a target for Sxl regulation and is important for male germline identity and spermatogenesis . Tdrd5l expression is highly male-biased , both at the RNA and protein levels ( Fig 2 ) . When female germ cells are sensitized by partial loss of female sex determination genes , expression of Tdrd5l exacerbates the masculinized phenotype in these germ cells ( Fig 5 ) . Significantly , expression of Tdrd5l is sufficient to promote spermatogenesis in XX germ cells present in a male soma ( XX tra-mutant testes , Fig 5 ) . Thus , Tdrd5l clearly has a role in promoting male identity autonomously in the germline , which must coordinate with non-autonomous influences from the soma for proper germline sex determination . Loss of Tdrd5l also has a strong effect on male fecundity , even though it is not absolutely required for spermatogenesis . The 50% reduction in fecundity is a strong effect and indicates that Tdrd5l plays an important role in the male germline . However , the fact that some spermatogenesis still proceeds suggests that other factors act in combination with Tdrd5l to control this process . One good candidate is Phf7 which we have previously demonstrated to have a similarly important , but not absolute , requirement for spermatogenesis [20] . However , our analyses of Tdrd5l , Phf7 double mutants did not reveal any synergistic effect on male germline development . This suggests that other important players in promoting male germline identity remain to be identified . Our data indicate that Tdrd5l regulates male germline identity by influencing post-transcriptional gene regulation . Other tudor-domain containing proteins have been shown to act in RNA-protein bodies to influence RNA stability and translational regulation [reviewed in 33] . Further , Tdrd5l localizes to cytoplasmic punctae , specifically a subset of the punctae that also contain Decapping Protein1 ( DCP-1 ) , suggesting that these bodies are related to Processing bodes ( P-bodies ) that are known to control post-transcriptional gene regulation [28 , 50] , ( reviewed in [29] ) . Interestingly , Tdrd5l’s closest homologs , Tejas in flies and TDRD5 in mice , have been shown to regulate piRNA production and transposon regulation [25 , 26] . Further , their localization to VASA-containing nuage is thought to influence transposon control [26 , 51] . However , we observe no changes in transposon expression regulated by Tdrd5l , and Tdrd5l does not co-localize with VASA in nuage . We have also not observed any genetic interaction between tejas and Tdrd5l . Thus , we propose that Tdrd5l plays a distinct role in regulating male germline identity and spermatogenesis , and this may be in the regulation of mRNAs rather than transposons . One possibility is that the role of mouse TDRD5 in transposon regulation and spermatogenesis has been separated into the roles of Tejas in transposon regulation and Tdrd5l in regulating male identity and spermatogenesis in flies . It is widely known that regulation of germline identity is dependent on post-transcriptional mechanisms involving tudor-domain proteins such as the original Tudor protein [52] , which helps define the germ plasm , an RNA body that regulates germline identity ( reviewed in [53] ) . Further , the regulation of sex-specific gametogenesis is also dependent on RNA bodies and their requisite tudor-domain proteins , such as TDRD5 in mouse [54] , ( reviewed in [27] ) . Our studies indicate that initial germline sexual identity is similarly regulated by post-transcriptional mechanisms , including RNA bodies containing other , distinct , TUDOR-domain proteins such as Tdrd5l .
Gonads were dissected from 1–3 day old flies raised at 25°C . Ovaries were dissected from virgin females . 3 biological replicates were dissected for each genotype . Total RNA was isolated from all genotypes using RNA-bee ( Tel-Test ) . Contaminating DNA was removed from the RNA using Turbo-DNA-free ( Ambion ) . 200ng of RNA was used to prepare each library using the illumina TruSeq RNA Library Prep kit v2 . 100bp paired-end read sequencing was done by the Johns Hopkins Genetic Resources Core Facility . The bam mutant male and female libraries were sequenced in one lane and the Sxl-RNAi , control-RNAi libraries were sequenced in separate lane , therefore having 6 libraries per lane . Quality of raw reads was assessed using the fastQC kit ( Babraham Institute ) . RNA-Seq reads were mapped to the Drosophila genome using Ensembl BDGP6 release 85 , and Bowtie 2 . 2 . 9 , TopHat 2 . 1 . 1 , and HTSeq 0 . 9 . 1 [55–57] . Differential gene expression analysis was done using DESeq using ensemble annotation BDGP6 [22] . Adjusted P value of 0 . 05 used for significance cutoff . Differential exon analysis was done using DEXSeq [19] . The fly stocks used were obtained from Bloomington Stock Center unless otherwise indicated . bam1 [58] , bamΔ86 ( BDSC# 5427 ) , nos-Gal4 [59] , the control RNAi used was p{VALIUM20-mCherry}attP2 ( BDSC# 35785 ) , uas-Sxl-RNAi = TRiP . HMS00609 ( BDSC# 34393 ) , uas-CG15930-RNAi = TRiP . GL01046 ( BDSC# 36882 ) , Snf148 , otu17 , tej48-5 , attP40{nos-Cas9} ( NIG-FLY# CAS-0001 ) , YFP:dDCP1 ( a kind gift from Ming-Der Lin , [28] ) . Fecundity tests were carried out by setting up crosses with one Tdrd5l mutant male and 15 virgin females of the control stock . The control stock used is nos-Cas9 isogenized to FM7KrGFP fly stock to reproduce the treatment of the Tdrd5l mutant fly lines while screening for transformants . Each male was mated with virgin females for 4 days . Females were then discarded and each male was placed with another 15 virgin females in a new bottle . This was repeated twice more for a total of four mating bottles per male . All offspring were counted by day 18 . Total offspring per male was calculated by averaging the number of offspring from each of the four mating bottles for each male . Adult ovaries and testes were fixed , blocked and stained as previously described [60] . All images were taken with a Zeiss LSM 510 confocal microscope . Primary antibodies and the concentrations used are as follows: chicken anti-Vasa 1:10 , 000 ( K . Howard ) ; rabbit anti-Vasa 1:10 , 000 ( R . Lehmann ) ; mouse anti-Sxl 1:8 ( M18 , DSHB ) ; mouse anti-Armadillo 1:100 ( N2 7A1 , DSHB ) ; rat anti-HA 1:100 ( 3F10 , Roche ) ; guinea pig α-TJ ( 1:1 , 000; generated by J . Jemc using the same epitope as previously described [61] ) ; mouse anti-HTS 1:4 ( 1B1 , DSHB ) . DSHB: Developmental Studies Hybridoma Bank . Secondary antibodies were used at 1:500 ( Alexa-fluor ) . Samples were mounted in vectashield mounting solution with DAPI ( vector Industries ) . For RT-PCR and qRT-PCR , total RNA was isolated from ovaries and testes using RNA-bee ( Tel-Test ) . Contaminating DNA was removed from the RNA using Turbo-DNA-free ( Ambion ) . RNA was converted to cDNA using Superscript II ( Invitrogen ) . qRT-PCR was done using 2 biological replicates and in technical triplicate , using SYBR green detection . In-situ hybridization was carried out as previously described [62] . DIG-labelled sense and antisense probes were synthesized by in vitro transcription of PCR product generated from RP98-1M22 BAC ( BACPAC Resources Center ) . Mutant alleles of Tdrd5l were created using CRISPR-Cas9 mediated genome editing . Small guide RNA ( sgRNA ) was designed and cloned following the Perrimon lab protocol [63] , using the U6b-sgRNA-short vector described therein . The sgRNA was injected by Best Gene inc . into nos-Cas9 ( II-attP40 ) flies . The HA::Tdrd5l transgenic flies were generated by BAC recombineering [64 , 65] , using the CH322-188C18 BAC obtained from the BACPAC Resources Center . A 3xHA epitope tag was added to the N-terminus of the gene ( S1 Fig ) . This construct was also modified to delete the Sxl binding sites in the intron and the 3’UTR . The Sxl binding site in Tdrd5l 3’UTR was deleted using QuickChange II site-directed mutagenesis kit ( agilent ) . The intronic binding site was removed by deleting the entire intron . | Like humans , all sexually reproducing organisms require gametes to reproduce . Gametes are made by specialized cells called germ cells , which must have the correct sexual identity information to properly make sperm or eggs . In fruit flies , germ cell sexual identity is controlled by the RNA-binding protein Sxl , which is expressed only in females . To better understand how Sxl promotes female identity , we conducted an RNA expression profiling experiment to identify genes whose expression changes in response to the loss of Sxl from germ cells . Here , we identify Tudor domain containing protein 5-like ( Tdrd5l ) , which is expressed 17-fold higher in ovaries lacking Sxl compared to control ovaries . Additionally , Tdrd5l plays an important role in males as male flies that are mutant for this gene cannot make sperm properly and thus are less fertile . Moreover , we find that Tdrd5l promotes male identity in the germline , as several experiments show that it can shift the germ cell developmental program from female to male . This study tells us that Sxl promotes female identity in germ cells by repressing genes , like Tdrd5l , that promote male identity . Future studies into the function of Tdrd5l will provide mechanistic insight into how this gene promotes male identity . | [
"Abstract",
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"... | 2019 | Tudor-domain containing protein 5-like promotes male sexual identity in the Drosophila germline and is repressed in females by Sex lethal |
The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication . Although the genomic revolution appears to have had little impact on this problem , and might even have exacerbated it because of the flood of additional and usually ineffective leads , the emergence of high throughput resources promises the possibility of rapid , reliable and systematic identification of approved drugs for originally unintended uses . In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer , myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds . The method uses variable gene signatures to identify bioactive compounds that modulate a given disease . This is in contrast to previous methods that use small and fixed signatures . This strategy is based on the observation that diseases stem from failed/modified cellular functions , irrespective of the particular genes that contribute to the function , i . e . , this strategy targets the functional signatures for a given cancer . This function-based strategy broadens the search space for the effective drugs with an impressive hit rate . Among the 79 , 94 and 88 candidate drugs for breast cancer , myelogenous leukemia and prostate cancer , 32% , 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs , or drugs with suggestive literature evidences , with an FDR of 0 . 01 . These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development , and has potential application for the personalized medicine .
The average research and development ( R&D ) cost for the 10-odd years to develop a new pharmaceutical now exceeds a billion dollars [1] , [2]; anti-cancer drugs being especially costly [2] . The process encompasses compound identification , toxicity testing in animals , early phase clinical trails , and efficacy in late phase trials . The failure of more than 90% of drugs during development [1] , is perhaps the single greatest contributor to overall cost of pharmaceutical R&D . This cost in time and money can in principle be substantially reduced by repositioning drugs that are already approved for other purposes . One way to screen approved drugs for new purposes is computationally . Computational chemistry provides valuable contributions in hit- and lead-compound discovery [3] . Systems biology approaches have also been recently used to capture the complexity of drug discovery and repositioning [4] , [5] . Computational approaches have rarely , however , been a key contributor to drug discovery or repositioning [3] . This is in part because the majority of the studies focus only a few genes/proteins [6] , either as the drug targets , or “disease signatures” while there is increasing evidence that many effective drugs act on multiple rather than single targets [4] , and evidence is starting to emerge that pathologies can be a consequence of small abnormalities in many genes , rather than major abnormalities in a few genes [7] , [8] . In addition , many existing methods constrain search space by imposing similarity requirements–including similarity of ligand structures [9] , expression profile of drug response [10] , topological similarity of target-drug , drug-drug and disease-drug [11] , [12] networks , and side-effect similarity [13] , which diminishes the effectiveness of de novo drug discovery . The main idea underlying a number of current methods , including the one presented here , is to identify genes whose expressions are reverse correlated under disease and drug perturbations [14] , [15] , [16] . Our approach , however , uses functional signatures rather than gene signatures . Ideally a functional signature would be represented by pathways or other functional modules that are perturbed by the disease and restored by drugs . The utility of such a definition is limited by lack of a comprehensive set of functional modules/pathways . We therefore adapted an alternative approach that identifies a drug for repositioning when the reverse ordered lists of disease perturbed and drug perturbed genes has a statistically significant overlap . We thereby remove the requirement for representing a disease by a fixed number of genes . Because we use a large number of genes in our analysis , we filter out genes that are expressed differently between untreated cell line and disease samples; a step that is generally not present in gene signature based methods . Our approach allows the detection of heterogeneous drug candidates that may restore cellular functions through different paths , in keeping with the idea that drugs acting selectively on multiple targets may be more efficacious than single-target agents , and that a particular physiological process may be modulated by multiple paths . This is in contrast to other approaches which either use a fixed small number of genes as the disease signature [14] , [15] , [16] or limit candidates to drugs whose properties ( such as expression profiles ) are similar to those of existing drugs [9] , [10] , [11] , [12] , [13] . As with other approaches [14] , [15] , [16] we utilize two databases: the Connectivity Map ( CMAP ) which provides information on expressed genes in cancer cell lines perturbed by bioactive compounds [6] , [14] , and the Gene Expression Omnibus ( GEO ) [17] , which stores transcript levels for various cancers . We consider as potential candidates , compounds that down ( up ) -regulate cell-line genes which are up ( down ) -regulated in transformed tissue cells . We use a three-step strategy to identify candidate compounds . First , we compare the expression of genes in the untreated cell line and the cancer tissue sample , and retain genes that are expressed in both . Second , we download the ranked list of perturbed cell line genes from CMAP , and generate a ranked list of genes from tissue samples ranked by differential expression . Both steps are designed to make the expression data comparable between cell lines and cancer samples . Finally , as shown in Fig . 1 , we compare the K ( window size ) most up-regulated genes in the tissue ( UC ) against the K most down-regulated genes in the cell line list ( DB ) , for each compound . We assume a compound is a candidate for repositioning if there is significant number of overlapping genes between UC and DB , and vice versa . We illustrate that this new strategy with database integration and straightforward statistical analysis is able to identify a remarkably large number of plausible candidates for myelogenous leukemia , prostate and breast cancer . Of the more than 1300 CMAP compounds , 4 are currently in use against breast cancer , 5 against myelogenous leukemia and 3 against prostate cancer . Our analysis returned 1 of the 4 , 2 of the 5 and 1 of the 3 . The relative plausibility of the candidates is further indicated by the fact that 11/45 , 5/50 and 6/50 candidates for repositioning against breast cancer , myelogenous leukemia and prostate cancer , respectively , are currently in clinical trials for those diseases , these statistics summarizing the most important indicators of performance . These results not only demonstrate the effectiveness of the approach , but also hint the potential application of the approach for the personalized medicine by reverse-correlating of patient's expression profile against the expression profiles of all available drugs , as detailed in the discussion section .
One distinguishing feature of our method is that it targets cellular functions rather than genes , i . e . , the focus of the method is to bring abnormal functions associated with disease back to the normal state . This strategy is based on the observation that diseases stem from failed/modified cellular functions , regardless of which of the particular genes contributing to the function are aberrant [19] . For the purpose of finding therapeutics , we do not have a fixed list of signature genes for a given disease . Instead from a large set of ranked differentially expressed genes for a particular disease , we find compounds whose effect on the expression of most perturbed genes is opposite that of the disease . This results in a number of overlapping but different ( for different compounds ) subsets of genes . On the other hand , for a particular disease the functions associated with the subsets are similar . This characteristic of variability at the level of genes , with conservation at the level of function can be partially seen in Table S2 , S3 where for each drug candidate the list of genes is very different while the list of pathways is similar . We used mRNA expression as a surrogate measure of the functional change because of its wide availability either for drug response or disease perturbation . The method is , however , applicable to other data types ( protein expression , methylation and so fourth ) . Since our method focuses on functional recovery and identifying different but overlapping subsets of genes for different compounds , it can cover potential drugs with heterogeneous properties . On the other hand , we do find genes that are targeted by a large number of our identified compounds . For example , LAMB1 , CAV1 and RPL35 , tend to be targeted by most of predicted drugs for breast cancer as shown in Table S2 . The mechanisms and range of action of many current drugs are poorly understood . Even drugs with known targets often have “off-target” effects [5] . While many such effects are undesirable , some of them provide the opportunity for repositioning . We have used pathway analysis to interpret the functional rationale for repositioning . The same analysis also provides some understanding mechanism . As an example , consider Tamoxifen , which is used extensively for the treatment of both early and advanced estrogen receptor positive ( ER+ ) breast cancer [46] . Our results indicate that tamoxifen is a candidate for repositioning to myelogenous leukemia . In particular , the overrepresentation of genes in this pathway , which are upregulated in myelogenous leukemia , and down-regulated by Tamoxifen suggests the possibility that aberrant TGF-β signaling plays a role in myelogenous leukemia . Since TGF-β production is down-regulated by tamoxifen in other tissues [47] , tamoxifen might function as an anti-myelogenous leukemia drug by repressing this pathway ( Table S3 ) . This suggestion is supported by the fact that expression of estrogen receptors ESR1 and ESR2 is relatively unaffected by treatment with Tamoxifen ( of the 20 , 469 ranked genes , ESR1 and ESR2 ranked 4184 and 4734 respectively – well below the number of top ranking genes used in the study: 700/800 for UC/DB and DC/UB ) . Consequently it seems unlikely that the effect of Tamoxifen on leukemic cells is mediated by these receptors . We therefore speculate that tamoxifen acts similarly in breast cancer , and thereby exerts its effects in a dual manner; i . e . through inhibition of TGF-β , in addition to inhibition of estrogen . Militating against this possibility are the facts that the TGF-β pathway is not over-represented in UC/DB transcripts , and other investigations did not find evidence for the regulation of TGF-β genes/proteins by tamoxifen in breast cancer patients [48] . On the other hand an increased expression of TGF-β1 , which is often seen in tumors of breast cancer patients , correlates with poor prognostic outcome [49] . This apparent conflict might be resolved by the recent discovery that tamoxifen decreases extracellular TGF-β1 proteins secreted from breast cancer cells , but not intracellular ones [50] . This result is also compatible with our finding that the adherens junction and focal adhesion pathways are both over-represented in breast cancer cells , and these pathways are potentially inducible by TGF-β [37] . These observation are in line with other studies documenting decreased metastasis when TGF-β signalling is blocked in high-grade breast tumor [51] , and suggest that tamoxifen represses the metastasis of breast cancer cells by down regulating the TGF-β pathway and preventing loss of polarity and cell–cell contacts . Taken collectively , the functional analysis of our results suggests a potential mechanism for tamoxifen , which is independent of an interaction with the estrogen receptor , and has tamoxifen suppressing tumor metastasis and growth by down-regulating TGF-β signaling . Our results also suggest that some exploration of the identified non-FDA approve drugs ( new drug candidates ) could be fruitful . If the fraction of FDA approved drugs in clinical trials is taken as a measure of what is worth exploring ( i . e . we conservatively neglect other supporting evidence ) , then we'd expect 8 of the 34 non-FDA approved drugs for breast cancer to be ultimately worthy of clinical trials; and 4 of the 44 for myelogenous leukemia and 5 of the 38 for prostate cancer ( i . e . we'd expect this number to get through animal toxicity tests , and efficacy tests when available , and enter phase 1 trials ) . There are several issues that may limit the future development of the approach . First , the optimization of the window size requires availability of the known FDA-approved drugs in CMAP , which may not always the case especially when expanding this approach to the other diseases that are functionally close to the three cancers . Second , the sensitivity of the approach to the subtype , or the different stage , of the same disease needs to be studied further . The approach will have great application to the personalized medicine if it is able to identify different drugs for the disease at different stages/subtype because the relative cheap price to get the patient expression profile . Finally , although mRNA expression is used to measure the functional change of the cell , we expected the better results using the other data that may be more representative of the cellular functions , such as protein expressions .
Expression data in response to bioactive compounds for breast cancer , prostate cancer and myelogenous leukemia cell lines were obtained from the connectivity map ( http://www . broad . mit . edu/CMAP/ ) ( Build 02 ) [6] , [14] . Differential expression data in response to breast cancer ( GDS2617 ) , leukemia ( GDS2908 ) , and prostate cancer ( GDS1439 ) were obtained from the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO ) [17] . The data sets are picked in such a way that there is fairly big number of samples and the expressions are normalized by GEO database . The ranked list of differentially expressed genes for a given cancer is calculated using t-statistic . The bioactive compound specific signatures fetched from CMAP are based on cell lines ( i . e . cancerous cells with and without treatments ) , while those from GEO were based on tissue cells ( i . e . normal and cancer tissue cells ) . Since the different cell types are not directly comparable , we first normalized gene-expressions according to the untreated cell line and the cancer tissue samples . We retain only genes that are expressed in both tissue and cell line . In particular we applied the t-test to the normalized scores , and calculated the corrected p-values for multiple testing by a false discovery rate ( FDR ) procedure . The FDR is defined as the expected proportion of false positives among the significant results and is a more appropriate measure than the raw p-value for multiple hypotheses testing . The FDR threshold was set as 0 . 01 , and the genes with clearly different gene-expressions were removed from both samples . As a result , we retained 15572 genes ( 77% ) , 20469 genes ( 92% ) , and 12220 genes ( 55% ) for breast cancer , myelogenous leukemia , and prostate cancer , respectively . We prepared two types of ranked lists of genes . One was generated from tissue samples ranked by differential expression between normal and cancer tissues from GEO data . The other was obtained from the ranked list of perturbed cell line genes from CMAP . In the former case , the top and bottom k genes were defined as up-regulated genes in cancer ( UC ) and down-regulated genes in cancer ( DC ) . In the latter case , the top and bottom k genes were defined as up-regulated genes by bioactive compounds ( UB ) and down-regulated genes by bioactive compounds ( DB ) . The genes of interest are the top and the bottom k genes in a ranked list where k ranges from 100 to 10000 in increments of 100 . We counted overlapping genes in between UC and DB ( UC/DB ) and in between DC and UB ( DC/UB ) to investigate compounds up-regulating down-regulated cancer genes ( DC/UB ) , or down-regulating up-regulated cancer genes ( UC/DB ) . We performed the Fisher's exact test to prove if the overlap is significant by comparing the number of overlapping genes to that of randomly selecting genes ( background ) . The p-value was transformed into FDA corrected for multiple hypotheses . The FDR threshold was set as 0 . 01 . For each value k , a compound is labeled as bioactive if the number of overlapping genes ( as explained in Fig . 1 ) is statistically significant . The sensitivity and specificity were calculated by measuring the proportions of true positives ( fraction of FDA drugs identified ) and true negatives ( fraction of identified compounds that failed clinical trials ) . For each cancer , we chose values of k ( one for UC/DB and one for DC/UB ) that gave maximum specificity , subject to the constraint of non zero sensitivity ( at least 1 correct prediction ) , non zero duality and a FDR less than 0 . 01 . In this way we identified for further investigation , a total of 90 compounds ( and associated genes ) for breast cancer ( 28 suppressors of up-regulated cancer genes; 62 enhancers of down-regulated genes ) ; 36 compounds for myelogenous leukemia ( 10 suppressors; 26 enhancers ) , and 171 compounds for prostate cancer ( 83 suppressors; 88 activators ) . The results regarding different window size are presented in Table S7 and Fig . S1 . We mapped correlated genes in UC/DB and in DC/UB onto the KEGG pathways and counted the number of genes mapped and total number of existing genes with respect to each pathway . Given the number of genes and total number of all of genes we used , a p-value is calculated with hypergeometic distribution [52]; we accepted only pathways with the p-values below 0 . 05 as over-represented pathways [53] . We collected data from KEGG DRUG Database ( http://www . genome . jp/kegg/drug/ ) , DrugBank ( http://www . drugbank . ca/ ) and PharmGKB ( http://www . pharmgkb . org/ ) to map International Nonproprietary Name ( INN ) to generic names and alias . FDA approved drugs were found from FDA service: Drugs@FDA . All clinical trials data and references that we checked for our predictions were shown in Table 2 and Table S1 with corresponding hyperlinks . | The effective drug of a given disease is aimed to bring abnormal functions associated with disease back to the normal state . Using expression profile as the surrogate marker of the cellular function , we introduce a novel procedure to identify candidate therapeutics by searching for those bioactive compounds that either down-regulate abnormally over-expressed genes , or up-regulate those that are abnormally under-expressed . We show that the approach detects a pool of plausible candidates as repositioning/new drugs . In contrast to previous studies , our approach uses a variable big number of genes and/or gene combinations as a representation of functional signatures to identify bioactive compounds that modulate a given disease , irrespective of the particular genes that contribute to the cellular functions; therefore it covers potential drugs with heterogeneous properties . The method may also have potential application for the personalized medicine . | [
"Abstract",
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] | [
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] | 2012 | Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer |
In the presence of exogenous mortality risks , future reproduction by an individual is worth less than present reproduction to its fitness . Senescent aging thus results inevitably from transferring net fertility into younger ages . Some long-lived organisms appear to defy theory , however , presenting negligible senescence ( e . g . , hydra ) and extended lifespans ( e . g . , Bristlecone Pine ) . Here , we investigate the possibility that the onset of vitality loss can be delayed indefinitely , even accepting the abundant evidence that reproduction is intrinsically costly to survival . For an environment with constant hazard , we establish that natural selection itself contributes to increasing density-dependent recruitment losses . We then develop a generalized model of accelerating vitality loss for analyzing fitness optima as a tradeoff between compression and spread in the age profile of net fertility . Across a realistic spectrum of senescent age profiles , density regulation of recruitment can trigger runaway selection for ever-reducing senescence . This novel prediction applies without requirement for special life-history characteristics such as indeterminate somatic growth or increasing fecundity with age . The evolution of nonsenescence from senescence is robust to the presence of exogenous adult mortality , which tends instead to increase the age-independent component of vitality loss . We simulate examples of runaway selection leading to negligible senescence and even intrinsic immortality .
Senescence , usually treated as synonymous with “aging , ” refers to a deterioration in physiological condition with age , manifest as an increase in mortality and a decline in fertility . Since this phenomenon is detrimental to reproductive success , natural selection might be expected to cause its postponement or elimination from the life history of organisms . Its apparent ubiquity in the natural world , therefore , has been treated as a challenge for evolutionary theory [1] . There is a general acceptance that this challenge has , in principle , been met , and modern understanding of the widespread occurrence of senescence in nature has been hailed as one of the great triumphs of evolutionary thinking [2] . Discussions of the evolution of senescence broadly follow one of two paradigms , based either in classical population genetics or in physiological ecology [3] . The first emphasizes the accumulation of late-acting deleterious mutations on hypothesized genes with age-specific expression [1 , 4] . More generally , this paradigm hypothesizes an antagonistic pleiotropy of age-specific genes in which mutations confer a fitness benefit early in life at the cost of some deleterious effect later [4–6] . The key insight of this perspective is that the force of selection on the additive component of genetic variance necessarily declines with age , so that an early-age cost is more strongly selected against than an equivalent late-age cost , and , a fortiori , an early-age benefit more than compensates for a late-age cost [4] . Hamilton concludes: “ . . . for organisms that reproduce repeatedly , senescence is to be expected as an inevitable consequence of the working of natural selection” [4] . This population genetic analysis has been challenged recently both theoretically and empirically . First , a declining force of selection is only guaranteed for mutations with additive effects , and it has been suggested that mutations with proportional effects—for which the force of selection need not decline with age—may be more relevant [7] . Second , Hamilton himself concedes: “To what extent and in exactly what way life schedules will be moulded by natural selection depends on what sort of genetical variation is available” [4] . Thus , the more such genes there are , the more evolutionary pressure there will be toward compressed life histories . However , despite much empirical work over several decades , evidence for the availability of genes with the necessary age-specific effects appears to be thin ( reviewed in [8] ) . In contrast to the classical population genetic approach , the “disposable soma” theory [3 , 9–11] is based firmly within physiological ecology . Thus , it is claimed that birth and death schedules are the result of the action of integrated physiological processes concerned with the optimal partitioning of available resources between reproduction and somatic maintenance or growth . In particular , there is an inherent “cost of reproduction” in which an early-age reproductive benefit incurs a late-age cost in decreased survival , possibly in the form of latent damage that is only unmasked later in life [8] . Relevant genetic mutations must have effects that are manifest at this physiological level . This is potentially a much more constraining paradigm , though apparently more strongly supported by current evidence [8 , 12] . Nevertheless , given this tradeoff between early fecundity and longevity , it has again generally been concluded that senescence is inevitable [3] . In particular , immortality has long been considered theoretically impossible because of the inevitability of senescent aging [13–16] . Yet this understanding of the evolution of senescence fails to account for organisms showing negligible or even negative senescence [16 , 17] . These are species such as the freshwater Hydra vulgaris [18] with period survival that remains constant or increases with adult age . They include some organisms with apparently indefinite lifespans such as the Great Basin Bristlecone Pine Pinus longaeva [19 , 20] , which continues producing viable cones at well over 4 , 000 y old , the Quaking Aspen Populus tremuloides [21] , and the Creosote Bush Larrea tridentata [22] , both of which have clonal clusters at least 10 , 000 y old . These and other examples [16] have recently led to a reversal of the traditional perspective in which the problem was to explain the evolution of senescence from nonsenescence . On the contrary , given the ubiquity of senescence in nature , and the abundance of explanations for its presence , it seems very unlikely that the majority of today's organisms are descended from nonsenescent ancestors ( even bacteria exhibit senescence [23] ) . Rather , an important issue now is to provide an evolutionary account for those organisms that appear to exhibit little or no senescence , but which almost certainly have evolved from ancestors that did exhibit senescence . Rising to this challenge , a theoretical analysis of the costs of senescent aging [24] has shown that , although senescence is often favored by a high and sustained early vitality ( a measure of intrinsic net fertility ) , nonsenescing strategies are locally optimal if vitality loss in the presence of senescence would otherwise be sufficiently fast . Similarly , an optimization model [17] has shown how negative senescence can evolve for species that grow in body size throughout their lives , if this growth carries proportionate benefits in increasing reproductive output and decreasing mortality . Both these analyses are concerned with the optimal tradeoff between fecundity and mortality , and so lie within the disposable soma paradigm . These analyses have not modeled density dependence , except implicitly as a limiting case in which population growth is set to zero . In this paper , we construct explicit models , within the disposable soma paradigm , for a very general class of organisms including those without indefinite somatic growth . These reveal density dependence in recruitment as a sufficient driver for the evolution of nonsenescent life histories from senescent ancestors . Density-limited recruitment sets up a balance of opposing selective forces that underpins the direction of evolution toward either compressed ( shorter and faster ) or spread ( longer and slower ) reproductive life . Thus , on the one hand , future reproduction is worth less than present reproduction to an individual's fitness , given a future extrinsic mortality risk [1] . On the other hand , future reproduction by an individual's mature offspring may be worth more to its inclusive fitness than its own present reproduction , if otherwise viable offspring face an extrinsic mortality risk before recruitment . The crucial advance that we make is prefigured by Abrams [25] , who showed that faster senescence is favored by positive or zero density-independent growth , and also by density-dependent adult mortality , whereas slower senescence requires density-dependent fecundity . Our advance on his analysis is to show how the slower senescence can take the form of a runaway selection to negligible senescence , and even intrinsic immortality . Indeed , density-dependent recruitment reflects the widely prevailing ecological condition of bottom-up regulation in crowded habitats . We show that it is unwise either to ignore it , or to represent it only implicitly as zero population growth , because of its ubiquity in nature and its significant consequences for the evolution of senescence . Here , we perform an optimization analysis of vitality evolution as a fitness tradeoff between compression into earlier life and spread into later life in the context of density-dependent recruitment , which accords with the abundant evidence that reproduction is intrinsically costly to survival [8 , 13 , 15 , 26–28] . For populations at recruitment-regulated equilibrium , we demonstrate generic conditions under which natural selection itself increases the extrinsic recruitment losses , by successive genomic invasions increasing the level of crowding within the population . Stronger density dependence means fewer recruitment opportunities into the adult population and , therefore , a natural selection that is weighted toward maximizing generation length over early-age vitality . This positive feedback leads to the novel result that density regulation can trigger selection for ever-reducing senescence . We develop a model that shows the potential for runaway selection of reduced senescence to arise across a wide range of age-specific vitality profiles , including accelerating loss from an early or late onset , and constant aging ( concordant with Deevey types I and II survivorship ) . We find that natural selection can favor evolution of nonsenescence , and even immortality , from senescence in the presence of exogenous mortality , without a requirement for special life-history characteristics such as increasing intrinsic fecundity with age ( cf . [17] ) . Simulations of this process are given for various scenarios , including stochastic environments . The first four Results sections develop our analytical framework . Section 1 outlines the assumptions we make about the action of density-dependent recruitment . Section 2 specifies precisely the relation between the concepts that we use of vitality and senescent and nonsenescent aging . Our approach is to define these concepts in terms of “instantaneous rates” ( affecting a combination of mortality and fecundity; cf . [29] ) , rather than on the rate of change of age-specific reproductive value ( e . g . , [30] ) . The section Model: Invasion of Mutations outlines our assumptions concerning the effects of mutations on the key life-history parameter controlling the rate of senescence , and states our main result concerning the possibility of evolution from a highly compressed life history to a highly spread life history . The section Example describes a specific example of evolution from positive senescence to non- ( or even negative ) senescence . Finally , the section Simulations outlines stochastic simulations of the model . Supporting material is provided in the Methods section and in Text S1 . We conclude with a discussion of the model predictions for life-history conditions and biotic environments that favor negligible senescence .
Consider a population of organisms in which juveniles reach reproductive maturity at age a = am . For ages a ≥ am , write t = a − am for the relative age of an adult . Thus , individuals of age t = 0 are newly recruited adults . The total population density of adults is N . We assume that density-dependent effects have consequences for the juvenile phase as follows . Density dependence is assumed to act only through adult density on recruitment into the adult population , with the density of juveniles having no impact . For example , juveniles may be plant seeds , or small and mobile ( e . g . , planktonic ) , whereas adults are sessile and confined to specialized habitats . Density dependence then acts through one or both of two routes . Assumptions 1 ) –3 ) describe its possible action on juvenile survival . If there is no density dependence , juveniles survive from birth to maturity with maximum positive probability f . If density dependence acts , however , juvenile survival is reduced with increasing adult population via the decreasing function ℓ ( N ) . This occurs in recruitment-limited populations , for example , if adults occupy a high proportion of potential recruitment sites . Assumptions 4 ) –6 ) describe the possible action of density dependence on adult fecundity . Thus , if there is no such action , age-dependent adult birth rates are given intrinsically . If there is a density-dependent effect , it is assumed to act uniformly on all adult age classes through the age-independent decreasing function β ( N ) . For example , competition between increasingly many adults for limited nutritional resources may lead to a decrease in metabolic capacity available for reproduction beyond what is required for somatic maintenance . It is unlikely in reality that such effects would be age independent , but we assume this to be approximately the case for the sake of technical simplicity . Assumption 7 ) says that at least one , and possibly both , of these two density-dependent effects operates , and that their combined effect has the potential to reduce adult recruitment to zero at sufficiently high adult population densities ( at which point the population reaches its carrying capacity K ) . This clearly has a self-limiting effect on adult population size . Finally , for technical simplicity , we assume that there is no density-dependent effect on adult mortality ( assumption 8 ) ) . These assumptions restrict the action of density dependence to a net effect on the recruitment of juveniles to the adult population . We focus on density-dependent recruitment because it is a prevailing ecological condition , and because others have previously shown it to favor slower senescence [25] and longer lifespan [31] . We suspect that natural selection may respond to the wastage of offspring that have low recruitment probability by spreading out the production over a longer lifespan . Since fitness-raising mutations raise adult carrying capacity , they always intensify this wastage in density-dependent juvenile losses , leading us to hypothesize the possibility of a runaway process . The probability of survival of adults from maturity to age t ≥ 0 is: which is density-independent . Suppose the population is at ( age-structured ) equilibrium , with equilibrium adult population density N* . Then it is easily shown that the equilibrium Euler-Lotka equation holds: Write bt = . Then , bt is the density-independent rate of recruitment of new adults ( age t = 0 ) whose parent was of age t when they were born . In view of assumptions 1 ) –7 ) , we may therefore write Equation 2 in the form: where F ( N ) = ℓ ( N ) β ( N ) is a decreasing function of N satisfying F ( 0 ) = 1 , and F ( N ) → 0 as N → K . R is the expected lifetime reproductive success ( ELRS ) of an adult in the absence of density effects ( which act only through juveniles ) . For a positive equilibrium population density N* , we have F ( N* ) < 1 , and hence R > 1 from Equation 3a . The larger N* is , the larger R must be to sustain Equation 3a . That is , equilibrium population density is an increasing function of R . Intrinsic characteristics determine the organism's ability to effect somatic and genetic repair , and generally to maintain its condition in the face of challenges inherent in its genetic makeup and environment . Extrinsic characteristics , which generally influence mortality , are determined by residual features of the environment , such as fluctuations in resource availability , a sudden spike in predator numbers , and various possible accidents such as floods or drought , etc . We regard an organism as susceptible to cumulative damage throughout its life , such as damage to DNA from free radicals [32] , but also as having some capacity to repair this damage . In particular , we regard growth and development as a process in which an organism not only can repair the damage it sustains , but also has excess “capital” to invest in the development of additional soma . The rate of “senescence” can be thought of as the net rate at which damage accumulates . Thus , senescence sets in when the organism can no longer repair all the damage it sustains , and physiological deterioration results: that is , rate of damage accumulation is greater than rate of repair . Negative senescence is the opposite: rate of damage accumulation is less than rate of repair . In this case , somatic growth leads either to increased reproductive output , or decreased mortality , or both ( cf . [17] ) . A classic life-history trajectory begins with a new adult organism exhibiting either no or negative senescence ( increasing size ) , which later declines with age until accumulated damage outstrips the organism's capacity for repair , and ( increasing ) senescence sets in at older ages . To formalize these points , we first decompose mortality into intrinsic and extrinsic components: where we have assumed ( for simplicity ) that the extrinsic mortality rate g is constant . For fecundity , we write where is the birth rate of newly recruited adults ( age t = 0 ) , and mt is the intrinsic relative fecundity of adults of age t . Clearly , m0 = 1 , and more generally , mt determines the proportion of new-adult fecundity that is retained by adults of age t . ( This may be greater than one if fecundity of adults increases with age—a negative senescence effect . ) Multiplying both sides by the density-independent juvenile survival rate f gives a decomposition of adult recruitment rates: bt = b0mt . Following [24] , we combine these characteristics and their interactions in the notion the intrinsic relative vitality of adults of age t , defined by: Clearly , v0 = 1 , and more generally relative vitality measures how well an organism of age t > 0 can be expected to survive and reproduce relative to its performance as a new adult ( age t = 0 ) . The ELRS Equation 3b can now be written in the form: Now define two associated quantities . First , the age-specific rate of loss of vitality: This allows us to write: If ϕt > 0 , then relative vitality vt declines with age . This is to be expected due to the inherent cost of reproduction—any increase with age in relative fecundity is assumed to be more than offset by an intrinsic mortality cost . Senescence is the familiar accelerating loss of vitality that results from damage accumulation exceeding repair . For early-adult ages , we will also allow the possibility of decelerating vitality loss when repair exceeds damage accumulation , which is negative senescence . The point of inflection between positive and negative senescence is nonsenescent aging when repair just matches or counterbalances damage accumulation . This condition of sustenance ( sensu [29] ) results in constant vitality loss with age . A Deevey Type II ( linear ) survivorship profile resulting from constant intrinsic mortality is an expression of nonsenescent aging . Even in the absence of senescence ( ϕt constant ) , the organism retains a finite intrinsic lifespan determined by aging ( ϕt > 0 ) . Only if there is no such aging does the organism become intrinsically immortal . We recognize that evolutionary theory has generally treated “aging” and “senescence” as synonyms . Nonsenescence is clearly not synonymous with intrinsic immortality , however; hence , our treatment of nonsenescent aging ( or “aging” ) as the age-independent component of vitality loss that precludes immortality . In terms of the age-specific vitality loss ϕt , we define the senescence rate to be ; that is , by the acceleration ( or deceleration ) of the decline in relative vitality . Then we have: How senescence varies over an adult life history , and the evolutionary forces affecting this , will be investigated in subsequent sections . In the simplest case , the rate of senescence is constant ( age-independent ) , and can be specified in terms of a parameter x: This gives a constant rate of positive senescence ( see Equation 10a ) when x > 0 , but gives nonsenescent aging ( see Equation 10c ) when x = 0 . More generally , the senescence rate may be age-dependent , and then the senescence parameter x2 can be defined as the asymptotic rate of senescence as t → ∞; i . e . , the rate of ( necessarily nonnegative ) senescence at very old ages . These more-complex scenarios are considered in Appendix A of Text S1 , which also treats the influence of negative senescence . The age-specific rate of loss of vitality derived from Equation 11 is ϕt ( x ) = x2t + μ0 , where we interpret the constant component μ0 as the nonsenescent component of intrinsic mortality rate . Thus , from Equation 9 , relative vitality declines with adult age according to the Gaussian: With this form , a large x determines early onset of significant decline in relative vitality shortly after adulthood , whereas a small x delays significant vitality loss to later ages ( see Figure 1A ) . Delayed-onset vitality loss—i . e . , the maintenance of a high level of relative vitality for a significant proportion of life history—can also be represented by age-dependent forms of the senescence rate , as discussed in Appendix A of Text S1 . An example is illustrated in Figure 1B . The variable x determines the rate of senescence , so that an organism with a life history that maintains ( iteroparous ) reproductive activity over an extended lifespan has small x , whereas an organism with big-bang ( semelparous ) reproduction followed by an early death has large x . We assume that there is a fundamental tradeoff between early-adult reproduction and extended high relative vitality . Thus , we suppose that the new-adult birth rate ( and hence , the new-adult recruitment rate b0 = ) is an increasing function of x , so that high early-adult reproductive output ( large x ) is paid for by a rapid decline in relative vitality with age . Conversely , a low level of early-adult reproductive output ( small x ) may be sustained over a long intrinsic lifespan . From Equation 7 , we can therefore write the ELRS as a function of the senescence parameter x: This tradeoff is illustrated in Figure 1B . We assume that the evolution of this life-history tradeoff occurs through mutations in the senescence variable x . Given this assumption , the following invasion theorem holds ( in accordance with [31 , 33 , 34] ) . Invasion Theorem: a mutation that changes intrinsic adult life-history characteristics through variation in x will invade and go to fixation if and only if it results in an increase in the ELRS R ( x ) . This result depends on strong assumptions , in particular that the environment is constant and the intrinsic mortality component μ0 remains constant . In addition , it assumes that once a mutation has invaded a population , there will be sufficient time subsequently for it to go to fixation before some other , possibly compounding , mutation arises . These assumptions imply that our equilibrium populations are genetically homogeneous with respect to the relevant phenotypic expressions . This allows us to develop a simple analytical theory . However , all these assumptions will be relaxed in the computer simulations described in the section Simulations and in Appendix B of Text S1 . The equilibrium Euler-Lotka Equation 3a implies that an increase in R is equivalent to an increase in equilibrium density N* . This means that a mutant will invade a wild-type population if and only if the equilibrium density of a monomorphic population of mutants exceeds the equilibrium population density of the wild type . We distinguish two opposing types of mutation , which we call a spreader mutation , which decreases x , and a compressor mutation , which increases x . The major effect of a spreader mutation is to increase the nominal transition age between “younger” ( high-vitality ) and “older” ( low-vitality ) adults ( Figure 1 ) . Thus , the intrinsic death rate decreases at all ages , promoting extended lifespan , and the birth rate decreases , at least initially , but may increase later in life , because the “young adult” birth rate profile is sustained for longer , albeit at a lower level . In effect , spreader mutations spread vitality into later life at a cost of a slower rate of reproduction . A compressor mutation has the opposite effect , increasing birth rates early in adulthood , but compensating by decreasing them later and increasing ( particularly late-age ) death rates . In effect , compressor mutations increase early-age vitality at a cost of an earlier onset and more precipitous senescent decline later in life in both fecundity and survival . A mutation of small effect in the senescence variable x takes the form x → x + δx , where δx is a small change , which is negative for a spreader mutation ( provided x > 0 ) , and positive for a compressor mutation . Thus , from Equation 13 , the mutation changes R ( x ) to R ( x + δx ) = R ( x ) + R′ ( x ) δx . By the Invasion Theorem , the mutation will go to fixation provided R ( x + δx ) > R ( x ) , i . e . , provided R′ ( x ) δx > 0 . For a spreader mutation ( δx < 0 ) , this requires R′ ( x ) < 0 , and for a compressor mutation ( δx > 0 ) , it requires R′ ( x ) > 0 . We shall show that it is possible , starting with a population that exhibits early-onset senescence ( large x ) , for an indefinite sequence of spreader mutations to invade and go to fixation , resulting in an ever-increasing equilibrium population density N* and an ever-increasing R . As discussed above , we assume that the new-adult recruitment rate b0 ( x ) is increasing in x . We also assume that the minimum new-adult recruitment rate , ω0 = b0 ( 0 ) , is positive , so that some reproductive output is achieved even in the nonsenescent state ( x = 0 ) . From Equation 12 , relative vitality vt ( x ) is decreasing in x for each t , and therefore , is also decreasing in x . Then from Equation 13 , R ( x ) = b0 ( x ) S ( x ) expresses the tradeoff between increasing early-adult reproduction and decreasing vitality with age . Clearly , S ( x ) “wins” this tradeoff—resulting in a decreasing R ( x ) —if b0 ( x ) does not increase too fast with x . In effect , a slow increase in b0 ( x ) expresses a strong inherent cost of early reproduction . This is the situation that favors invasion by spreader mutations . Conversely , b0 ( x ) “wins” the tradeoff—resulting in an increasing R ( x ) —if b0 ( x ) increases rapidly with x . This circumstance favors invasion by compressor mutations . We seek new-adult recruitment functions b0 ( x ) for which R ( x ) is decreasing in x . Consider the two-parameter family of near-linear functions: If D = 0 , this is linear in x with slope C , and with D > 0 , it is asymptotically linear as x → ∞ . The exponential factor is important for small x , since it ensures that b0 ( x ) is very flat , increasing only slowly near x = 0 . In Appendix C of Text S1 , it is shown that , for D > 0 and C sufficiently small , and with relative vitality given by Equation 12 ( and for the more general forms considered in Appendix A of Text S1 ) , R ( x ) in Equation 13 is monotonically decreasing in x , with R ( x ) → κCω0 , a positive constant , as x → ∞ ( where ) . Clearly , if R ( x ) is decreasing and Cω0 ≥ 1 , then R ( x ) > 1 for all x , and so there is a viable equilibrium population for every value of x , representing an evolutionary continuum from the most extreme compressed life history ( x → ∞ ) to the most extreme spread , nonsenescent life history ( x = 0 ) . Examples of behaviors of R ( x ) for b0 ( x ) in the family of Equation 14 are shown in Figure 2A–2C . Figure 2D shows an example in which b0 ( x ) increases rapidly for small x , and then only slowly approaches its straight-line asymptote . The corresponding R ( x ) has a complicated shape , with two evolutionary optima , one near x = 0 , and a more prominent one at a value near x = 1 . In terms of life-history constraints , the spreader-favoring birth functions impose developmental conditions that cause mutations away from the nonsenescent state x = 0 to have little impact on early adult recruitment compared to their impact on relative vitality . This result shows that it is possible for an indefinite sequence of spreader mutations to invade , each one leading to an increase in population density , and eventually leading to the fitness-maximizing , nonsenescent state at x = 0 . In Text S1 , we show that this result holds under much more general assumptions on the form of relative vitality and initial recruitment function , but still with the fundamental life-history tradeoff expressed in terms of a senescence variable x governing the magnitude of the age-specific rate of senescence . For a given senescence rate , we assume that relative vitality is partitioned between births and deaths as follows: where 0 ≤ αb , αd ≤ 1 are fixed ( age-independent ) parameters with αb + αd = 1 , μ = μ0 + g is the total age-independent mortality rate , and . A partition of this form is illustrated in Figure 3 . Stochastic simulations were developed to exploit the Gaussian example of Equation 12 using a partition of the form of Equation 15a and 15b; i . e . , with Φt ( x ) = ½ ( xt ) 2 ( see Methods below ) . Populations evolved towards smaller x , and thus slower senescence , given sufficiently small C and large D for Equation 14 . Figure 4 shows an example of a population with extrinsic adult mortality set to a low value ( small g ) , in addition to the extrinsic mortality imposed on juveniles by their inability to dislodge resident adults from any of the 200 habitable sites . The individuals making up the population were given life histories characterized by a relatively large R0 ( as in Figure 2A ) , and an equal partition of senescence between birth rate and period survival ( αb = αd = 0 . 5 ) . The size of x and μ0 diminished rapidly ( Figure 4A and 4B ) after population size had equilibrated ( Figure 4C ) , with eventual production of some intrinsically immortal individuals ( Figure 4C , red line ) . These immortals did not senesce , because they had zero age-dependent loss of vitality ( x = 0 ) , nor did they age , because they had zero intrinsic mortality ( μ0 = 0 ) . They could never accumulate to displace all mortals , however , because each remained susceptible to extrinsic adult mortality . The mean value of x stabilized just above zero , probably due to an ever-increasing time to fixation in the population of ever-longer lived individuals . The simulation departed from the analytical model with respect to genetic variation , by allowing every offspring to carry a ( small ) mutation rather than having a linear sequence of mutation followed by fixation . In further trials , setting g = 0 allowed immortals to accumulate in the population until they entirely filled it . Note that g = 0 does not imply no extrinsic mortality , only that the nonheritable components of mortality are concentrated in juvenile stages . Populations evolved negligible senescence even under high exogenous hazard , although a large g favored a high ω0 ( as predicted in Appendix B of Text S1 ) with the consequence that intrinsic nonsenescent aging , μ0 , was also high , and populations did not sustain intrinsic immortals . With new-adult recruitment functions of the Equation 14 type controlled by large C and small D , such as the b0 ( x ) for Figure 2B and 2C , populations evolved towards larger x , and thus faster senescence . Simulations were also undertaken with g and K varying stochastically ( stochastic environment ) . Conclusions obtained for constant g and K were robust under these extensions , except that immortals evolved more rarely ( see examples in Appendix B of Text S1 ) .
The analyses and simulations have employed an ecologically realistic framework in which selection on the life-history strategy depends on the chances for recruitment into the adult population . This in turn depends on the life history of the other ( resident ) individuals in the population . Such a situation is a form of frequency-dependent selection: the fitness of a strategy cannot be assigned in absolute terms , but it depends on the strategies of the other individuals in the population . Frequency-dependent life-history evolution is an underexplored , yet fundamental , component of the ecology in which selection operates on organisms . We have obtained a general set of frequency-dependent conditions under which spreader mutations invade in indefinite sequence from any senescent state ( Results sections Model: Invasion of Mutations , and Example ) . These results extend previous optimization analyses [17 , 24 , 31 , 34] by showing that the process once initiated is not systematically vulnerable to counteracting compressor mutations , and is largely free of conditions on life history for a population regulated by density-dependent recruitment . Any organism that currently favors spreader mutations at recruitment-regulated equilibrium may continue to do so endlessly in an environment with constant exogenous hazard over evolutionary timescales ( Figure 2A and 2B , but cf . Figure 2C and 2D ) . Although somatic growth that continues into early-adult ages strengthens selection on nonsenescence ( Results section Model: Senescent and Nonsenescent Aging as Components of Vitality Loss ) , it is not a prerequisite for it ( Appendices A and C of Text S1 ) . This is an important finding because early-adult growth is itself a form of negative senescence , and so begs the question of what selection pressures may favor it . These predictions are confirmed by simulations in various environments , including stochasticity in exogenous hazard and in carrying capacity ( Results section Simulations and Appendix B of Text S1 ) . Our analysis explicitly models developmental constraints through the functional form of b0 ( x ) , and environmental hazard with the constant extrinsic mortality rate g , allowing us to explore their influences on the direction of evolution . The runaway selection for reduced senescence arises directly from a model of organisms with strong inherent cost to reproduction expressed by a function b0 ( x ) that increases relatively slowly with x , especially for small x ( Results section Example ) . In effect , such organisms have developmental constraints that impact more strongly on reproductive rate than on reproductive longevity . In contrast , cases in which b0 ( x ) increases relatively fast represent organisms for which an increment in early reproduction carries only a light inherent cost . Unsurprisingly , selection then favors incrementally earlier reproduction in the presence of exogenous hazard , as it does if the cost is delayed far into the future . We have illustrated this range of outcomes for various forms of b0 ( x ) that can sustain viable populations across all x , and have one or more attractors at 0 ≤ x ≤ ∞ , depending on the shape of the function ( Figure 2 ) . Although there always exists a class of functions b0 ( x ) with the required properties to trigger runaway selection for reduced senescence ( Appendix C of Text S1 ) , these will certainly form a small subclass of the set of all possible b0 functions . It is difficult to characterize the relative size of this functional space mathematically , and understanding the biological significance of its characteristics is a theme for future work . Senescent aging theory from Medawar [1] onward has emphasized the fundamental role of exogenous hazard in favoring early reproduction . Accordingly , our definition of adult mortality retains an explicit distinction of extrinsic from intrinsic sources , g from μ0 , with a positive value of μ0 representing a decline in relative vitality due to nonsenescent aging ( Results sections Model: Senescent and Nonsenescent Aging as Components of Vitality Loss , and Example ) . The predictions of our model for evolution of nonsenescence from senescence embrace both adult and juvenile susceptibility to extrinsic mortality . We obtain the novel result that evolution of nonsenescence from senescence is robust to the presence of exogenous adult mortality . A reduction in the realized adult lifespan tends instead to increase the age-independent component of vitality loss ( nonsenescent aging ) . In the event that organisms adapt to hazards in gene-by-environment interactions , selection for early reproduction to hedge against future mortality risk will be offset by selection to reduce this risk . Our distinction of nonsenescent from senescent aging offers a reworking of George Williams' hypothesis [6] that populations subject to high exogenous hazard should have faster senescence , which has received mixed empirical support ( reviewed in [35] ) . Williams et al . [35] point out that this hypothesis constitutes the principal tool for predicting senescence schedules , because no other environmental factor has been proposed to account for observed variation in senescence rates . However , Caswell [36] has recently demonstrated theoretically that exogenous mortality can have no effect on the age pattern of selection gradients . In consequence , there is now an absence of predictive tools to account for environmentally induced variation in senescence . Our model provides a new one , in terms of exogenous mortality favoring nonsenescent aging under density-dependent recruitment . Most natural organisms pack their reproductive output into relatively short lives . Our model is consistent with this reality , which has a number of drivers . Individuals may frequently die from extrinsic causes , before the onset of senescence as we model it . This is evidenced in the natural rarity of age-related fecundity loss , and the rarity of degenerative disorders such as Alzheimer disease in nonhuman species . Moreover , compressor mutations are favored by conditions of density-independent fecundity , and a high μ0 is optimal under high extrinsic mortality . Even if such conditions exist only temporarily , the evolution of shorter lifespans will tend always to proceed more swiftly than the evolution of longer lifespans ( with concomitantly slower turnover ) . Organisms with very long lifespans tend to be completely or virtually immobilized as adults , which guarantees the consistent density dependence in recruitment that underpins the model of runaway selection on spreader mutations . Many organisms are not so place-dependent , and therefore may be less consistently prone to density-dependent recruitment . Finally , negligible senescence may be too costly or mechanistically impossible for many organisms , due to developmental constraints [23] . The crowded , but stable , conditions predicted to favor runaway selection on spreader mutations may contribute to the extreme lifespan of the Ocean Quahog Clam ( Arctica islandica ) . Individuals of this species can attain lifespans in excess of 200 y [16] in highly aggregated populations [37] . Our analysis predicts that selection will favor longer reproductive lifespan in response to severely limited recruitment opportunities , for example , if larvae can secure a foothold in suitable sediment only where space is released by an adult death . Likewise for the Bristlecone Pine: populations that support the longest-lived trees occupy arid and exposed sites that afford few recruitment opportunities [38] . Trees continue producing cones with viable seeds throughout adult life [19] , suggesting that seeds and seedlings may have short viability relative to adult lifespans of some 5 , 000 y . Under these conditions , we have shown how selection can favor adults that outlive their neighbors to set seed in their place . Although this direct benefit of reduced adult mortality may not apply to species with juveniles that can outlive the adult stage , for example in seed or seedling banks , even a low rate of juvenile mortality may suffice to meet the condition for spreader invasion if the attrition is spread over a long enough pre-adult phase . Recruitment limitation is important in determining the population density of the exceptionally long-lived Harvester Ant Pogonomyrmex occidentalis , which has an average colony life expectancy of 17 y [39] . Because nests are static structures and new queens almost never colonize their own nest , there is a clear advantage to a resident queen outliving her neighbors to implant offspring in their place . Other highly place-dependent species capable of long lifespans include the sea anemones , for example , Anthopleura xanthogrammica with indeterminate somatic growth and an average longevity estimated to exceed 150 y [40] . Negligible senescence is recorded for a few organisms susceptible to high extrinsic mortality , such as the freshwater hydra with an intrinsic lifespan of at least 4 y [18] . Its sessile habit is likely to induce strong density impacts on juvenile recruitment . However , our model cannot directly account for the species' apparent lack of aging ( even in one 30-y-old clone; D . Martínez , personal communication ) ; i . e . , zero μ0 . Although hydra are susceptible to predation and stochastic hazards in their shallow freshwater habitat , we suggest that the population genotype may be supplied from a source population sheltered from hazard ( i . e . , zero g ) . This would be interesting to model theoretically and empirically by testing for source-sink population dynamics . The idea that colonists inherit traits for a high rate of population increase r while slower maturity prevails at carrying capacity K [41 , 42] became an ecological paradigm in the 1960s and seemed to promise a scheme for life-history evolution [43] . Declarations that this scheme is flawed now span four decades [44–48] , attesting to the popularity of r- and K-selection as a dichotomy that endures even in apparent defiance of reason . Two principal deficiencies are deemed to render the original idea of K-selection inoperable as a life-history theory: ( 1 ) it lacks a specific equation linking carrying capacity K to individual life-history traits; and ( 2 ) its life-history predictions ignore age structure . Empirical evidence for life-history correlates of r- and K-selection is consequently mixed [44 , 47 , 48] . Our model recognizes the role of the explicitly age-structured ELRS in defining K ( Equation 3 ) . Analysis of evolution in ELRS generates the novel prediction that recruitment limitation can suffice alone to drive an indefinite delay in senescent onset and even indeterminate generation length . This type of K-selection applies more to organisms with inherently more costly reproduction , which are also those most susceptible to senescence creeping into their genomes [4] . Nonsenescence can arise in hazardous environments , but intrinsic immortality requires the near absence of extrinsic adult mortality ( though not of extrinsic juvenile mortality ) .
Simulation of evolution in senescent and nonsenescent aging . In the nonsenescent state , x = 0 , the death rate ( Equation 1 ) is constant , dt = μ0 + g , where μ0 is intrinsic mortality and g is extrinsic mortality . If μ0 = 0 , there is , in addition , no nonsenescent aging ( organisms are intrinsically immortal; section Model: Senescent and Nonsenescent Aging as Components of Vitality Loss ) , so we refer to μ0 as the aging parameter . The nonsenescent ( x = 0 ) ELRS is R0 = ω0/ ( μ0 + g ) , and we assume that independent selective processes operate to maximize R0 through a ( concave ) tradeoff between ω0 and μ0 . Maximization of R0 is then determined by a Marginal Value Theorem ( see Appendix B of Text S1 ) . Asexual genotype life histories were defined by age-specific birth and death profiles derived from Equations 15 ( a and b ) and 14 ( derivations in Appendix B of Text S1 ) . At each time step in the simulation , each individual was given a chance to produce offspring , then to die , and then for its offspring to recruit with small mutational increments or decrements to x and μ0 . Two parameter constants , K and g , described the simulated environment , and a further five defined the organism inhabiting it: K , total carrying capacity of habitable sites for adults; g , extrinsic mortality acting equally on all adult ages; B0 , D0 , shape parameters for the positive concave tradeoff between the nonsenescent rates of new-adult recruitment ω0 and intrinsic mortality μ0 ( as in Figure B . 1 in Appendix B of Text S1 ) ; αb , partitioning of vitality between births and deaths ( αd = 1 − αb , as in Equations 15 ) ; C , D , parameters for birth function b0 ( x ) ( as in Equation 14 ) . An array of size K = 200 was initially seeded with a population of ten just-matured recruits ( age t = 0 ) . These had nonzero values of the two evolvable parameters controlling vitality loss: x ( defining the senescence rate ) and μ0 ( nonsenescent mortality rate ) . Each subsequent time step involved: adult reproduction; adult death; juvenile recruitment to empty sites; and juvenile inheritance of parental x and μ0 with small mutations . Life-history evolution was monitored for a range of input parameter values . In some simulations , the environmental parameters K and g were allowed to vary stochastically at each time step . Details are given in Appendix B of Text S1 . | Senescent aging is an irreversible deterioration in physiological condition with age , which many organisms express even when removed from harmful environmental influences . The inevitability of senescence for repeatedly reproducing organisms has well-developed theoretical foundations . Since reproduction carries physiological costs , natural selection in a hazardous environment favors reaping early benefits , and delaying the cost in physiological decline until later in life when there is a greater chance of being dead from exogenous factors . But some organisms show negligible senescence , and a few , such as Hydra and the Bristlecone Pine , appear to have indefinite lifespans . We ask how such species could have evolved from ancestors with senescent life histories . In large populations , juveniles attempting recruitment into the adult population can be “crowded out” by already established adults . We show how this phenomenon can trigger a process of runaway selection on ever-reducing senescence , which can even result in the evolution of intrinsic immortality . Contrary to previous hypotheses , we find the rate of senescence to be insensitive to environmental hazard , which instead influences background , age-independent rates of physiological decline . | [
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] | 2007 | Density Dependence Triggers Runaway Selection of Reduced Senescence |
The extracellular matrix ( ECM ) is a pivotal component adult tissues and of many tissue-specific stem cell niches . It provides structural support and regulates niche signaling during tissue maintenance and regeneration . In many tissues , ECM remodeling depends on the regulation of MMP ( matrix metalloproteinase ) activity by inhibitory TIMP ( tissue inhibitors of metalloproteinases ) proteins . Here , we report that the only Drosophila timp gene is required for maintaining the normal organization and function of the germline stem cell niche in adult females . timp mutant ovaries show reduced levels of both Drosophila Collagen IV α chains . In addition , tissue stiffness and the cellular organization of the ovarian niche are affected in timp mutants . Finally , loss of timp impairs the ability of the germline stem cell niche to generate new cysts . Our results demonstrating a crucial role for timp in tissue organization and gamete production thus provide a link between the regulation of ECM metabolism and tissue homeostasis .
The extracellular matrix ( ECM ) is an essential component of adult stem cell niches , and hence a fundamental player in tissue homeostasis , as it regulates stem cell fate by mediating signal delivery and by providing matrix-directed differentiation [1 , 2] . In addition , the ECM is also required for tissue structure and integrity , organ morphogenesis and signaling [3] . Thus , we might expect ECM degradation and remodeling to be tightly coordinated during organogenesis and tissue maintenance . Matrix metalloproteinases ( MMPs ) are a class of well-known proteolytic enzymes that are able to degrade most ECM components and promote ECM turnover [4 , 5] . Because of their functions in ECM remodeling , MMPs play key roles in development and regeneration , as shown for branching morphogenesis , angiogenesis and wound healing [6] . Synthesized as inactive zymogens , MMPs are normally activated extracellularly and are classified depending on their substrate specificity or on the presence of structural motifs . Thus , MMPs can degrade fibrillar collagen ( collagenases ) or denatured collagen ( gelatinases ) , they can process non-collagen components of the ECM such as fibronectin and a number of membrane-bound MMPs have also been described . MMPs are typically regulated by the Tissue inhibitor of metalloproteinases ( TIMP ) family proteins , which are secreted multifunctional proteins that engage MMPs non-covalently to block access to their catalytic domain . Given the importance of the correct control of MMP activities , it has been proposed that an imbalance between TIMP and MMP molecules may lead to pathological conditions [7–9] . Furthermore , the control of stem cell proliferation by MMP activity has been recently reported in the ovarian niche in Drosophila , underscoring the importance of ECM metabolism in stem cell niche regulation [10] . While vertebrates contain twenty-six MMPs and four TIMPs , the Drosophila melanogaster genome possesses two MMPs ( Mmp1 and Mmp2 ) and a single timp gene [11] . Loss of timp function dramatically affects vitality and fertility of Drosophila adults , as mutant flies show a much-reduced lifespan and a ten-fold diminution in egg deposition [12] . timp mutant adults also display morphological defects , visible in the presence of autolyzed tissue in the abdominal cavity and in their inflated wings , a phenotype consistent with a role for Timp in ECM integrity and remodeling . The Drosophila female grows two ovaries in its abdomen . Each ovary consists of a series of egg-producing tubules termed ovarioles in which newly generated egg chambers or follicles develop . Each ovariole contains a specialized structure at the anterior apex—the germarium—home to germline and follicle stem cells . Egg chambers are assembled throughout the female’s life span in the germarium ( Fig 1A ) . Developing egg chambers are composed of 16-cell germline cysts enveloped by a monolayer of somatic cells that form the follicular epithelium . Adjacent egg chambers are connected by a string of 5–8 somatic cells organized in a one-cell wide filament known as the interfollicular stalk . The germarium and the concatenated egg chambers are surrounded by a basement membrane , a specialized ECM that offers structural support and constitutes a substrate for tissue migration in the ovary [13 , 14] . Basement membranes are rich in type IV Collagen , Laminins , Perlecan , Nidogen , and they may contain other extracellular matrix components such as Papilin , BM-40 or Glutactin [15 , 16] . In addition to the basement membrane , the germarium also contains a significant amount of interstitial matrix , accumulated between the different cell types that reside within it . Because both the interstitial matrix and the basement membrane are continuously remodeled during normal oogenesis to allow egg chamber formation and maturation [14] ( our unpublished observations ) , the timp gene and the effector proteins Mmp1 and Mmp2 are likely to play a role in female gametogenesis in Drosophila . The germarium constitutes a bona fide niche for germline and somatic stem cells [17 , 18] . At the anterior tip of the structure , a limited number of germline stem cells ( GSCs ) are kept within the area of influence of the stromal components of the niche , the terminal filament cells , the cap cells and the escort cells ( Fig 1A ) [19 , 20] . Several signaling pathways that originate in the cap cells and escort cells eventually act on the receiving germline to regulate the maintenance of the GSC population within the niche , controlling their proliferation and daughter cell differentiation . In spite of the myriad of published studies focusing on the signals in the germarium , so far we lack an understanding of the roles that the ECM and its metabolism play in the correct functioning of the ovarian niche . Furthermore , considering the fact that the ECM is a key component of vertebrate stem cell niches and that the physical properties of the extracellular niche influence cell fate , a detailed analysis of ECM remodeling in a stem cell niche is bound to have wide implications in more complex systems [2 , 21] . Here , we have used a multidisciplinary approach to examine the role of timp in the Drosophila ovary . Considering the canonical function of timp in ECM remodeling , our results demonstrating that timp is necessary for tissue organization and gamete production provide a link between regulation of ECM metabolism and proper tissue homeostasis in vivo .
Given the strong evidence linking Timp to the metabolism of ECM components , we set out to identify ECM proteins that are present in different amounts in control and timp mutant ovaries and to quantitate their relative levels . We performed in duplicate a 4-plex iTRAQ ( isobaric Tags for Relative and Absolute Quantitation ) analysis of our samples ( Fig 1B ) . Since null timp 1-week old adult ovaries were significantly smaller than sibling controls we decided to use the ovoD1 dominant mutation , which blocks oogenesis at stage 4–5 of development [22] , and thus compare gonads of similar size and containing developing egg chambers of roughly equivalent developmental stages . We used ovoD1/+;; timp28/TM3 ovaries as control samples and ovoD1/+;; timp28/Df as experimental tissue ( S1A Fig ) . Utilizing the MASCOT search engine , we identified 644 proteins , which allowed the quantitation of protein products corresponding to 359 genes . We recognized 10 proteins with control/experimental average expression ratios ≤0 . 50 and 38 with ratios ≥1 . 50 , corresponding to a total of 47 genes ( S1 Table ) . Thus , 13 . 4% of the identified genes were either under- ( 2 . 8% ) or over-represented ( 10 . 6% ) in control versus timp-null ovaries . A bioinformatic search for significant functional pathways and the association of enriched biological annotations to the gene list using PANTHER and GeneCodis tools failed to identify any statistically overrepresented functional pathways or protein networks in the iTRAQ data set . However , the Gene Ontology analysis of the selected candidates indicated several facts . First , three genes with putative proteolytic activity ( CG5618 , psa and DppIII ) were over-represented in timp mutant ovaries . Interestingly , transmission electron-microscopy studies of mutant ovaries showed frequent cellular degeneration not observed in control samples . Escort cells and follicle cells often displayed clear cytoplasms , multi-lamellar bodies and multi-vesicular vacuoles containing cell debris ( S4M and S4N Fig ) . Second and most relevant to the aim of this work , proteins corresponding to nine ECM components were identified by the iTRAQ experiments . Of those , only the two collagen IV α chain homologues present in Drosophila , Cg25C ( α1 ) and Viking ( α2 ) [23 , 24] were significantly changed , as they were specifically reduced in experimental tissues ( Fig 1C ) . In order to confirm that the proteins identified in the iTRAQ study represented changes due to the loss of timp activity , we performed a series of tests . First , we used an LTQ-Orbitrap ion trap mass spectrometer to compare a fraction of the proteome of 1-week old w1118 ( wild-type ) ovaries with that of ovoD1/+;; timp28/TM3 of the same age , as determined by the iTRAQ study . The LTQ-Orbitrap analysis identified 610 proteins with a MASCOT score above 50 and a peptide hit ≥ 2 . 380 of these ( 62 . 3% ) were also found in the ovoD1/+;; timp28/TM3 iTRAQ analysis ( S1B–S1D Fig and S1 Table ) . A Gene Ontology analysis utilizing the GeneCodis tool to search for biological annotations significantly associated to both sets of identified genes rendered similar results , with over 90% of the clustered protein hits in the iTRAQ and LTQ approaches falling in the same Biological Process categories ( S1C and S1D Fig ) . These results corroborated that the iTRAQ experiment identified a representative proteome of the normal tissue . Second , we performed two-dimensional DIfference Gel Electrophoresis ( 2D-DIGE ) to validate proteins differentially expressed either in control ovaries ( w1118;; timp28/TM3 ) or in experimental ones ( w1118;; timp28/Df ) . Protein samples isolated from both types of ovaries were labeled with different fluorescent dyes and separated in two-dimensional electrophoresis , which allowed the isolation and identification by mass-spectrometry of three proteins . Two of these were up-regulated in control samples ( Chorion protein S16 and Vitelline membrane 26Aa , with standardized logarithm abundance ratios of 3 . 02 and 3 . 67 , respectively ) and the third one was more abundant in timp mutant ovaries ( Regucalcin , average ratio of -1 . 56 ) . In agreement with these findings , the iTRAQ experiment identified another of the chorion proteins in the Drosophila genome ( S15 ) as over-represented in control ovaries , while Regucalcin was found over-expressed in mutant ovaries ( S2A–S2C Fig and S2 Table ) . Third , to rule out the possibility that the genetic combination used in the iTRAQ study ( a deletion for the timp gene in trans to a larger deficiency for the locus ) could identify as false-positives flanking genes removed by the deficiency or the deletion , we checked individually all the genes removed by both deletion and deficiency ( Syn and timp ) or by the deficiency alone ( CG12814 , CG12817 , CG12420 , CG34107 , CG42795 , CG3999 , CG43143 , CG6293 , CG12818 , CG12592 , CG18545 , CG31406 , Best1 , Sirt6 , pont , Bruce , sle and jumu ) . None of the proteins encoded by these genes were identified in the iTRAQ study . Altogether , the above experiments link the absence of the timp gene and the selective loss of ECM Collagen IV from mutant ovaries , hence suggesting a substantial connection between collagen IV metabolism and Timp activity . To test if timp is involved in the regulation of Collagen IV turnover , most likely by preventing excessive MMP-mediated degradation , we performed an in vivo zymography assay on live ovaries . We incubated 1-week old control and timp mutant ovaries with human Collagen IV-FITC , fixed them , co-stained them with Rhodamine-Phalloidin and measured the intensity of the FITC signal accumulated in the basement membrane of the treated ovaries . FITC fluorescence levels are proportional to Collagen IV-FITC cleavage and the proteolytic activity of the target tissue . Treatment of control and mutant ovaries in parallel rendered significantly different fluorescent intensity values , with the ECM of timp ovaries consistently showing stronger signal levels ( Fig 2 ) . Importantly , pre-incubating the samples 30’ with 1mM 1 , 10-Phenanthroline—a general MMP inhibitor ( see for instance [25]—blocked Collagen IV-FITC degradation ( S3 Fig ) . Thus , assuming that Collagen IV-FITC incorporates at equal levels in both control and experimental BMs , we conclude that Drosophila timp modulates Collagen IV degradation by regulating MMP activity in the ovarian ECM . The germarium is subdivided into 3 regions: Region 1 contains the GSCs and proliferating germ line; Region 2 is populated by 16-cell germline cysts that eventually become enveloped by follicle cells , the progeny of the two follicle stem cells located in this Region; in Region 3 cysts bud off to form developing egg chambers ( Fig 1A ) . To determine if the distribution of major ECM components in anterior ovarian tissues requires timp activity , we studied the distribution of basement membrane components in control germaria by immunostaining . We found strong accumulation of Collagen IV α2 ( Vkg ) , Perlecan , Laminin β-chain and Laminin γ-chain surrounding the terminal filament and GSC niche region . Weaker staining was generally observed along the exterior of Regions 1 and 2 , with stronger levels accumulating on the basal side of the nascent follicular epithelia in Region 3 and surrounding the interfollicular stalks . The distribution of the above ECM proteins in timp null mutant females , even as old as 3–4 weeks and exhibiting severe morphological defects , was generally undistinguishable from controls ( S4 and S5 Figs ) . Transmission Electron Microscopy ( TEM ) analysis on 3–4 week old control and mutant germaria showed an equal accumulation of electron dense material consistent with that observed by confocal microscopy for Collagen IV , Perlecan and Laminins in the GSC niche area and in the lateral regions ( S6 Fig ) . During mid-oogenesis , the basement membrane of the follicular epithelium serves as a substrate for follicle rotation , a global migration process by which egg chambers execute three complete turns while transitioning from a spherical shape to an elongated egg [14] . During rotation , follicle cells basally secrete matrix components that assemble at the basement membrane , creating a polarized extracellular network perpendicular to the long axis of the developing egg . This “molecular corset” acts to restrict central region growth of the egg chamber while allowing expansion towards the poles [26] . Since timp is required for Collagen IV metabolism , a major component of the basement membrane , we tested whether Collagen IV secreted by the follicle cells could assemble properly at the basement membrane in the absence of timp activity . To this end , we first checked that the polarized accumulation of Collagen IV and Perlecan in rotating egg chambers from 2-week old mutant females was indistinguishable from controls ( S4C , S4D , S4J and S4K Fig ) , suggesting again the absence of gross malformations in ECM organization , at least as judged by confocal microscopy . Second , we performed FRAP ( Fluorescent Recovery After Photobleaching ) experiments utilizing a GFP protein trap in the viking gene to monitor the deposition of Collagen IV . We measured fluorescence recovery at three different time points , from stages 1–3 , when most egg chambers have not yet initiated rotation ( [14] and our unpublished observations , but see [27] ) and during rotation ( stages 5–6 and 7–8 ) . Before stage 5 , neither control nor mutant egg chambers showed any sign of recovery in the bleached regions 100 min . after photobleaching . In contrast , stage 5–8 control and timp mutant follicles displayed detectable deposition of Collagen IV:GFP in the photobleached area 100 min . after bleaching ( S7 Fig ) . Since we could not detect statistically significant differences between control and timp mutant samples 100 min . after bleaching , we conclude that the general architecture and organization of the ECM during oogenesis is not grossly affected by the absence of timp function . The absence of gross alterations in the localization of ECM proteins in mutant germaria could suggest that changes to MMP activity resulting from the lack of timp are highly localized ( e . g . affecting only specific cell types ) and/or modify ECM properties without affecting its overall distribution . Overexpression of timp in the somatic component of otherwise wild-type ovaries using a UASt-timp insertion [28] combined with either a heat-shock Gal4 or the germarium-specific Gal4 c587 [29] resulted in the formation of compound follicles containing more than one germline cyst ( Fig 3A and 3B ) . This fusion phenotype could be caused by a defect in the encapsulation of the cysts in germarial Region 2 or by a failure in the specification of stalk cells that separate adjacent egg chambers [30] . Significantly , c587-Gal4 expression , which produced the strongest UASt-timp induced phenotypes , is specific to a subset of somatic cells in the germarium [29] , implying the need for a fine regulation of extracellular proteolytic activity in anterior ovarian tissues during early cyst formation . Examination of ovaries containing timp-induced egg chamber fusions revealed the presence of cells expressing the stalk cell marker Lamin C towards the periphery of the chambers , in the approximate position where stalk cells would normally be located ( Fig 3C and 3D ) . These observations emphasize the importance of the fine-regulation of timp function in the somatic cells of the germarium for the correct organization of stalk cells and for the encapsulation of new cysts , a process that requires extensive cell migration and ECM remodeling . Importantly , enlarged germaria induced by timp overexpression lack the strong accumulation of Mmp1 in the region where the first interfollicular stalk would form ( Fig 3E and 3F; see also Fig 7 for a detailed description of Mmp1 localization ) , supporting the idea that timp overexpression may affect Mmp1 activity during cyst encapsulation . Our data suggest that the dynamics of Collagen IV protein accumulation are affected in the absence of the timp gene . As collagen is an important contributor to tissue stiffness [21] , we assessed the mechanical properties of control ( n = 7 ) and mutant ( n = 12 ) ovarioles using Atomic Force Microscopy ( AFM ) -based indentation measurements . We found a significant reduction in the stiffness along mutant germaria , including the GSC niche and the area where the follicle stem cells reside , as well as in the follicular epithelium of early egg chambers and their interfollicular stalks ( Fig 4 and S8 Fig ) . Our AFM data demonstrate that the absence of timp results in the alteration of physical properties in germarial ECM and early egg chambers . As reported below , the decrease in tissue stiffness may explain the striking morphological defects in tissular organization observed in mutant germaria and their impaired ability to generate new cysts . Null timp mutant females are sub-viable , semi-sterile and grow smaller ovaries , a phenotype that can be rescued by the leaky expression from a UAS-timp transgene in absence of a Gal4 driver ( Fig 5A–5C ) . Upon closer inspection of 4-week old mutant ovaries , we observed depleted ovarioles containing very few or no maturing follicles ( Fig 5D–5F ) , suggesting that oogenesis is severely affected in the absence of the timp gene and that this depends on the age of the tissue . We thus decided to analyze in greater detail the effects of timp loss-of-function in aging ovaries . First , we focused on the development of interfollicular stalks . While in control ovaries they are formed by a one-cell wide filament of 5–8 cells , timp mutants frequently showed abnormally long interfollicular stalks containing 9 or more cells ( Fig 6A and 6B ) . Lamin C , a marker for differentiated stalk cells , accumulated normally in these additional cells from timp null mutants , suggesting alterations in cell recruitment rather than a non-specific accumulation of cells in mutant stalks . The frequency of ovarioles containing large 9+ cell stalks augmented dramatically with time , increasing from ~15% to ~50% in ovaries dissected from 1 and 7 day old timp females , respectively ( S2 Table ) . Second , we analyzed niche organization in detail . Wild-type germaria typically exhibited an elongated shape with a length ( anterior-posterior ) to width ratio of approximately 2:1 ( Fig 6C and 6E and S2 Table ) . In ovaries dissected from 1–7 day old timp null mutant females , germaria were essentially indistinguishable from controls . In contrast , ovaries from older mutants showed significant alterations to germarium morphology: 59 . 1% of germaria from 2-week old mutants displayed a much more rounded appearance with a length-width ratio of 1 . 45:1 ( Fig 6D–6F and S2 Table ) . In such rounded germaria , many gross abnormalities in stem cell niche organization were observed . While control germaria always showed a stereotyped arrangement of cell types , with the terminal filament and cap cells at the tip ( Fig 6C ) , we found mutant germaria where these cells were abnormally positioned . The example in Fig 6F depicts a germarium in which the terminal filament and cap cells are adjacent to an interfollicular stalk and to a differentiated egg chamber . Because the positioning of the GSCs adjacent to the cap cells at the base of the terminal filament is maintained in mutant germaria , the abnormal arrangement observed in a large proportion of aged timp germaria implied that GSCs were positioned in close proximity to follicle cells ( Fig 6G and 6H and S2 Table ) , while the intercalation of Escort Cells in between germline cysts seemed normal . The changes in shape and the alterations in its organization in older mutant adults show that timp plays a role in maintaining germarium structure . To analyze the outcome of niche activity , we determined whether loss of timp affected the ability of mutant ovaries to produce new germline cysts . We counted the number of 2- , 4- , 8- and 16-cell cysts present in 10-day and 3-week old germaria . Compared to controls , timp mutants showed a clear diminution in the number of cysts populating 3-week old germaria ( controls 9 . 5±2 . 2 cysts/germarium , n = 19; mutants 5 . 8±3 . 5 cysts/germarium , n = 29 . S3 Table ) . This difference is not a consequence of a drop in the number of GSCs present in the niche , as control and timp hemizygous germaria did not show statistically significant differences in GSC numbers either 10 days or three weeks after eclosion ( S3 Table ) . This result indicates that Collagen IV degradation in mutant tissues does not affect visibly niche signaling . Given the progressive loss of developing cysts in mutant germaria even in the presence of a normal pool of GSCs , we next performed a lineage experiment in which we compared the ability of control versus mutant germaria to produce new cysts . To this end , we generated flip-out clones to induce the expression of a reporter gene ( mCD8::GFP ) in random germline cysts from 1-week old ovaries . Females of this age were chosen because the vast majority of mutant ovaries this old do not present gross morphological alterations that might cause pleiotropic effects on cyst production ( see above ) . We scored the number of germaria containing GFP-expressing cysts 3 days after clone induction to make sure they were generated from labeled GSCs or cystoblasts . We found marked germline cysts in 18 . 5% of control ovarioles ( n = 216 ) and in 10 . 4% of mutant ones ( n = 202 ) . Normalized by the total number of GSCs in each of the groups , we found a frequency of 6 . 6 marked ovarioles per 100 GSCs in control ovaries , while mutant ovaries contained on average 4 marked ovarioles per 100 GSCs ( S4 Table ) . Altogether , the above results demonstrate that 1-week old timp mutant germaria are only 60 . 7% efficient in germline cyst production compared to controls of the same age and they strongly support the idea of a direct correlation between timp activity and tissue homeostasis . Because the canonical role for Drosophila timp is to regulate MMP activity in a variety of tissues , we sought to determine if MMP distribution was affected in mutant ovaries . Mmp1 localizes to the terminal filament and in the cap cells area in control ovaries . From region 2 onwards it accumulates evenly along the basal side of the follicle cells . Stronger signal can be detected in the interfollicular stalks of stage 2 and older egg chambers . In striking contrast , null timp mutant germaria and interfollicular stalk cells show a diffused distribution of Mmp1 protein ( Fig 7A and 7B ) . An Mmp2::GFP fusion protein driven by its endogenous promoter has been reported to accumulate most strongly in the anterior tip of the germarium , where it mediates the distribution of the niche signal Wingless [10] . Upon examination of the localization of Mmp2::GFP in mutant germaria , we found that timp mutants do not show a conspicuous signal in the niche region , indicating that , like in the case of Mmp1 , timp activity affects Mmp2 localization ( Fig 7C and 7D ) . This view is supported by the fact that timp mRNA is expressed strongly in the area were GSCs reside and in region 2 ( Fig 7E and 7F ) . Interestingly , the expression domains of Mmp1 and Mmp2 in the germarium and of Mmp1 in the interfollicular stalks correlate with the timp mutant phenotypes described above and suggest a possible role for timp in regulating MMP activity in the germarium . Because the germarium and interfollicular stalk phenotypes are rescued by the ectopic expression of timp ( see Materials and Methods and S2 Table ) , our observations suggest that endogenous timp might antagonize Mmp activity that to ensure germarium organization and proper interfollicular stalk formation .
Most evidence from Drosophila has reinforced the canonical view of MMPs and Timp as opposing regulators of ECM turnover , especially in tissue remodeling [28 , 32] . For example , in a number of different Drosophila tissues ectopic timp expression phenocopies the loss of MMPs [33–35] and the phenotypes of the sub-viable timp mutant adults used in this work , such as wing blisters [12] , are strongly suggestive of perturbed ECM regulation . However , up to now little or no direct evidence has been presented for the roles of endogenous timp in Drosophila or how its activity impinges on the regulation of ECM turnover . In this work we present several lines of evidence that support a canonical role for Timp in regulating ECM turnover in the Drosophila ovary . The reduced levels of the two Collagen IV subunits in timp mutant ovaries are consistent with an alteration in the turnover of this conserved basement membrane component [36] . Collagen IV is composed of long helical trimers that form a network via end domain and lateral interactions [37] . Significantly , because the increased proteolytic cleavage of human Collagen IV-FITC by cultured timp mutant ovaries could be blocked using a general MMP inhibitor , our observations strongly suggest that the loss of timp alters Collagen IV turnover via changes to MMP proteolytic activity . Drosophila embryos with reduced MMP activity showed lower levels of Collagen IV accumulation at the leading edge of epidermis wound sites [38] . Because Mmp1 and 2 act redundantly to promote wound healing , which could be blocked by overexpressing timp , MMP activity is required for Collagen IV deposition at least in the embryo . Our results indicate that during oogenesis endogenous timp activity is required for normal ECM organization , most likely by maintaining an appropriate balance of extracellular protease activity , a suggestion in agreement with the fact that timp overexpression blocks MMP2-dependent posterior follicle cell trimming during ovulation [35] . Further investigation will be required to confirm if one or both of the Drosophila MMPs are required for Collagen IV remodeling in oogenesis . However , it is also possible that timp controls ECM composition in other ways , as MMP-independent functions have been reported for timp in other organisms ( reviewed in [39] ) . Given the altered Collagen IV levels in timp mutants and the morphological changes to ovariole organization , we expected to see alterations to ECM composition and gross basement membrane lesions . Surprisingly , we found that the distribution of conserved basement membrane components was not consistently affected when examined by confocal microscopy . Nor did we observe significant alterations to the strikingly electron dense extracellular matrix accumulations seen in the apical stem cell niche regions of the germarium . Atomic Force Microscopy has proved useful for studying the regulation of ECM molecules by extracellular proteases in other models ( reviewed in [40] ) . Our examination of the mechanical properties of control and timp mutant ovaries using this method demonstrated that germaria and egg chambers were significantly softer than control tissues . While ovarioles are composite materials with different structures contributing to the measured apparent elastic modulus ( K ) , structures closer to the surface ( such as the basement membrane ) will contribute more than internal components . In fact , structures further away from the surface than ~1/10 of the indentation depth will not add to K [41 , 42] . Given the ~100nm thickness of most ovarian basement membranes , the dramatic reduction in tissue stiffness of timp mutants in the 200nm indentation measurements is almost certainly a consequence of changes to the basement membrane itself . Under normal circumstances and considering the role of Collagen in determining a tissue’s elastic stiffness [21] , the consistently Collagen IV-rich ovarian basement membranes are likely to form a stiff layer . This conserved ECM component has been shown to promote the basement membrane-mediated constriction of Drosophila tissues and organs [43] . Overall our data strongly suggest that in the absence of timp the ECM surrounding ovarian tissues , and particularly the germarium , has altered mechanical properties conferring lower stiffness despite a largely normal distribution of major ECM components . The nature of ECM changes in timp mutants remains to be determined . It could reflect alterations to the molecular organization of ECM components such as Collagen IV and Laminin that are not visible to confocal or electron microscopy . Although we have tested the distribution of many different ECM components in Drosophila , it was not possible to examine the distribution of all the 20+ known or putative ECM components encoded by the Drosophila genome [44] . Developing suitable tools to determine the distribution of these additional ECM components will be needed to fully evaluate the role of timp in determining ECM composition . Of particular interest will be the remaining uncharacterized putative ECM components , such as Glutactin [45] , that were detected by the iTRAQ screen as being expressed in the ovary . The relatively normal distribution of ECM components in timp mutant ovaries is related to another key question: how do adult flies develop and survive for weeks in the absence of timp ? What stops secreted and membrane-bound MMP proteins from wreaking havoc in the absence of Timp ? Mouse knockouts of individual timp genes are viable [46–49] and often fertile , exhibiting relatively minor defects in specific tissues frequently alleviated by decreasing MMP function in the affected tissues ( reviewed by [50] . There are some interesting parallels between the effects of removing timp from mice and Drosophila . For example , mice null for timp-3 , the closest orthologue to Drosophila timp [11] , are viable but show decreasing lung function ( and increasing collagen proteolysis ) with age that eventually results in death after around 13 months [48] . MMP activity is believed to be tightly regulated at multiple levels including transcription , secretion and zymogenic-activation [51] . Our observation that MMP accumulation in the basement membrane is reduced in timp mutants suggests that the expression of Mmp1 and Mmp2 might be regulated to compensate for the absence of timp . In other Drosophila tissues , it has been shown that the JNK pathway controls Mmp1 expression [34 , 52] . We hypothesize the existence of feedback mechanisms in the ovary that detect the level of extracellular proteolytic activity and adjust MMP expression accordingly , a mechanism that may alleviate the absence of timp function in younger mutant females but that is insufficient to compensate the prolonged loss of timp in aged ovaries . The eventual defects seen in timp mutants as they age might result from less effective tissue homeostasis caused by the long-term absence of Timp protein or by reduced MMP expression . The germarium houses germline ( GSC ) and follicle stem cell ( FSC ) populations that give rise to the germline and somatic components , respectively , of developing egg chambers . Cap cells , escort cells and terminal filament cells are responsible for producing niche signals that permit the maintenance of GSCs in their undifferentiated state [19] . Here , we have shown that the loss of timp can severely alter the shape and organization of the different domains of the germarium as mutant flies age and enlarge interfollicular stalks between egg chambers . Over-expression of timp in the germarium produced the opposite effects with elongated compound germaria and egg chamber fusions . In spite of the often-dramatic changes to germaria morphology seen in timp mutants , the basic association of GSC , cap cells and the terminal filament was always intact . This is consistent with our observation that GSC number is unaffected in timp mutants and suggests that E-cadherin-mediated adhesion between GSCs and cap cells [53] maybe sufficient to maintain local GSC niche integrity even if ECM properties are compromised . Thus timp appears to be important for the long-term maintenance of germarial structure and its stereotyped arrangement of different subdomains . FSC maintenance and proliferation in the niche depends on binding Laminin that they themselves secrete [54] . In addition to providing structural support within stem cell niches , the ECM is also believed to play a key role in stem cell niche signaling [2 , 21] . Recently , it has been shown that the secreted glypican Division abnormally delayed ( Dally ) -like ( Dlp ) promotes the long-range action of the Wingless ( Wg ) ligand in the germarium . Mmp2 , which can cleave Dlp in cell culture , opposes Dlp activity in the ovary , thus limiting the range of Wg signaling . Interestingly , the phenotypes associated with loss- and gain-of-function conditions for Mmp2 are similar to those we identified for timp , with long stalks seen in conditional Mmp2 mutants and egg chamber fusions when overexpressed [10] . These results are in accordance with our finding that loss of timp results in decreased Mmp2 accumulation in the anterior tip of the germarium and suggest that timp activity may regulate FSC proliferation via Mmp2 . In support of this idea , ectopic timp overexpression suppressed both the long stalks of Mmp2 mutants and egg chamber fusions of Mmp2 overexpression [10] . We envisage the basement membrane as a physical corset responsible for the maintenance of the proper shape of the ovarian niche , in spite of the tensions generated by the cell divisions and cellular movements that occur during new egg chamber assembly . This corset could be analogous to the Collagen IV—dependent structure that controls follicle shape during of the later stages of oogenesis [14] . The circumferential migration of FSC daughter cells [55] may play a role in secreting and/or organizing such a circular structure . Considering the striking defects observed in the organization of the mutant ovarioles , in which germaria displayed abnormal shapes and an aberrant arrangement of cell types , we propose that the loss of Timp regulation of the ECM causes a softening of the basement membrane , which is now unable to act as the corset upon which the tissue is modeled . As a consequence and as indicated by the progressive loss of developing cysts in aging mutant germaria , the new arrangement of the GSC and FSC niches provokes a significant impairment of ovarian homeostasis . Because a large number of stem cell niches of both vertebrates and invertebrates have been found to be contained within , or to interact with , specialized ECMs , the findings reported here provide a link between ECM metabolism , niche organization and tissue homeostasis that may be of general importance for the biology of stem cells .
We generated a timp null condition by combining a ∼15 kb deletion of that removes both timp and synapsin ( timp28 , a gift from A . Page-McCaw ) with the larger Df ( 3R ) ED5472 ( Bloomington Drosophila Stock Centre ) . Females bearing a deletion only affecting synapsin ( syn27 ) [12] over Df ( 3R ) ED5472 display none of the phenotypes associated with the removal of timp . The presence of a UASt-timp and either Heat-Shock Gal4 or the c587-Gal4 transgenes in timp28/Df ( 3R ) ED5472 mutant females grown at 25°C was sufficient to significantly restore normal ovary morphology . Viking: GFP is a protein trap in the endogenous Collagen IV α2-chain ( line G00205; FlyTrap; http://flytrap . med . yale . edu/ ) . UASt-timp , a gift from A . Page-McCaw , is a pUASt insertion carrying the complete timp coding sequence [28] . c587-Gal4 is a germarium-specific GAL4 line [29] . Mmp2-EGFP is an engineered BAC construct ( P[acman]-Mmp2-EGFP-GPI; [10] that expresses EGFP-tagged Mmp2 under endogenous regulatory sequences . To perform the flip-out experiments , we obtained w , hsFLP12/w; 10xUASt-mCD8::GFP/+; timp28 Act>y+>Gal4/TM6B and w , hsFLP12/w; 10xUASt-mCD8::GFP/+; timp28 Act>y+>Gal4/Df ( 3R ) ED5472 genotypes . Control and experimental flies were heat-shocked at 37°C for 30 minutes and the ovaries dissected and processed for immunostaining 3 days later . Antibody , DNA and rhodamine-phalloidin stainings were performed according to standard procedures . A list of the antibodies used is available in the Supporting S1 Text . Individual ovarioles were dissected in Schneider medium supplemented with streptomycin and insulin as described previously [56] . Fluorescence Recovery After Photobleaching ( FRAP ) Regions of interest were bleached at 100% 488 nm laser power for three 2-second scans ( 400 Hz , one line/frame average ) . Images were then collected at 20-minute intervals for 2 hours . Image series were captured using identical confocal settings for control and experimental ovarioles . Color depth was set to 12-bit and configured to minimize saturated pixels . Depth ( z ) thresholds were set well above and below each ovariole to guarantee the complete tissue was captured . Sections were taken at 630 nm intervals ( optimal ) . FITC was captured using the default Leica FITC configuration and low laser intensity . After recording position and z-depth ranges , image series were captured automatically . All image stacks were pre-processed using the standard background subtraction function of ImageJ ( default settings; 50 pixel radius ) . Measurements were taken using the Imaris Measurement Points Tool along the length of each ovariole ( 4 in the terminal filament , 5 in regions 1-2a of the germarium , 3 in regions 2b-3 , 4 in each of the stalks and 4 measurements in each of the egg chambers ) . Each ovariole contained at least 3 stalks and 4 egg chambers . Ovarioles were measured along three different lines running from the germarium to posterior follicles . Thus , TFs were quantified 12 times/ovariole , the niche region 6 times/ovariole , etc . TEM samples were prepared following standard procedures . Sections were examined with a Zeiss EM902 electron microscope at 80Kv , and photographed at 50 . 000x magnification . 0–3 day-old females were yeasted for 3 days prior to dissection . Ovaries were dissected in PBS and immediately frozen in liquid nitrogen . For the iTRAQ experiment , we collected four biological replicas . For the LTQ-Orbitrap , we analyzed two biological replicas . For the 2D DIGE analysis , we collected four biological replicas . Protein quantitation was carried using four independently-collected samples from control and experimental ovaries . A MALDI TOF/TOF 4800 ( AB SCIEX , Foster City , CA , USA ) mass spectrometer was used for acquisition and data processing . An overview of the experimental design is shown in Fig 1B . The final quantitation was performed on identified proteins associated at least to three quantitated peptides . Prepared protein samples were digested with 1:20 sequencing grade trypsin ( Roche Molecular Biochemicals ) . Peptides were analyzed using a linear trap Orbitrap Velos ( LTQ Orbitrap Velos ) hybrid mass spectrometer . The identified fragments were searched against the Drosophila melanogaster database of UniProtKB/Swiss-Prot using Mascot ( version 2 . 3 . 0 ) . A total of 50 μg of proteins from each condition labeled with 400 pmol of Cy3 or Cy5 dyes were used to perform Isoelectro focusing . The second dimension was performed on 12% SDS-PAGE gels . Fluorescent gel images were scanned and changed protein spots with p-values ≤0 . 05 were picked for finger-printing identification . MALDI samples were automatically analyzed in an Ultraflex MALDI-TOF/TOF mass spectrometer . To perform ontological and functional studies , a list of candidate genes coding for differentially-expressed proteins was evaluated using PANTHER ( http://www . pantherdb . org/ ) and GeneCodis ( version 3; http://genecodis . cnb . csic . es/ ) tools . 0–3 day-old females were yeasted for 3 days prior to manipulation . To assay for the collagen IV degradation in live tissue , we dissected control and experimental ovaries in Schneider’s medium supplemented with Fetal Bovine Serum ( 15% vol/vol ) containing streptomycin/penicillin [56] and incubated them in a culturing cocktail containing Collagen IV-FITC ( Collagen , type IV from human placenta , fluorescein conjugate; Invitrogen ) . Upon fixation in 4% paraformaldehyde , the fluorescence of control and experimental samples were captured using identical confocal settings and measured using IMARIS software . 137 measurements from 7 ex-vivo wild-type ovarioles and 179 from 12 timp nulls were collected . Monodisperse polystyrene beads were glued to silicon cantilevers with a nominal spring constant of 0 . 1 N/m . Force-distance-curves were taken at an approach speed of 10μm/s and a maximum force F = 6nN . Force—distance curves were analyzed for different indentation depths δ ( 0 . 2μm , 0 . 5μm and 1μm ) using a Matlab-based custom algorithm [57] . | The extracellular matrix ( ECM ) offers signals and support to stem cell niches , local microenvironments that provide these cells with necessary factors for their survival . The ECM also helps shaping and maintaining tissues and organs in adult animals . Because the repair of damaged tissue or the replenishment of cell lineages in functional organs requires significant cellular rearrangements , ECM remodeling has to be tightly coordinated with stem cell niche activity . By studying Timp , a regulator of ECM remodeling , we have discovered that the Drosophila timp gene is required to maintain ECM composition and biophysical properties and the organization of the female germline stem cell niche . Because loss of timp prevents proper gamete production in experimental ovaries , our results thus link ECM metabolism and tissue homeostasis . | [
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"materials... | 2016 | ECM-Regulator timp Is Required for Stem Cell Niche Organization and Cyst Production in the Drosophila Ovary |
Despite its prominent placement between the retina and primary visual cortex in the early visual pathway , the role of the dorsal lateral geniculate nucleus ( dLGN ) in molding and regulating the visual signals entering the brain is still poorly understood . A striking feature of the dLGN circuit is that relay cells ( RCs ) and interneurons ( INs ) form so-called triadic synapses , where an IN dendritic terminal can be simultaneously postsynaptic to a retinal ganglion cell ( GC ) input and presynaptic to an RC dendrite , allowing for so-called triadic inhibition . Taking advantage of a recently developed biophysically detailed multicompartmental model for an IN , we here investigate putative effects of these different inhibitory actions of INs , i . e . , triadic inhibition and standard axonal inhibition , on the response properties of RCs . We compute and investigate so-called area-response curves , that is , trial-averaged visual spike responses vs . spot size , for circular flashing spots in a network of RCs and INs . The model parameters are grossly tuned to give results in qualitative accordance with previous in vivo data of responses to such stimuli for cat GCs and RCs . We particularly investigate how the model ingredients affect salient response properties such as the receptive-field center size of RCs and INs , maximal responses and center-surround antagonisms . For example , while triadic inhibition not involving firing of IN action potentials was found to provide only a non-linear gain control of the conversion of input spikes to output spikes by RCs , axonal inhibition was in contrast found to substantially affect the receptive-field center size: the larger the inhibition , the more the RC center size shrinks compared to the GC providing the feedforward excitation . Thus , a possible role of the different inhibitory actions from INs to RCs in the dLGN circuit is to provide separate mechanisms for overall gain control ( direct triadic inhibition ) and regulation of spatial resolution ( axonal inhibition ) of visual signals sent to cortex .
The dorsal lateral geniculate nucleus ( dLGN ) acts as a gateway for visual signals that reach cortex . The principal cells , the relay cells ( RCs ) , constitute about 75–80% of the cells in the nucleus , while the remaining 20–25% are intrageniculate interneurons ( INs ) [1] . The RCs receive synaptic inputs from a variety of sources: direct feedforward excitation from retinal ganglion ( GC ) cells [2–8] , indirect feedforward inhibition via the INs , which in turn are excited by GC cells [7 , 9] , feedback inhibition from the thalamic reticular nucleus ( TRN ) [1] and feedback excitation from primary visual cortex [10 , 11] . Both the IN and TRN cells further receive excitatory feedback from cortex opening up for feedback inhibition of RCs involving the entire thalamocortical loop [1] . Despite its prominent position in the early visual pathway , and the relative abundance of anatomical and physiological data recorded from the nucleus , the functional role of the dLGN circuit is still poorly understood . Mathematical modeling of the properties of the network will clearly have to be a key component in elucidating its function . A striking feature of the dLGN circuit is that INs and RCs are known to form so-called triadic synapses [12–16] . Such triadic synapses are typically formed at sites that are proximal on the RC dendrites and distal on the IN dendrites . At these sites , a single retinal terminal contacts postsynaptic terminals on both an IN dendrite and an RC dendrite . The IN terminal is , at the same time , postsynaptic to the GC input and presynaptic to the RC [14] . In the triads , GABA-release from the IN may be triggered directly by local GC input , providing a localized source of inhibition of RCs , which may be functionally decoupled from the IN soma [12 , 13 , 15 , 16] . In addition to the complex triadic action , the INs also provide standard , axonal inhibition of RCs [14] . Until now , there has to our knowledge been no dLGN network study investigating the functional role of these triadic circuit elements . A key reason is that while several biophysically detailed neuron models for RCs have been developed [17–23] , models of INs have been more scarce . However , recently our group developed the first comprehensive multicompartmental IN models including active dendritic conductances placed on anatomically reconstructed dendritic morphologies [24] , opening up for investigations of the functional role of the different putative inhibitory action by INs on RCs in the dLGN network . Various types of visual stimuli have been used to probe the response properties of the dLGN circuit: light or dark bars , gratings , and spots of various sizes [25] . Based on experiments with flashing circular spots [26] , Einevoll and Heggelund [27] developed a mechanistic firing-rate model to account for the changes in the spatial response properties of RC cells in cat compared to its GC input . In qualitative accordance with known anatomy and physiology for cat X cells , the RC neurons in the model received excitatory input from single GC neurons and indirect feedforward inhibition from INs , which in turn received input from of a handful of GC neurons . While this model successfully accounted for the observed area-summation curves in RC cells , i . e . , the experimentally observed response vs . spot-diameter curves , it could not distinguish between the various possibilities of inhibitory action from INs to RCs , i . e . , whether the inhibition was predominantly triadic or axonal . To investigate the putatively different roles of triadic and axonal inhibitory action from INs in the dLGN circuit , we here develop and investigate a biophysically detailed , spiking neuron network model designed to be analogous to the firing-rate network model in [27] . A key component of the network is an adapted version of the recent multicompartment IN model [24] allowing for explicit studies of how the various modes of inhibition affect the shape of measured spot-response curves for dLGN cells [26] . In the next section we introduce the circuit model and describe the models of the GC input , the IN and the RC , as well as their synaptic connections . In Results we first investigate and describe the behavior of the IN model , then probe the functional behavior of the triadic circuit . Next , we illustrate how the various modes of inhibition affect the area-summation curves and finally explore differences between the transient ( onset ) and sustained ( steady-state ) responses to spot stimulation . Our findings are then discussed in the final Discussion .
Input to the dLGN circuit was provided by a layer of five retinal ganglion neurons ( GCs ) , spatially organized with one center cell and four peripheral cells equidistant from the center cell ( Fig 1 ) . Each GC axon was assumed to synapse at two different locations , i . e . , ( i ) in a triadic synapse where the interneuron ( IN ) and one of the relay cells ( RCs ) both receive excitatory input , and ( ii ) in a ‘conventional’ synapse on the proximal IN dendrite . The IN formed two inhibitory synapses on each of the five RCs , ( i ) a dendrodendritic synapse ( part of the triad ) and ( ii ) an axodendritic synapse . In the present application of the model we only computed the response of the central RC . In addition to the local triadic inhibitory action due to synaptic inputs from the central GC ( called direct triadic inhibition below ) , this cell received extra ‘back-propagating’ triadic inhibition ( called soma-driven triadic inhibition below ) and axonal inhibition following firing of action potentials in the IN . Thus the RCs were decoupled in the sense that firing of action potentials in one RC did not affect the firing of the other RCs . Therefore , the only effect of the four peripheral ( non-central ) GCs came from their proximal inputs to the IN . For simplicity we here assumed that these four synaptic weights are the same , an approximation which is unlikely to bear out in real biological situations . However , the use of circular flashing spot stimuli concentric with the receptive field of the central RC , implies that the response of the central RC will largely be determined by the sum of these four weights , not their individual variation [27] . The spike trains of GCs were modeled descriptively as non-stationary Poisson processes . The visual input driving the GCs were circular light spots centered on the middle GC . The outputs were spike trains with mean rate and temporal profile fitted to experimental data . The components that make up our circuit were modeled at different levels of detail . To allow for local processing in the dendrites and because the IN is known to be electrotonically extensive [28] , a multicompartment model was needed . We selected an existing model [24] and simplified its morphology . Some of the parameters were adjusted to otherwise preserve the model’s properties . The RC spikes constitute the main output from our network model . A single-compartment RC model was decided to be sufficient as these neurons are thought to be electrotonically compact [28] . With slight modifications discussed below , a previously published model was used [29] . The IN and RC models were both based on standard cable theory ( see e . g . , [30] ) , and the complete dLGN circuit model was implemented in the NEURON simulation environment [31–33] . Both neuron models were based on previously published models and are available from ModelDB [34]: IN model from [24] ( ModelDB accession number 140249 ) and RC model from [29] ( ModelDB accession number 3343 ) . In the following section , the individual components of the circuit and their parameterizations are presented in detail . As in the firing-rate based circuit model of [27] , a descriptive filter model was used to generate the input from the GC cells to our model dLGN circuit . Specifically , the input spike trains from the five GC cells were generated by non-stationary Poisson processes with rates determined by a response function Rg ( t , d ) describing the firing rate for a circular spot of radius d as a function of time . This response function was in turn modeled as a product over a spatial part Gg ( d ) and a temporal part Fg ( t ) [35] , i . e . , Rg ( t , d ) = Gg ( d ) Fg ( t ) . Neuron and synapse parameters were initially set up according to the calibrated ( default ) parameters listed in Tables 1–4 . As in [27] we modeled the response to circular spots concentric with the receptive field of the central GC input ( cf . Fig 1 ) . The only stimulus parameter varied was thus the spot diameter d , with the spot sizes ranging from much smaller than , to much larger than the receptive-field center . In the simulations each trial consisted of a 500 ms period of full-field background luminance followed by a 500 ms stimulus period with the circular spot added on top . In accordance with [27] , mean firing rates from GC , IN , and RC cells over the entire or selected parts of the stimulus period were computed . ( These firing-rates were found from time-averaging post-stimulus time histograms ( PSTHs ) and correspond to what is more precisely referred to as ‘spike-count’ firing rates [43] , but in the present paper we will for simplicity generally refer to them as firing rates . ) However , all spike trains were also stored for further analysis . In addition , membrane potentials from relevant neural compartments ( i . e . , RC and IN soma compartments as well as IN triad compartments ) were recorded for a subset of the trials . For each spot diameter several simulations ( ‘trials’ ) were run , and the spike-count firing rate for each trial computed . So called area-summation response curves of the type considered in [26] and [27] , i . e . , spike-count firing rates averaged over numerous trials as functions of spot diameter , were produced ( cf . Fig 3 ) . Unless otherwise noted , ten trials were used in the computation of the trial-average firing rate for each parameter set and spot size , and the response vs . spot-diameter curves were filtered with a seven-point rectangular window to produce smoother area-summation curves . Such area-summation curves were calculated for a large set of parameter values ( cf . Table 5 ) to investigate the link between model parameters and response curves . In the present application of the model we only considered the response of the IN and the central RC . The receptive-field center diameter dc was determined numerically by identifying the spot diameter that produced the maximum response rc , see Fig 3 . Here we were interested both in maximal responses for the RC ( r c R ) and IN ( r c I ) . Similarly , the surround diameter ds was given by the spot size diameter producing the minimum response rcs , and at the same time fulfilling ds > dc . From these four quantities we calculated several response measures: The ratio d c R / d c G was calculated to measure the effect of inhibition on RC receptive-field tuning [26 , 27] . In the absence of inhibition , one would expect the relay cell to inherit the receptive-field size from the GC cell , and this ratio would be close to 1 . 0 . As a measure of how much the center response is reduced by the surround ( center-surround antagonism ) , we also calculated the normalized difference between the maximum response to center stimulation ( rc ) and the minimum response when the surround is stimulated as well ( rcs ) [26 , 27]: α = ( r c - r cs ) / r c · 100 % . ( 7 ) Finally , we also investigated temporal aspects of the response and computed area-response curves both for the transient ( onset ) response , i . e . , trial-averaged spike-count firing rate for the first 100 ms after stimulus onset , and the sustained ( steady-state ) response corresponding to the averaged rate in the time interval from 400 to 500 ms after stimulus onset . Simulation and data acquisition of the dLGN circuit model was fully implemented as class objects in Python [44] , using the Python package LFPy [45] for object-representations of individual cells post-synaptic to GC units . LFPy relies on the NEURON simulation environment [33] to solve the membrane potentials for the multicompartment IN unit and single-compartment RC units . NEURON also intrinsically allows specification of neuron-to-neuron connectivity , i . e . , building network models . With a relatively low total segment count ( 177 ) for the multi-compartment IN model , each network instance was simulated serially in a matter of seconds at a temporal sampling rate of fs = 16 kHz , resulting in realtime factors as high as ∼10% for our computer hardware described below . Parallel execution was therefore only incorporated on the parameter scan level , as discussed below . Typically , only spike times and resulting rates were returned from each network element , but readouts such as membrane voltages were readily available if needed . All simulations for each parameter set ( and spot size ) were repeated 10 times or more ( see above ) with different seeds resulting in a total of more than one million simulations . The GC model was implemented in NEST [46] as a spike generator ( rather than a neuron model ) named exp_onset_generator . Simulations were performed on a compute cluster with Intel Xeon 2 CPUs running Linux 2 . 6 . 32 using NEURON 7 . 3 and NEST 2 . 3 . r10450 . Software was compiled with the GNU Compiler v . 4 . 7 . 2 and linked against the GNU Science Library v . 1 . 14 . Trials were configured using the NeuroTools . parameters package [47] . Data analysis was performed on the same computers and Apple MacBook Pro computers using NumPy 1 . 7 . 1 , Pandas 0 . 11/0 . 12 , and Matplotlib 1 . 2 . 1/1 . 3 . 0 under Python 2 . 7 . 3 . Results were stored in HDF5 files using the PyTables package . Further analysis was performed using Pandas/NumPy and Matplotlib for visualization .
Before embarking on the dLGN circuit behavior , we demonstrate in Fig 4 the salient integrative properties of the interneuron ( IN ) model . The simplified ball-and-sticks morphology of the IN is illustrated in Fig 4 with the soma ( black square ) in the center , and the five dendrites protruding out from it with locations of both the distal , i . e . , triadic , and proximal synapses marked ( panel A ) . In the remaining panels ( B–E ) , the membrane potential in only two selected dendrites are considered for figure clarity reasons . When a single GC spike arrives at a distal IN synapse ( panel B ) , the response is partly mediated by local , active ion channels . The distal dendrites undergoes a rapid , local depolarization ( up to ∼0 mV ) due to activation of local Na+ channels , after which the potential decays from subsequent activation of K+ ( and deactivation of Na+ ) channels . The distal-dendrite membrane potential is observed to remain at a relatively depolarized level , i . e . , above –50 mV , for an extended period of time ( about 20 ms , see inset panel B ) . The endured response is partly due to the activation of local T-type Ca2+ channels , as we have shown previously [38] . Due to the widening of the dendritic stick , i . e . , increase of stick diameter in the central direction , the EPSP is strongly attenuated upon its propagation towards the soma , and is not sufficient for driving the soma above the action potential threshold ( panel B ) . A single spike arriving at a proximal synapse results in only a small depolarization of the membrane potential ( panel C ) , i . e . , too little to evoke either triadic inhibition or generate a somatic action potential which in turn would provide axonal inhibition . Further , when a single distal and a single proximal synapse positioned on the same branch are activated at the same time ( panel D ) , the resulting soma potential is still too small to generate an action potential . However , when all five proximal synapses are activated by simultaneous spikes ( panel E ) , a somatic axon potential is generated which next provides axonal inhibition on postsynaptic RC cells . Moreover , this axonal action potential back-propagates into the dendrites where it also activates triadic inhibition . This latter type of triadic inhibition is here denoted soma-driven triadic inhibition . The mechanism behind the two types of triadic inhibition , i . e . , ‘direct’ and ‘soma-driven’ , is illustrated in Fig 5 . In panel B , a single incoming GC spike input to a distal ( triadic ) synapse ( illustrated in panel A ) triggers a large postsynaptic response in the distal IN dendrite . If the response is sufficiently large , as in the current example , it will lead to direct triadic inhibition from the IN to the RC partner in the triadic circuit . While the excitatory GC input to the RC cell alone would give an immediate RC action potential ( red curve in panel C ) , this action-potential firing is prevented when this excitatory input is accompanied by direct triadic inhibition ( black curve in panel C ) . ( For the present model example we find that the triadic inihibition must arrive within 1 . 3 millisecond after the excitatory GC input to prevent the generation of an RC spike . ) In soma-driven triadic inhibition a somatic action potential in the IN , induced by sufficiently synchronous excitatory GC inputs onto the proximal dendrites ( cf . Fig 4 ) , results in a back-propagating action potential which in turn induces triadic inhibition ( panel D in Fig 5 ) . However , this type of triadic inhibition takes a few milliseconds to occur , i . e . , too late to prevent the firing of an RC action potential ( panel E ) . This inhibition can thus only affect GC spikes reaching the dLGN circuit at a later time . Fig 5 illustrates the importance of timing of the triadic inhibition in the regulation of RC firing: when a GC spike impinges on the dLGN circuit ( RC and IN cells ) , only the direct triadic inhibition acts fast enough to affect the immediate spike generation in RC cells . Such direct triadic inhibition probably underlies what is known as time-locked , or simply locked inhibition in the experimental literature [15] . Some key features of the dynamics of the triadic circuit when stimulated by a flashing circular post , are illustrated in Fig 6 . While our numerical experiments each last for 1000 milliseconds , the figure focuses on the spiking activity in the half-second window around the stimulus onset at 500 milliseconds . Panel A shows the membrane-potential dynamics of the IN for an example trial , both in the soma ( blue line ) and in the distal part of the dendritic segment ( green ) receiving synaptic input from the central GC cell . This panel also shows the time stamps of the GC input spikes driving the circuit , both from the center GC cell ( top row of tiny triangles ) and from the four peripheral GC cells combined ( bottom row of triangles ) . A first observation is that in the typical case , an input spike from the central GC cell causes direct triadic inhibition ( see , for example , arrow 1 in panel A ) while a fairly synchronous barrage of four spikes from the set of GC cells is needed to evoke a somatic action potential ( see , for example , arrow 2 in panel A ) . Given the much higher firing-rate of the central GC cell compared to the peripheral GC cells in the present example , the direct triadic inhibition will occur more often than firing of somatic action potentials . As a consequence , the soma-driven inhibition ( soma-driven triadic and axonal ) will occur less frequently than direct triadic inhibition . Note , however , that the involvement of dendritic Na+ and K+ channels in mediating the local response induces an effective refractory period ( the channels do not have time to reset between two input spikes ) . This is evident during the first 50 milliseconds or so after stimulus onset , when the firing-rate of the central GC cell is so high that not all incoming spikes result in the distal-dendrite membrane potential passing firing threshold ( see , for example , arrow 3 in panel A ) . Direct triadic inhibition will therefore not occur at every input spike . Such a depression of triadic inhibition for high input rates was also seen experimentally [15] . Panel B in Fig 6 illustrates the corresponding RC response . When there has been a long time since the previous excitatory GC input spike ( see , for example , arrow 4 in panel B ) , direct triadic inhibition prevents the firing of an RC spike . However , if a new GC input spike arrives before the RC membrane potential has returned to its resting value , the direct triadic inhibition may not be sufficient to prevent the firing of an RC action potential ( see , for example , arrow 5 in panel B ) . The chance for an incoming GC spike to generate an RC spike can be further reduced by soma-driven inhibition leading to a transiently hyperpolarized RC membrane potential ( see , for example , arrow 6 in panel B ) . We also note that the inhibition is more efficient at preventing the firing of RC action potentials in the background state , i . e . , prior to stimulus onset at 500 milliseconds , than immediately after stimulus onset: For example , during the depicted background state ( 250–500 ms ) only two of the seven incoming GC spikes result in the firing of an RC spike , corresponding to a transfer ratio [48] of 2/7 ≈ 0 . 29 . In contrast , in the first 75 milliseconds after stimulus onset ( 500–575 ms ) , six of thirteen incoming GC spikes result in an RC spike , corresponding to a transfer ratio of 6/13 ≈ 0 . 46 . This transfer ratio smaller than unity value reflects that two or more incoming GC spikes are normally needed to elicit an RC spike [48–50] . As the spiking response to individual stimulus presentations typically varies between trials , the post-stimulus time histogram ( PSTH ) [43] is commonly used to characterize neural spiking responses . Examples of such PSTHs for the set of experiments underlying the experimental area-response curve measurements for the GC and RC on which the present model is tuned ( cf . Fig 5 in [27] ) , can be found in [26] ( Fig 3 and 4 therein ) . Fig 7 shows PSTHs for the GC , IN and RC cells in Fig 6 found by binning spikes found from many repetitions , i . e . , many trials of our numerical ‘experiment’ . Panel A shows the PSTH from the central GC cell in a 500 ms window around the spot onset , while panel B similarly shows the corresponding PSTH for the IN cells . The two lower panels show corresponding PSTHs for the RC cell for two extreme situations: only axonal inhibition ( i . e . , triadic inhibition turned off , wIRt = 0 ) in panel C , and only triadic inhibition ( i . e . , axonal inhibition turned off , wIRa = 0 ) in panel D . For these particular model parameters we see that the peak response in the PSTH following stimulus onset is largest for the GC ( ∼200 s−1 ) and smallest for the IN ( ∼50 s−1 ) . For the RC we see that both the background ( i . e . , response before stimulus onset ) and peak responses are larger for the case with axonal inhibition ( panel C ) than for triadic inhibition ( panel D ) , implying that for the present choice of model parameters the triadic inhibition is more efficient than axonal inhibition in reducing RC firing . We now move on to compute and investigate area-summation curves , that is , the time-average of PSTHs of the type shown in Fig 7 , as a function of spot diameter . These time-averaged PSTHs correspond to what is more precisely referred to as ‘spike-count’ firing rates [43] , but in the following we will for simplicity refer to them as firing rates . In the present modeling study we in particular investigate the effects of various types of inhibition on the area-summation curves of the RC and IN neurons . Examples of such calculated area-response curves are given in Fig 8 . Here the black line gives the area-response curve of the central GC cell providing the input , the blue line the corresponding curve for somatic spikes for an IN , while the solid , dashed and dotted red lines show RC response curves for different choices of model parameters specifying inhibitory effects from the IN . The response curves shown here correspond to the ‘raw’ data , i . e . , prior to filtering by a seven-point rectangular window ( see Methods ) , and the jagged response curves serve to illustrate the inherent variability of the trial-averaged response . The bottom panel in Fig 8 shows the data normalised to the maximal response for each cell , thus highlighting the shapes of the area-response curves rather than their response magnitudes . Fig 8 shows example area-summation curves for the three different types of inhibition considered here: The figure also shows the resulting area-summation curve when all these three types of inhibition is included at the same time . A first observation in Fig 8 is that the GC response in all cases is larger than the RC response , essentially reflecting that the transfer ratio at the retinogeniculate relay always is less than one [26 , 27 , 48 , 49] . The spot diameter with the largest responses corresponds to the size of receptive-field center , and we observe that while the central GC cell has a center diameter d c G of about 2 degrees , the IN center diameter d c I is about 3 degrees , cf . panel B . This larger center size reflects that the IN is driven by multiple , spatially separated GCs . For the case with direct triadic inhibition only ( RC-i ) we observe that while this inhibition reduces the RC firing rate by about a factor two compared to the GC input ( solid curves in Fig 8A ) , the shape of the response curves , i . e . , normalized response , is essentially identical ( panel B ) . Thus the direct triadic inhibition essentially acts as a gain control , only . With soma-driven inhibition included as well ( RC-ii ) , some changes in the shape is observed ( dashed red curve in panel B ) . In particular , a close inspection of panel B reveals that the receptive-field center size d c R of the RC cell now is seen to be somewhat smaller than the GC center size . An even larger reduction of the center size is observed in the case of axonal inhibition only ( RC-iii ) . This reduction in receptive-field center size seen for cases ( RC-ii ) and ( RC-iii ) ( as well as the example in Fig 8 with all three types of inhibition included , RC-all ) reflects the larger resulting receptive-field size of the IN providing the inhibitory action on the RC cell [26 , 27] . Another key qualitative feature observed in Fig 8 is the larger center-surround antagonism , i . e . , large relative dampening of the full-field response ( e . g . , d = 10 degrees ) compared to the peak response , seen for the cases where the inhibitory effects are the strongest ( RC-ii and RC-all for the example model in Fig 8 ) . For IN this center-surround antagonism is instead reduced compared to the GC input . In the following we show area-summation curves results both when only triadic or axonal inhibition are active like in Fig 8 , and in the likely more realistic case when both types of inhibitions simultaneously affect the relay-cell response . For reference we show in the top row of Fig 9 the RC response for the case with neither triadic nor axonal IN inhibition . Here we observe that the overall RC response changes only moderately when increasing the excitatory connection strength wGR between the central GC cell and the RC cell with almost 50% from the lowest value considered ( wGR = 11 . 6 nS ) . The reason is that the transfer ratio , i . e . , the fraction of incoming GC spikes resulting in an outgoing RC spike , is already quite high even for this lowest weight . This leaves limited room for further increase in the response . Another observation is that without inhibition the RC and GC response curves always have their maxima at the same spot diameter , i . e . d c R ≈ d c G . With direct triadic inhibition included ( second row in Fig 9 ) we see that the RC response curves drop substantially , e . g . , about 50% for the peak response and even more for the full-field ( large-spot ) response for the lowest value of wGR ( 11 . 6 nS ) . Unlike in the case with no inhibition , increased excitation strength wGR is seen to increase the RC response as extra excitation will compensate for the added direct triadic inhibition . We further see that the shapes of the RC response curves are similar to the ‘no-inhibition’ curves , the main difference is a vertical shift of the response curves . Such a vertical shift implies a larger relative reduction of the full-field response compared to the center response , i . e . , an increased center-surround antagonism . Thus direct triadic inhibition increases the RC center-surround antagonism αR , particularly for the lower excitatory weights . The RC receptive-field center size d c R is essentially unaffected by the direct triadic inhibition . This follows from the fact that in our IN model , excitation of the distal IN dendrite results in small EPSP amplitudes at the soma ( Fig 4B ) . Thus direct triadic inhibition on the RC cell can only occur due to spiking inputs from the central GC cell , and such inhibition can only affect the gain control within the triadic synapse structure ( as was clearly illustrated in the normalized response plot for the direct triadic case in Fig 8B ) . Since distal IN excitation barely affects the somatic membrane potential and does not generate IN somatic action potentials , an IN area-summation curve is likewise absent from the second row of Fig 9 . The three lower rows of Fig 9 depict area-response curves for various combinations of direct and soma-driven triadic inhibition and axonal inhibition . The different rows correspond to different values of the proximal excitation of INs ( wGIp ) , while different columns still correspond to different values of the retinogeniculate excitation ( wGR ) . The area-response curve of the IN is , of course , independent of the value of wGR , so the same IN response curve is seen in the same-row panels . By comparing area-response curves for increasing values of proximal IN excitation wGIp we see , as expected , a large increase in the IN response . The increased response is also accompanied by a reduction in center-surround antagonism of the IN neuron . However , the IN receptive-field center size d c I is much less affected . In each of the nine panels in the lower three rows in Fig 9 there are four ( red/orange ) RC area-summation curves corresponding to different values of the axonal inhibition weight wIRa . The topmost curves correspond to the situation without axonal inhibition ( wIRa = 0 ) , while the three other curves corresponds to different non-zero values of wIRa ( 2/4/8 nS ) with the lowest curve corresponding to the largest weight considered ( wIRa = 8 nS ) . It is seen that not only does increased axonal inhibition reduce the RC response , it also reduces the RC receptive-field center size d c R . Both effects are seen to be strongest when the proximal excitation wGIp of the IN is largest . The effects of the various model components and parameters on key response measures for the results in Fig 9 are summarized in Fig 10 . This figure shows how the RC and IN receptive-field center sizes ( d c R , d c I ) , center-surround antagonisms ( αR , αI ) , and maximum firing rates vary with the axonal inhibition weight ( wIRa ) for a set of different values of the weight of ganglion-cell activation of the RC ( wGR ) and of the proximal dendrites of the IN ( wGIp , color coded according to legend box below figure ) . A first observation is that the receptive-field center size of the RC ( d c R ) is substantially reduced both when the proximal excitation of the proximal IN dendrites ( wGIp ) and when the axonal inhibition weight from the IN to the RC ( wIRa ) are increased ( panel A ) . This is as expected as both these weights determine the overall axonal inhibition of the RC providing the shrinkage of the RC receptive-field center [27] . In contrast , the receptive-field center size of the IN ( d c R ) can naturally only depend on the weight of the proximal synapse from the GC ( wGIp ) . This increase is quite modest , however , and most of the observed variation in the ratio between the IN and RC center sizes ( d c I / d c R ) ( panel B ) comes from the variation of the RC center size . The center-surround antagonism for the RC ( αR ) is seen to be almost independent of the axonal inhibition weight wIRa ( panel C ) . This implies that the RC center-surround antagonism is little affected by axonal inhibition . For the smallest values of the weight of the ganglion-cell input to the RC ( wGR = 11 . 6 nS ) , αR does not depend much on somatic IN activity ( panel C ) : αR is large , about 0 . 7 , for all considered values for wGIp . However , for the two largest values of the ganglion-cell input weight to the RC ( wGR = 13 . 6 nS , wGR = 15 . 6 nS ) some variation with wGIp is observed: For example , for wGR = 15 . 6 nS , αR is seen to vary between ∼0 . 4 for wGIp = 0 to ∼0 . 6 for wGIp = 1 . 8 nS . As wGIp = 0 corresponds to the case with direct triadic inhibition only ( and it is also seen that αR is essentially independent of wIRa ) this substantial increase in αR must be due to soma-driven triadic inhibition . Thus while direct triadic inhibition alone is seen to be sufficient to assure a large centre-surround inhibition when the retinogeniculate excitation wGR is weak , soma-driven triadic inhibition can provide the same when the retinogeniculate excitation is strong . The center-surround antagonism for the IN ( αI ) is generally much lower than for the RC [27] and is seen to vary between ∼0 . 25 and ∼0 . 4 depending on the value of wGIp ( panel C , dashed lines ) . The maximum firing rate r c R of the RC , i . e . , the firing rate at the peak of the area-summation curve , is as expected seen to decrease both with increasing axonal inhibition weight ( wIRa ) and increasing synaptic input onto the proximal dendrites of IN ( wGIp ) ( solid lines in Fig 10D ) . For the IN , the maximum firing rate r c I is correspondingly seen to increase when the weight of proximal synaptic input from GCs ( wGIp ) increases ( panel D , dashed lines ) . In Fig 11 we show , in analogy to Fig 9 , the same set of area-summation curves in the absence of triadic inhibition , i . e . , wIRt = 0 . In this case where only axonal inhibition acts on the RCs , we observe as expected less reductions of RC responses , particularly for the smallest considered values of wGIp and wGR . However , as confirmed by the corresponding parameter dependence of the key response measures shown in Fig 12 , most qualitative effects of increasing the inhibitory synaptic weights are similar to what was seen for the case with triadic inhibition included , cf . Fig 10: The receptive-field center size of the RC ( d c R ) ( panel A ) decreases with increasing axonal inhibition ( wIRa ) and increasing ganglion-cell drive onto proximal IN dendrites ( wGIp ) . This is also the case for the maximal RC firing rate r c R ( panel D ) , but here the firing rates are as expected overall higher compared to the case with triadic inhibition . A final observation in Fig 12 is that the center-surround antagonism for the RC ( αR , panel C ) is seen to generally be lower when triadic inhibition is absent , cf . Fig 10C . This is in accordance with the previous observation for the results with triadic inhibition ( Fig 10C ) where αR was seen to be largely independent of the axonal inhibition weight wIRa . This was interpreted to reflect dominance of triadic inhibition over axonal inhibition in determining the RC center-surround antagonism . Without triadic inhibition we observe in Fig 12C that αR instead increases with the value for the axonal inhibition weight wIRa . So far we have only considered the spike-count rate pooling all spikes within the whole 500 ms time interval following stimulus onset in our simulations . As seen in Fig 7 there is a strong transient component of the response with a peak in the PSTHs around 25 ms after stimulus onset while the generally much lower sustained ( steady-state ) response is reached around 100 ms after onset . This is in qualitative accordance with observations in flashing-spot experiments on cat RCs [26 , 35 , 51] . We thus next asked the question of whether triadic and axonal inhibition have differential effects on the transient and sustained responses of the RC . In Fig 13 we compare area-response curves computed for the transient phase ( 0–100 ms after stimulus onset ) to the sustained phase ( 400–500 ms after stimulus onset ) for the same model examples as in Fig 8 . Comparison of the ( unnormalized ) responses in the top row demonstrates the large differences in firing rates , the transient response ( panel A ) being up to a factor two larger than the sustained response ( panel B ) . For the present model examples , the triadic and axonal inhibition are seen to be roughly equally effective in dampening the RC response for the transient response for spots filling the receptive-field center ( panel A ) . Interestingly , however , the triadic inhibition is seen to be more effective than axonal inhibition in dampening this response to center-filling spots for the sustained response . This feature is seen also for other values of retinogenculate excitation wGR than the one used in this example , cf . Fig 14B . Comparison of the normalized area-response curves ( panels C and D in Fig 13 ) reveals only subtle differences in the area-response shapes . One observation is that soma-driven triadic inihibition seems slightly more effective in suppressing the sustained than the transient RC responses for the largest spot diameters . For the sustained response we also observe in panel D a weak ‘noisy’ minimum in the response for spot diameters d around 5–6 degrees for the case with both triadic and axonal inhibition ( RC-all ) , a feature not present for the transient response ( panel C ) . This minimum stems from the strong activation of the INs for these spot sizes ( cf . blue curve in panel D ) compared to for the larger spot sizes , i . e . , d∼8–10 degrees . While this feature of the IN response curve is also present for the transient response , it is slightly less so . The more prominent role of the combined triadic and axonal inhibition in modifying the receptive-field of the RC ( compared to the GC ) in the sustained response than in the transient response is also manifested by the slightly smaller receptive-field center size ( d c R ) and widths of the peak of the area-summation curves , cf . panels C and D in Fig 13 .
Our model contains three distinct types of inhibition , ( i ) direct triadic inhibition , ( ii ) soma-driven triadic inhibition and ( iii ) axonal inhibition , each with putatively different inhibitory effects on the RCs . We consider the present investigation to be only the first of several applications of the present modeling approach . Until recently , dLGN circuit models lacked a key ingredient , namely an IN model incorporating the key dual-action inhibitory features of this cell type . With the arrival of the first multicompartmental dual-action IN model [24] , we can now , with the combined use of existing ( single-compartment ) Hodgkin-Huxley type models for relay cells ( see [55] for an overview ) , investigate dLGN circuitry in models at a new level of biological realism . This will not only enable elucidation of the role of dLGN circuitry in shaping spatial response features like here , but also the key role played by the circuit in temporal processing of the incoming spike trains from retina [39 , 52] . Below we discuss various directions where the present modeling approach should be considered . The present model is based on data from several animal species: the target RC and GC area-response curves are from cat dLGN [26 , 27] , the RC single-compartment neuron model was developed to investigate network dynamics in ferret slices [29] , while the multicompartment IN model is based on data from mice [24] . Main features of thalamic physiology seem to be well conserved across species [52] . However , the applicability of the present ‘chimeric’ model to account for data from different species is presently unknown , in particular since different arrangements of the LGN circuit elements may give very different signal-transformation properties [65 , 78] . This will have to be explored by comparison of model predictions with experimental data from the various species . With the advent of ever more sophisticated techniques for controlling gene expression in mice ( accompanied by the possibility for optogenetic activation [80] ) , the mouse dLGN has emerged as a particularly interesting model system [52] . The full mouse dLGN has only about 18 . 000 neurons , so network simulations of a sizable fraction of the visual field is feasible with present-day computers . However , ‘no nucleus is an island’ [52] , and a comprehensive understanding of the function of the dLGN circuit likely also will require simultaneous modeling of the primary visual cortex ( with 360 . 000 neurons [81] ) and maybe also other brain areas . Such modeling of the visual thalamocortical system in mice can be facilitated by joint application of models at different levels of resolution . In the present model , for example , the GC input was modeled by means of stochastically generated spike trains obeying spatiotemporal probability distributions found from descriptive firing-rate models . The RC cells were modeled as single-compartment Hodgkin-Huxley type neuron models producing spikes , but the connection to the previous firing-rate model of the same system [27] was apparent as very similar trial-averaged area-response curves were produced . In the same vein one could envision modeling the effects of cortical feedback to the dLGN circuit by means of firing-rate models for populations of cortical cells feeding back to spiking network models in the dLGN ( rather than firing-rate models [74] ) . With a comprehensive mapping of the physiological and anatomical properties of the cells ( and their connections ) in mouse dLGN and visual cortex on the way [81 , 82] , the time seems ripe for comprehensive efforts to finally build mechanistic models mimicking signal processing in the dLGN . | While the basic receptive-field structure of cells in the dorsal lateral geniculate nucleus ( dLGN ) , the station between retina and visual cortex in the early visual pathway , was mapped out half a century ago , the function of this nucleus in molding the visual signals is still poorly understood . One reason is that the dLGN contains enigmatic inhibitory interneurons which can act with different inhibitory action on the excitatory relay cells . In addition to standard axonal inhibition , relay cells and interneurons form so-called triadic synapses , where an interneuron dendritic terminal can be simultaneously postsynaptic to a retinal input and presynaptic to a relay-cell dendrite , opening up for so-called triadic inhibition . Taking advantage of a recently developed biophysically detailed multicompartmental model for an interneuron , we here use a network model to investigate putative effects of these inhibitory actions on the response properties of relay cells stimulated by circular flashing spots . Our results suggest a possible role of the different inhibitory actions in providing separate mechanisms for overall gain control ( triadic inhibition ) and regulation of spatial resolution ( axonal inhibition ) of visual signals sent to cortex . | [
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"and",... | 2016 | Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells |
HIV-1-infected cells persist indefinitely despite the use of combination antiretroviral therapy ( ART ) , and novel therapeutic strategies to target and purge residual infected cells in individuals on ART are urgently needed . Here , we demonstrate that CD4+ T cell-associated HIV-1 RNA is often highly enriched in cells expressing CD30 , and that cells expressing this marker considerably contribute to the total pool of transcriptionally active CD4+ lymphocytes in individuals on suppressive ART . Using in situ RNA hybridization studies , we show co-localization of CD30 with HIV-1 transcriptional activity in gut-associated lymphoid tissues . We also demonstrate that ex vivo treatment with brentuximab vedotin , an antibody-drug conjugate ( ADC ) that targets CD30 , significantly reduces the total amount of HIV-1 DNA in peripheral blood mononuclear cells obtained from infected , ART-suppressed individuals . Finally , we observed that an HIV-1-infected individual , who received repeated brentuximab vedotin infusions for lymphoma , had no detectable virus in peripheral blood mononuclear cells . Overall , CD30 may be a marker of residual , transcriptionally active HIV-1 infected cells in the setting of suppressive ART . Given that CD30 is only expressed on a small number of total mononuclear cells , it is a potential therapeutic target of persistent HIV-1 infection .
CD30 , a member of the TNF receptor superfamily , is expressed on tumor cells found in Hodgkin and other aggressive lymphomas [1 , 2] but only on a very small percentage of lymphocytes in healthy individuals [2 , 3 , 4 , 5 , 6 , 7] . Although its functions are largely undefined , CD30 has been implicated in the activation , proliferation and death of selected cell populations [2 , 4 , 8 , 9] . Stimulation of this receptor has been shown to activate NF-κB , a protein complex that regulates immune responses [2 , 9 , 10] . Infections with viral pathogens , such as human T-cell lymphotropic virus ( HTLV ) , Epstein-Barr virus ( EBV ) , and poxviruses markedly increase surface expression of CD30 compared with cytokine activation alone [4 , 11 , 12] . Given the rarity of CD30 expression in vivo , the dramatic increases in the setting of these infections may be secondary to virus-specific cell stress responses . Prior studies have demonstrated that stimulation of CD30 results in increased HIV-1 expression ex vivo in cells obtained from untreated individuals , and higher levels of soluble CD30 are associated with HIV-1 disease progression [4 , 5 , 8 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20] . However , many of these studies were performed in the setting of untreated HIV-1 infection and the relationship between surface CD30 expression , soluble CD30 ( sCD30 ) and viral persistence in ART suppressed individuals or those with viremic control off ART is unknown . It is possible that CD30 is expressed on transcriptionally active HIV-1-infected residual cells in the setting of ART . Therefore , we investigated the relationship between CD30 and HIV-1 burden in peripheral blood and gut-associated lymphoid tissue from HIV-1-infected individuals on and off therapy . Overall , we demonstrate that HIV-1 infected CD30+ T cells markedly contribute to the total population of HIV-1 infected and transcriptionally active CD4+ T cells in several individuals regardless of ART use . In addition , we observed that CD30 and HIV-1 transcriptional activity co-localized in gut-associated lymphoid tissues in individuals on or off ART . Finally , we showed that brentuximab vedotin , significantly reduced the total amount of HIV-1 DNA in PBMC obtained from infected individuals .
We analyzed the peripheral blood of HIV-1 infected and uninfected individuals ( S1 Table , S1 Fig ) for CD30 expression on PBMC , and plasma sCD30 levels . The frequency of CD30+CD4+ T cells was significantly higher among viremic and ART-suppressed HIV-1 infected groups , irrespective of ART regimen , compared to HIV-1-uninfected controls ( p = 0 . 045 and p = 0 . 002 respectively ) . However , no significant differences in the frequency of CD30+CD4+ T cells were observed between the untreated and treated HIV-1 infected cohorts ( Fig 1A ) . Plasma soluble CD30 levels were significantly higher in the viremic donor group as compared to the suppressed and uninfected group ( p<0 . 001 and p<0 . 001 respectively ) ( Fig 1B ) , but sCD30 levels did not correlate significantly with CD30 surface expression ( Fig 1C ) . The relationship between CD30 expression and immunological phenotypes in the setting of HIV-1 infection was determined on fresh peripheral blood CD4+ T cells isolated from HIV-1-infected and uninfected individuals . Both the frequencies of CD69 and co-expression of HLA-DR and CD38 were significantly increased in healthy controls and ART suppressed participants within CD30+CD4+ T cells compared to the CD30-CD4+ T cells ( CD69; p = <0 . 002 and p = 0 . 001 , respectively; HLA-DR; p = 0 . 002 and p = 0 . 03 , respectively ) ( Fig 1D and 1F ) . The expression of CD30 is known to increase during many biological processes , including cellular activation [21] . However , the expression of CD30 is rare in individuals without concomitant viral infection or specific hematological malignancies . Therefore , to clarify whether we were simply selecting a highly activated population of CD4+ T cells , we determined the percentage of CD69+ , HLA-DR+ or PD-1+ cells that expressed CD30 in addition to the percentages of CD30 that express these markers . Our data showed that few cells expressing markers associated with early or late activation ( CD69 and CD38/HLA-DR , respectively ) expressed CD30 ( Fig 1E and 1G ) . CD30 is thought to be up-regulated on activated CD4+ T cells , and while studies have demonstrated this following artificial stimulation in vitro [21 , 22 , 23] , we are unaware of any data examining CD30 expression on unstimulated , non-malignant cells ex vivo . Therefore , we determined the expression of CD30 on unstimulated CD4+ T cell subsets in HIV-uninfected individuals . Interestingly , CD30 expression in HIV-uninfected donors was observed primarily on naïve CD4+ T cells , whereas most CD30-expressing CD4+ T cells in HIV-1-infected individuals , on or off ART , were found to be of effector/transitional memory phenotype ( Fig 1H ) . A significantly higher percentage of CD30-expressing cells in healthy controls and HIV-1 infected donors also co-expressed PD-1 ( p = 0 . 0020 , p = 0 . 015 ) , but again , less than 10% of all PD-1 expressing cells co-expressed CD30 ( Fig 1I and 1J ) . To assess the relationship between CD30 expression , HIV-1 transcriptional activity , and cell-associated HIV-1 DNA and RNA levels , we utilized fluorescence activated cell sorting to isolate CD30+CD4+ and CD30-CD4+ T cells obtained from 29 HIV-1-infected donors on suppressive ART ( n = 17 ) , viremic donors with high viral loads ( n = 9 ) , and individuals able to control HIV-1 without ART ( HIV-1 controllers , n = 3; HIV-1 plasma RNA levels <500 copies/mL ) . HIV-1 genomic DNA and unspliced RNA were quantified by PCR for each subset [24 , 25] . Cell-associated HIV-1 RNA was significantly enriched within the CD30+CD4+ T cell population ( p = 0 . 007 and p = 0 . 008 for ART suppressed and viremic groups , respectively ) ( Fig 2A and 2B ) . Despite CD30+ cells having several orders of magnitude higher HIV-1 DNA levels in samples from several individuals , no intragroup statistical significance was identified . Samples from all participants were included in the analysis , regardless of CD30 recovery from sorting ( which at times was low , e . g . <50 cells ) or if HIV-1 RNA or DNA were not detected in cells from these participants to reduce selection bias . In order to determine the contribution of CD30+CD4+ T cells to the total RNA and DNA burden of HIV-1 infection in peripheral CD4+ T cell compartments , we compared the total HIV-1 RNA and DNA recovered from each T cell subset , taking into account the number of cells obtained , from ART suppressed ( n = 17 ) , viremic donors ( n = 9 ) and HIV controllers ( n = 3 ) . The average percentage of CD4+ T cells expressing CD30 was found to be 3 . 97% , 1 . 05% and 0 . 03% in suppressed , viremic and controller groups , respectively ( Fig 2C ) . Despite the rarity of these cells , an average of 21% , 28 . 5% and 51 . 4% HIV-1 RNA was attributed to CD30+CD4+ T cells in suppressed , viremic and controller groups , respectively . Strikingly , >90% of detectable cell-associated HIV-1 RNA was found within CD30+CD4+ T cells in samples from five individuals either on or off ART ( Fig 2D ) . The largest contribution of CD30+ T cells to the peripheral HIV-1 DNA burden was observed in ART suppressed individuals , and 18 . 3% , 2 . 2% and 0 . 05% of HIV-1 DNA was attributed to CD30+CD4+ T cells in suppressed , viremic , and controller donor groups , respectively ( Fig 2E ) . Moreover , in three ART-suppressed individuals , >50% of peripheral cell-associated HIV-1 DNA was found within CD30+CD4+ T cells . Overall , there was a high degree of inter-participant variability in the contribution of CD30+CD4+ T cells to the peripheral HIV-1 burden . No significant relationship between the frequency of CD30+CD4+ T cells and CD4+ T cell count , age , or number of years since diagnosis was identified in non-parametric analyses ( all P>0 . 05 ) . However , the contribution of CD30+CD4+ T cells to the total pool of CD4+ T cell-associated HIV-1 RNA was found to be significantly higher in African American individuals ( mean = 42 . 7% ) compared to white individuals ( mean 12 . 8% ) , irrespective of ART regimen ( P = 0 . 0103 ) ( Fig 2F ) . There were no significant differences in HIV-1 DNA contribution between racial groups ( Fig 2G ) . In order to verify that the rare population of CD30+ cells obtained from cell sorting was not from non-specific staining or other flow cytometric artefact , we performed mRNA gene expression PCR assays incorporating mRNA for CD3 complex genes , CD4 , CD8α , and CD80 ( a surface marker expressed on B cells and non-lymphocyte cells ) on 5 additional sorted blood samples ( S2 Fig ) . These additional extractions did not involve the use of HIV-uninfected carrier cells , in order to obtain undiluted human RNA from CD30+ and CD30- cells . Overall , a majority of 2-ΔΔCt values from the CD3 and CD4 mRNA PCR arrays comparing CD30+ to CD30- CD4+ T cells were equal to or greater than one , suggesting that CD30+ cells have similar , if not higher levels of these mRNA transcripts . 2-ΔΔCt is a measure of difference between two populations within individual samples based on the cycle threshold values obtained by quantitative PCR . A value of one indicates no difference and values greater than one indicate a greater number of RNA transcripts . In contrast , CD8 and CD80 mRNA were not detected in CD30+ cells from 3 and 4 samples , respectively ( S2 Fig ) whereas CD80 and CD8 mRNA could be detected in all of the sorted CD30- cell populations . These results indicate that the sorted CD30+ cells have mRNA profiles consistent with CD3+CD4+ lymphocytes . We obtained rectal tissue from five ART-suppressed individuals for phenotypic analysis and flow sorting of CD4+ T cells into CD30+ , CD32+ , CD30+CD32+ , and remaining CD30-CD32- subsets ( S3 Fig & S3 Table ) and incorporated CD13 into the staining panel of two donors to ascertain potential non-lymphocyte cell contamination ( of note , CD14 is not expressed in gut-associated monocytes ) . Overall , we detected HIV-1 DNA and RNA in all of the CD30-CD32-CD4+ lymphocytes but none in the CD30+CD32- population . HIV-1 RNA and DNA were intermittently detected in the CD30+/CD32+ and CD30-/CD32+ subsets , at times , 1–2 log10 higher than the CD30-CD32- CD4+ T cells ( Fig 3A and 3B ) . CD4+ T cells had significantly higher expression levels of HLA-DR within the CD32 expressing subpopulations compared to CD30+CD32- and CD30-CD32- cells ( Fig 3C ) , and up to 42% of HLA-DR+CD30+CD32+ CD4+ T cells co-expressed CD13 compared to <11% of CD30+CD32- cells . No significant differences in HIV-1 RNA or DNA were observed between cell subsets obtained from the gut samples . It is possible that the extensive processing and collagenase treatment used to isolate cells from GALT samples may have altered surface phenotypes . Therefore , we proceeded to examine tissue samples directly using direct mRNA staining . To determine the single-cell co-localization of HIV-1 and CD30 mRNA transcriptional activity in tissue , we performed in situ hybridization ( ISH ) on tissue samples obtained from ileum and rectum of HIV-1-infected individuals on ( n = 6 ) and off ( n = 3 ) ART , and one aviremic HIV-1 controller ( S1 Table ) . In HIV-1-uninfected control tissue samples , <0 . 01% of all gut-associated lymphoid tissue ( GALT ) cells expressed CD30 RNA ( Fig 4 ) . However , the observed CD30 expression was almost exclusively co-localized with HIV-1 RNA in gut samples from both viremic and ART suppressed individuals ( Fig 4 ) . Interestingly , we found a significantly higher percentage of co-localization of CD30 and HIV-1 RNA in ART-suppressed individuals ( 88% of all HIV-1 RNA expressing cells in gut tissues expressed CD30 ) compared with co-localization in viremic participants ( 32 . 5%; P = 0 . 008 ) . In order to determine whether or not CD4+ T cells expressing CD30 harbour replication competent virus , we sorted PBMC from eight additional ART-suppressed individuals and performed traditional quantitative viral outgrowth assays ( qVOA ) incorporating serial dilutions of CD30+ T cell subsets . We were only able to detect positive p24 in CD30-CD32-CD4+ T cells in two of eight participant samples ( total input cells ranged from 3 to 10 million ) after 21 days of co-culture [infectious units per million cells ( IUPM ) of 0 . 11 and 0 . 23 ) We were not able to detect p24 in any wells containing CD30+ cells , but experiments were limited by the very small numbers of cells that could be recovered and incorporated in the qVOA experiments ( < 1 , 000 input cells/well ) . Brentuximab vedotin , an anti-CD30 antibody-drug conjugate approved for the treatment of various haematological malignancies in which CD30 is over expressed , has shown efficacy in reducing the burden of CD30+ malignant cells in vivo [10] . In preliminary experiments , we observed a 43% to 80% decrease in the percentages of CD4+ T cells expressing CD30 obtained from three ART-suppressed donors following 48 hours of ex vivo bretuximab vedotin exposure ( 10μg/ml ) compared with parallel experimental wells with no antibody-drug conjugate ( S4 Fig ) . Next , we exposed PBMC obtained from seven HIV-1-infected individuals on suppressive ART to various concentrations of brentuximab vedotin . Following culture of PBMC in the presence of antiretroviral drugs ( raltegravir , 3TC ) and brentuximab vedotin for 5 days , we observed a significant reduction in the mean total cell-associated HIV-1 DNA within samples at the higher input concentrations ( 100μg/ml p = 0 . 047 , 500μg/ml p = 0 . 039; Fig 5 ) . However , no reduction in the viability of total input PBMC was observed , suggesting that brentuximab vedotin may have selectively depleted CD30+ cells enriched in HIV-1 DNA . Overall , variations in DNA levels in no-ADC control wells were observed between participant samples . However , participants were not chosen based on baseline HIV reservoir size and all experimental wells , including those with and without brentuximab vedotin were exposed to the same input cell number and culture conditions . Successful use of the brentuximab vedotin for refractory lymphomas has been described in two HIV-1-infected patients[26] , but assessments of viral reservoir size after therapy have not been reported . We identified an individual who lacked detectable HIV-1 in cells after receiving six cycles of brentuximab vedotin for anaplastic and cutaneous T-cell lymphomas . The patient had low-level plasma viremia detectable after conventional chemotherapy ( <400 RNA copies/ml ) , but no HIV-1 was detected in purified CD4+ T cells using sensitive real-time PCR assays for cell-associated RNA or DNA , and no plasma virus was detected by clinical viral load assays after six cycles of brentuximab vedotin . Unfortunately , the individual passed away from central nervous system tumor recurrence , and further longitudinal sampling or tissue collection was not possible . Furthermore , cells were not able to be collected prior to initiation of brentuximab vedotin . Nonetheless , this finding was unexpected as we have observed detectable HIV-1 DNA and RNA in PBMC from all other HIV-1-infected individuals in a cohort of 15 patients who completed multiple cycles of systemic chemotherapy ( not including Brentuximab ) for a variety of solid organ and hematological malignancies , as we have previously reported [27] ( CD4+ T cell-associated HIV-1 DNA increased from a mean of 2 , 204 copies/106 cells to 43 , 961 copies/106 cells following completion of cancer chemotherapy in 10 individuals with pre- and post-chemotherapy time-points available ) . The HIV-1 DNA and RNA assays had a detection limit of <15 copies/106 CD4+ T cells given the available number of input cells for this individual .
Previous studies have shown that higher levels of sCD30 are associated with HIV-1 disease progression [4 , 5 , 8 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20] . However , the relationship between surface CD30 expression , sCD30 and HIV-1 burden in ART suppressed individuals and HIV controllers , is currently unknown . Here , we demonstrate that cell-associated HIV-1 RNA is significantly enriched in CD4+ T cells expressing CD30 , a member of the tumor necrosis factor receptor superfamily , and this effect was observed in many , but not all , individuals . These findings were observed in several HIV-1 infected donor groups , regardless of whether or not the participants were receiving suppressive ART . HIV-1 DNA was also enriched in CD4+ lymphocytes expressing CD30 from some individuals , with HIV-1 DNA exclusively detected in CD30+ cells from one participant . When present , the magnitude of nucleic acid enrichment within CD30+CD4+ T cells was higher than previously reported for peripheral blood CD4+ T cells co-expressing PD-1 alone , or PD-1 , TIGIT and LAG-3 ( <1 log10 fold change ) [28] , and roughly similar to the frequency of lymph node-derived PD-1+CD4+ T cells that harbor inducible HIV-1 and total HIV-1 DNA burden in peripheral blood CD32+CD4+ T cells as previously described [29 , 30] . Overall , our results suggest that CD30 may be a marker of residual , transcriptionally active HIV-1 infected cells in setting of suppressive ART . CD30 was found to be primarily expressed on peripheral blood CD4+ TTM/EM subsets in HIV-1 infected donors . Although central memory T cells ( TCM ) have been shown to be a major peripheral blood reservoir of HIV [31] , TEM and TTM subsets contribute significantly to the amount of cell-associated HIV-1 RNA and DNA in blood , and a majority of viral nucleic acid found in GALT , a major anatomic site of HIV persistence [32 , 33] . In addition , a recent study of six individuals on suppressive ART demonstrated that TEM cells contained the largest proportion of intact HIV sequences [34] . Given the possibility that CD30 expression on CD4+ TEM and TTM subsets may be due to cellular activation , we sought to clarify if we were simply selecting a highly activated portion of CD4+ T cells , rather than a potential marker of viral infection . Encouragingly , the majority of activated T cells in all individual samples in our study did not co-express CD30 . Furthermore , it is established that viremic individuals have higher levels of immune activation than individuals on ART [35] . In contrast , we observed that individuals on or off ART have similar frequencies of CD30+CD4+ T cells . This observation , in addition to our finding that <1% of CD4+ T cells , on average , were CD30 positive , suggest that CD30 is not a marker commonly expressed on activated T cells in the setting of HIV-1 infection , and may be due to more specific viral cellular stress responses . Results from the gut in-situ hybridization studies also support the conclusion that HIV-1 RNA is enriched in CD30-expressing cells in individuals on suppressive ART . Although we were unable to differentiate between CD4+ T cells and tissue-resident macrophages or dendritic cells , nearly all CD30 mRNA+ cells co-expressed HIV-1 RNA , and a larger majority of HIV-1 RNA+ cells expressed high levels of CD30 mRNA . We did not find significant differences between HIV-1 RNA and DNA levels determined by qPCR in sorted CD30- and CD30+ CD4+ T cell subsets isolated from fresh rectal tissue in ART-suppressed individuals , but the sample size was small and the number of CD32+ and/or CD30+ cells that could be recovered was often low . It is possible that gut tissue collagen digestion and processing influenced transcriptional regulation or surface presentation of CD30 and other surface markers , a phenomenon which has been previously described for other surface proteins [36] . The highest levels of HIV-1 RNA and DNA from rectal tissue-derived cells were obtained from cells co-expressing CD30 and CD32 . Interestingly , this subset had the highest HLA-DR expression and greatest frequency of CD13 cells , suggesting that there may have been some degree of macrophage/myeloid carryover during tissue sorting . It is also possible that the HIV-1 RNA was enriched in activated gut-associated CD4+ T cells , as lymphocyte activation has been shown to be higher in GALT than peripheral blood [32] . As a result , further phenotypic characterization and study of HIV persistence in these tissue-derived cell subsets is needed . Overall , our results suggest that CD30+ expression appears to be a marker of residual HIV-1 transcriptional activity in the setting of suppressive ART , at least in some individuals . However , several of our observations indicate that CD30 may also be a marker of more stable HIV-1 infection in the setting of ART or that CD30 expression waxes and wanes over time in infected cells . For example , sCD30 was only found to be higher in viremic individuals and sCD30 levels in ART-treated individuals were similar to those in uninfected controls . These data suggest that sCD30 may be increased as a result of persistent active viral production or excessive immune activation , whereas the surface expression of CD30 may represent a more stable , general HIV-related marker in both viremic and suppressed individuals . The mechanisms for the upregulation of CD30 in the setting of HIV-1 infection are unknown and warrant further investigation . Finally , we demonstrated that ex vivo treatment with brentuximab vedotin , an antibody-drug conjugate that targets CD30 , significantly reduced the total amount of HIV-1 DNA in PBMC obtained from seven HIV-infected individuals . These data suggest that CD30 may have a role as a potential HIV-1 therapeutic target , as it is highly correlated with residual HIV-1 transcriptional activity , even in individuals on suppressive ART , but is not expressed on a vast majority of otherwise healthy cells . Other cell surface markers that have previously been associated with HIV-1 enrichment ( e . g . PD-1 or CD32 ) are expressed on a variety of non-T lymphocyte cell types which may make clearance of rare HIV-infected cells without significant off-target toxicities challenging . Furthermore , there are very few cytoreductive ADCs in clinical use , and these drugs are typically directed towards disease-specific targets [10] . We observed variation in responses to ex vivo brentuximab vedotin within this study , which is expected given the described variation within HIV-1 burden and CD30 expression between individuals in blood . It is possible that factors such as cellular HIV-1 DNA burden and the level of CD30 expression within individuals may influence the reservoir responses to brentuximab vedotin , and further studies involving concomitant latency reversal are warranted . While the observed loss of CD4+ T cell-associated HIV-1 DNA and RNA from the individual who received multiple cycles of brentuximab vedotin is only anecdotal , this case finding inspired the detailed ex vivo work of CD30 as a potential marker for HIV-1 infection as described above . Further identification of HIV-infected individuals receiving brentuximab vedotin for malignancy and detailed viral reservoir investigation are needed . This study has several limitations . We observed that CD30 expression is significantly altered following freeze thaw procedures , and fresh blood draws were required . It is also highly possible that collagenase treatment of GALT samples altered surface phenotype , as previously described with other surface markers[36] . Additionally , the number of participants that could be recruited to the study was restricted and dependent on long cell-sorting time or the need for freshly reconstituted brentuximab vedotin . As a result , pre-selection of participants was limited and different individuals were included in each of the subsequent studies . Overall , we noted high inter-participant variability in the HIV-1 reservoir measurements . However , variation between study participants is expected given the complex interplay between infection and expression of surface markers . At times , samples with low CD30 recovery from cell sorting resulted in the inability to detect HIV-1 RNA or DNA . Variability in the lower limits of detection has been previously documented using flow cytometric sorting of rare target cells [23] . Nevertheless , rather than exclude these samples , we included results from all participants to avoid biasing our results . In some instances , very high levels of HIV-1 RNA enrichment were observed in CD30+ T cells from a minority of participants . These high measurements may have partially been due to artefact from calculations involving very small denominators ( e . g . small numbers of cells surveyed ) . However , we identified a very close relationship between HIV-1 RNA and CD30 mRNA expression in gut using in situ hybridization studies which provides additional support using an independent method that CD30 expression is directly related to cellular HIV-1 burden and transcriptional activity . We did not measure CD30 expression following ADC treatment in the brentuximab vedotin dose response experiments , but did observe large reductions in the percentage of CD4+ T cells expressing CD30 in the presence of ADC in preliminary experiments performed on samples from three ART-suppressed individuals . While this reduction may represent ADC-targeted cell killing , CD30 staining may also have been affected by steric interference with ADC-bound receptor or receptor downregulation . We were unable to detect positive viral outgrowth in experiments including blood CD4+ T cells expressing CD30 . This is likely due to limitations in the traditional qVOA when using very low numbers of input cells ( in some cases , less than 100 cells per well for CD30+ subsets ) , and may or may not reflect the potential for CD30 expressing cells to harbor replication competent virus . Further studies involving methods , such as whole genome sequencing , may prove more useful in estimating intact proviral burden within CD30+ subsets . In conclusion , we describe the enrichment of HIV-1 RNA in CD30+ CD4+ T cells from HIV-1 infected individuals on suppressive ART . Furthermore , a large proportion of total CD4+ T cell-associated HIV-1 RNA is found within CD30 expressing cells from suppressed and viremic individuals , and bretuximab vedotin is capable of reducing HIV-1 DNA burden . Further investigation is warranted to evaluate the stability of CD30 following HIV-1 infection and the development of latency , and to determine the efficacy of CD30 as a potential therapeutic target .
In total , 43 HIV-1-infected and ten HIV-uninfected participants ≥18 years of age were enrolled . Clinical data obtained included history of ART , viral load measurements , and CD4+ T cell counts , race/ethnicity , age , and detailed medical histories . Individuals on or off ART were included in the study . Leukapheresis was performed through the UCSF SCOPE cohort for the collection of >1 billion PBMC used in cell sorting and viral outgrowth assays ( VOA ) . The study was approved by the UCSF Committee for Human Research and the Dana-Farber/Harvard Cancer Centres Institutional Review board . All volunteers provided written informed consent . Peripheral blood mononuclear cells were separated from whole blood using density centrifugation over Histopaque ( Sigma ) . Isolation of CD4+ T cells was then performed using EasySep Human CD4+ T Cell Enrichment Kits ( Stem Cell Technologies ) , following the manufacturer’s protocol . For rectal tissue , to remove the epithelium and epithelial cells , 30 tissue biopsy tissue pieces were incubated in pre-warmed buffer containing 10mM DTT , 5mM EDTA , 10mM HEPES and 5% FBS for 20 minutes at 37°C under continuous rotation , vortexed and the supernatant aspirated . Tissue pieces were then incubated for a second time in the same pre-warmed buffer for a further 20 minutes at 37°C under continuous rotation . The sample was again vortexed , and then rinsed for 20 minutes at 37°C under continuous rotation with RMPI , 10mM HEPES and 5% FBS . To then isolate lymphocytes from the lamina propria , a second , pre-warmed buffer containing RPMI , 10mM HEPES , 7 . 5μg/ml DNAse and 5% FBS was applied to the tissue pieces , and these were again incubated for 20 minutes at 37°C under continuous rotation . The samples were then vortexed , and aspirated numerous times with a blunt 20G needle until tissue was viably broken down . The cells were then rinsed twice with RMPI , 10mM HEPES and 5% FBS and passed through a 70μm cell strainer , pelleted and stained as described below for fluorescent activated sorting . CD4+ T cells were stained in PBS with either Brilliant Violet 605-conjugated anti-CD4 ( OKT4 ) ( Biolegend ) , allophycocyanin ( APC ) -conjugated anti-CD30 ( BY88 ) ( Biolegend ) and fluorescein isothiocyanate ( FITC ) -conjugated anti-CD3 ( SK7 ) ( BD Biosciences ) for sorting only , or phycoerythrin ( PE ) -Cy7-conjugated anti-CCR7 ( 3D12 ) ( BD Biosciences ) , Brilliant Violet 711-conjugated anti-CD3 ( UCHT1 ) ( BD Biosciences ) , Brilliant Violet 421-conjugated anti-PD-1 ( EH12 . 1 ) ( BD Biosciences ) , Brilliant Violet 650-conjugated anti-CD4 ( SK3 ) ( BD Biosciences ) , alexa Fluor 700-conjugated anti-HLA-DR ( G46-6 ) ( BD Biosciences ) , PE-conjugated anti-CD38 ( HB7 ) ( BD Biosciences ) , fluorescein isothiocyanate ( FITC ) -conjugated anti-CD69 ( L78 ) ( BD Biosciences ) , Qdot 605-conjugated anti-CD8 ( 3B5 ) ( Life Technologies ) , PE-Texas Red-conjugated anti-CD45RA ( MEM-56 ) ( Life Technologies ) , allophycocyanin ( APC ) -conjugated anti-CD30 ( BY88 ) ( BioLegend ) and LIVE/DEAD Fixable Aqua Dead Cell Stain Kit ( ThermoFisher Scientific ) for CD4+ CD30+ T cell phenotypical analysis . For the rectal tissue samples , cells were stained with LIVE/DEAD Fixable Aqua Dead Cell Stain Kit ( ThermoFisher Scientific ) , PE-CF594-conjugated anti-CD45RA ( HI100 ) ( Fisher Scientific ) , Brilliant Violet 711-conjugated anti-CD4 ( SK3 ) ( BD Biosciences ) , Brilliant Violet 650-conjugated anti-CD3 ( SP34-2 ) ( BD Biosciences ) , allophycocyanin ( APC ) -conjugated anti-CD32 ( FUN-2 ) ( Biolegend ) , PE-conjugated anti-CD30 ( BERH8 ) ( BD Biosciences ) , APC-H7-conjugated anti-HLA-DR ( L243 ) ( BD Bioscience ) , Brilliant Violet 586-conjugated anti-CD13 ( WM15 ) ( BD Biosciences ) and Brilliant Violet 421-conjugated anti-CD38 ( HIT2 ) ( Fisher Scientific ) . Cells were then analyzed and sorted on a BD FACS ARIA II ( BD Biosciences ) , or analyzed on a LSR-II ( BD Bioscience ) . Single stained beads ( Life Technologies ) were used for software-based compensation . During some sorts , data for phenotyping was also acquired on all events and analyzed in FlowJo V10 ( TreeStar ) . Examples of gating strategies are shown in S1 & S3 Figs . Purification of HIV-1 DNA and RNA from sorted cell populations was achieved using a Qiagen AllPrep DNA/RNA mini Kit , and following the manufacturer’s standard protocol , with an additional DNAse treatment ( QIAgen ) . Given the small number of CD30+ cells that could be obtained from flow sorting , these cell fractions were spiked into uninfected carrier PBMC to maximize HIV-1 DNA and RNA recovery and normalize extraction efficiency between CD30+ and CD30- CD4+ T cell populations . Spiking rare cells also allowed for the input of similar amounts of RNA into each PCR reaction . Quantitative PCR was performed to determine the levels of HIV-1 cell-associated RNA ( caRNA ) , proviral DNA ( pvDNA ) , and CCR5 in each subgroup . CCR5 was used to calculate assay cell concentration and extraction efficiency . Primer pairs and probe sequences were used as described in [24 , 37] . Briefly , the same primer and probe sequences were used for both total HIV-1 DNA and unspliced RNA and sit near the Gag-LTR junction , a highly conserved region among all group M HIV-1 sequences . PCR reactions were performed on an ABI OneStep qPCR machine ( Applied Biosystems ) using either the ABI TaqMan Universal PCR Master Mix for DNA or the ABI TaqMan Fast Virus 1-Step Master Mix for RNA for up to 45 cycles as we have previously described [24] . Plasma from EDTA anticoagulant blood was isolated by density centrifugation followed by an additional spin and frozen until all donors had been recruited . Plasma was then thawed , and analysed using Human CD30 ( soluble ) Elisa Kit ( Life Technologies ) following the manufacturer’s standard protocol . PBMC were cultured at 1 x 106 cells/mL in RPMI medium ( Life Technologies ) supplemented with 10% heat-inactivated FBS ( Gemini BioProducts ) , 100IU/ml penicillin and 100μg/ml streptomycin ( Gemini BioProducts ) . Where required , Brentuximab Vedotin ( Seattle Genetics ) was added to cells at various concentrations . All cell cultures were performed in the presence of antiretroviral treatment [raltegravir ( 40nM ) , and efavirenz ( 8nM ) or T20 ( 20ng/mL] ( Selleck ) and IL-2 ( 500ng/mL ) ( Peprotech ) . Cells were cultured for five days , after which analysis by flow cytometry , DNA extraction , and quantitative PCR were performed , as described above . Existing gut tissue was obtained through the UCSF SCOPE cohort by colonoscopy . Three millimeter sections were obtained from the gut and/or ileum ( by jumbo forceps ) of HIV-1-infected individuals on or off ART . Informed consent was obtained from all participants under the approval of the UCSF Committee on Human Research . Tissue was promptly preserved in 4% paraformaldehyde before paraffin embedding and sectioning for ISH . RNA-ISH was performed using RNAscope branched-DNA technology distributed by Advanced Cell Diagnostics ( ACD ) , Newark , CA . The RNAscope assay was performed on 4-micron thick sections of FFPE human gut tissue ( ileal and rectal ) using the RNAscope 2 . 5 HD duplex kit . RNAscope probes targeting HIV-1 RNA , and the anti-sense sequence of the Hs-TNFRSF8 gene ( CD30 ) were developed by ACD and used according to the manufacturer’s recommendations . The HIV-1 infected ACH2 T cell line and HIV-negative rectal tissues were used as positive and negative controls , respectively . Sections were stained in multiplex for CD30 and HIV-1 RNA species and analyzed by bright-field microscopy . Whole slide digital images were obtained using the Leica Aperio slide scanner and total HIV-1-RNA+ or HIV-1-RNA+/CD30-RNA+ cells were manually counted in digital images of each section of tissues . Signal intensity was used as a qualitative measure of the level of transcription where small-punctate signals are thought to represent a single transcript and larger-darker clusters are thought to represent multiple transcripts in close proximity [38] . PBMC from an additional five individuals on suppressive ART were sorted into CD30+ and CD30- subsets followed by direct cell lysis and reverse transcription of mRNA to cDNA using TaqMan Gene Expression Cells-to-CT Kit ( Life Technologies/Ambion ) following manufacturer protocols , with the exception of the final preamplification step . cDNA of the CD3 receptor complex , CD4 , CD80 , CD8α and reference gene ( 18S and GUSB ) mRNA were then quantified using the ABI OnseStep-compatible Human T-Cell Receptor and CD3 Complex TaqMan Array ( ThermoFisher ) following manufacturer protocols . 2-ΔΔCt values were calculate as described [39] comparing CD30+ and CD30- CD4+ T cell subsets . B cells purified by negative magnetic bead selection ( StemCell Technologies ) were used as a control . Following fluorescent cell sorting of CD4+ T cell subsets based on surface marker expression as described above , cells were incorporated into a standard quantitative viral outgrowth assay exactly as previously describe [40] . In brief , this assay uses irradiated PMBC blasts , PHA and IL2 to maximally stimulate input cells , and is considered a gold-standard for quantitative co-culture . If sufficient cells were recovered , 5-fold serial dilutions starting with one to two million input cells per well were performed in duplicate . HIV-1 p24 levels were then determined by ELISA ( PerkinElmer , San Jose , CA ) following 21 days of co-culture . A well was considered positive if p24 levels were significantly higher than background levels from control wells containing uninfected cells . IUPMs and confidence intervals were determined using an online calculator incorporating maximum likelihood statistics as described [41] . Significant intergroup differences were determined using rank Kurskal-Wallis of Friedman tests ( depending on paired or unpaired data ) incorporating Dunn's tests for multiple comparisons . Wilcoxon matched-pairs signed rank tests were used to determine statistical significant between CD30 intergroup , paired samples . ( Graphpad Software , vs . 7 ) . | Previous studies have shown that higher levels of soluble CD30 are associated with HIV-1 disease progression . Many of these studies , however , were performed prior to the implementation of combination ART , and the relationship between surface CD30 expression , soluble CD30 and HIV-1 infection in ART suppressed individuals , or those with viremic control off ART , is not known . We demonstrate that cell-associated HIV-1 RNA is highly enriched in CD4+ T cells expressing CD30 , a member of the tumor necrosis factor receptor superfamily . These findings were observed in several HIV-1 infected donor groups , regardless of whether or not the participants were receiving suppressive ART . Furthermore , we demonstrate that ex vivo treatment with brentuximab vedotin , an antibody-drug conjugate that targets CD30 , reduces the total amount of HIV-1 DNA in PBMC obtained from infected individuals . Finally , we show through in situ RNA hybridization studies that CD30 and HIV transcriptional activity co-localize in cells from gut biopsies obtained from HIV-1 infected donors . These data suggest that CD30 may be a marker of residual , transcriptionally active HIV-1 infected cells in the setting of suppressive ART . | [
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"medicine... | 2018 | Increased HIV-1 transcriptional activity and infectious burden in peripheral blood and gut-associated CD4+ T cells expressing CD30 |
Plague , caused by the bacterium Yersinia pestis , is a highly infectious , zoonotic disease . Hundreds of human plague cases are reported across the world annually . Qinghai Plateau is one of the most severely affected plague regions in China , with more than 240 fatal cases of Y . pestis in the last 60 years . Conventional epidemiologic analysis has effectively guided the prevention and control of local plague transmission; however , molecular genetic analysis is more effective for investigating population diversity and transmission . In this report , we employed different genetic markers to analyze the population structure of Y . pestis in Qinghai Plateau . We employed a two-step hierarchical strategy to analyze the phylogeny of 102 Qinghai Plateau isolates of Y . pestis , collected between 1954 and 2011 . First , we defined the genealogy of Y . pestis by constructed minimum spanning tree based on 25 key SNPs . Seven groups were identified , with group 1 . IN2 being identified as the dominant population . Second , two methods , MLVA and CRISPR , were applied to examine the phylogenetic detail of group 1 . IN2 , which was further divided into three subgroups . Subgroups of 1 . IN2 revealed a clear geographic cluster , possibly associated with interaction between bacteriophage and Y . pestis . More recently , Y . pestis populations appear to have shifted from the east toward the center and west of Qinghai Plateau . This shift could be related to destruction of the local niche of the original plague focus through human activities . Additionally , we found that the abundance and relative proportion of 1 . IN2 subgroups varied by decade and might be responsible for the fluctuations of plague epidemics in Qinghai Plateau . Molecular genotyping methods provided us with detailed information on population diversity and the spatial-temporal distribution of dominant populations of Y . pestis , which will facilitate future surveillance , prevention , and control of plague in Qinghai Plateau .
Plague , caused by the virulent bacteria Yersinia pestis , is a highly infectious zoonotic disease [1 , 2] . Human infection is usually caused by direct contact with infected animals or fleas and is fatal without prompt antibiotic treatment . Three major plague pandemics have been documented in history , which not only led to millions of deaths but also facilitated worldwide spread of Y . pestis , causing virtual global colonization , except for in Australia and Antarctica [3] . Currently , Y . pestis circulates between multiple species of rodent hosts and species of flea vectors , and persists in multiple natural plague foci in Asia , Europe , Africa , and America , causing hundreds of human plague cases annually . Qinghai Plateau is one of the most severely affected plague regions in China and over 200 , 000 km2 of this territory is covered by natural plague foci . It is reported that at least 20 species of mammals and 11 species of fleas could be infected by Y . pestis in Qinghai Plateau [4] . Some of them , such as Marmota himalayana , act as a reservoir , maintaining Y . pestis transmission in the environment . The first isolate of Y . pestis in Qinghai Plateau was from M . himalayana in 1954 in Guide County , after the establishing of a routine surveillance system in Qinghai Plateau in the same year . Since then , two types of natural plague foci , characterized by different main hosts , Marmota himalayana and Microtus fuscus , have been identified . Human plague cases have been reported every year from 1954 to 2014 except for 1972 , 1984 , 1999 , 2000 , 2002 , 2007 , 2008 , 2010 , 2012 , 2013 , and 2014 in Qinghai Plateau , and more than 240 people have died from plague during this period [4] . In the early years since surveillance was established , human cases in Qinghai Plateau were associated with marmot hunting , which was an important means of livelihood for many local communities . In recent years , numbers of human cases have declined following the official prohibition of marmot hunting . Instead , a new disease pattern amongst marmots , livestock and humans has challenged our understanding of plague transmission . For example , an outbreak of primary human pneumonic plague in 2009 in Xinghai County was introduced by an infected dog [5] . Therefore , the threat of human plague remains , and effective control measures are still required . Previously , researchers have proposed many methods for local plague control , informed by conventional plague epidemiology [6 , 7] . However , it is difficult to trace the source of isolates precisely , to uncover the transmission dynamics of the isolates and to analyze the population structure and epidemiological characteristics , because of the difficulty in typing of Y . pestis , which is generally regarded as lacking much genetic variation within the species [8] . Molecular genotyping and phylogenetic analysis are useful analytic methods that are highly effective at increasing understanding of genetic relationships and molecular epidemiology . Multiple molecular methods have been applied to genotyping Y . pestis , including SNP ( Single Nucleotide Polymorphism ) [9–11] , MLVA ( Multiple Locus VNTR Analysis ) [12–14] , CRISPR ( Clustered Regularly Interspaced Short Palindromic Repeat ) [15 , 16] , DFR ( Different Region Analysis ) [17] , and IS ( Insertion Sequence ) [18] . These methods each have their own advantages and disadvantages for phylogenetic analysis . For example , data from genome-wide SNPs provide the highest resolution , but the cost of genome sequencing numerous samples from a population remains high . Use of a small subset of SNPs , as in our study , provides relatively low resolution . The same applies to the CRISPR method , because only three spacer arrays are available in Y . pestis [19 , 20] . MLVA seems to provide a high resolving power , but the high mutation rate of VNTR ( Variable Number Tandem Repeat ) loci leads to a high homoplasy rate in phylogeny , reducing the reliability of deep branches [21] . The method that combines both the SNP and MLVA markers largely avoids the limitations associated with each method and has been successfully applied to plague epidemiologic analyses in Madagascar , providing reliable and high resolution phylogeny [20 , 22] . In this study , we introduced a hierarchical strategy based on SNP , MLVA and CRISPR methods , to investigate the population diversity of Y . pestis in Qinghai Plateau , and to correlate the geographic distribution with different lineages of this pathogen .
Y . pestis strains were collected from 32 counties in Qinghai Plateau , between 1954 and 2011 , during routine plague surveillance . For each county , if fewer than five strains were isolated since the initiation of surveillance , all historical isolates were used . For the county with more available Y . pestis isolates , five or six strains from different host/vector and sampling periods were selected . In total , 102 strains were used in this study ( Table 1 ) . Bacteria strain and its background information used in this study were provided by the Bacteria Specialized Laboratory of Yersinia pestis , Medical Bacteria Center of Management and Preservation , China . The Y . pestis cultures were incubated in Luria-Bertani medium at 28°C for 48 hours . Genomic DNA was then extracted using the conventional SDS-lysis and phenol-chloroform method . Overall , 25 SNP loci ( S1 Fig and S1 Table ) were selected to genotype the isolates of Y . pestis from Qinghai Plateau . An economical and timesaving PCR method , using the GenoType Tsp DNA Polymerase , was developed to identify the nucleotide status of SNP loci in this study . Brief details of the principle and procedure of the PCR are shown in S2 Fig , and the primers are listed in S1 Table . PCR was performed in a mixture of 15 μl volumes containing 100 ng of DNA , 0 . 5 μM of each primer , 0 . 2 mM dNTPs , 1 . 5 mM MgCl2 and 1 U Tsp DNA polymerase ( Cat . No . : 11448–032 , Invitrogen , USA ) . Amplification took place under the following conditions: pre-denaturation at 94°C for 1 min 30 s; then 10 cycles of 30 s at 94°C , 30 s at 50°C and 1 min at 72°C; 20 cycles of 30 s at 89°C , 30 s at 50°C and 1 min at 72°C; finally , an extension at 72°C for 10 min . The products underwent electrophoresis by agarose gel at 100 V for 20 min and the visualized result recorded as “0” ( negative ) or “1” ( positive ) , respectively . Additionally , conventional PCR and DNA sequencing using the Sanger method were employed to verify the SNPs identified . All the primers ( S2 Table ) were designed with reference to the CO92 strain genome . PCR was performed using the recommended PCR mixture and conditions by using the TaKaRa Ex Taq DNA polymerase ( Code No . : RR001 , TaKaRa ) . The purified PCR products were then sequenced with the Applied Bio-systems 3730 automated DNA Sequencer . SNPs were identified by comparison with the allelic genes of Y . pseudotuberculosis strain IP32953 , which is regarded as the most recent common ancestor ( MRCA ) of Y . pestis , using the DNAstar software package ( DNAstar Inc . , Madison , WI , USA ) . A total of 19 VNTR loci ( S3 Table ) were selected to screen the diversity of Y . pestis isolates by the capillary electrophoresis method , using an ABI 370 sequencer , as described previously [13 , 14] . The PCR products were labeled with four different fluorescent dyes ( Rox , 6-Fam , Hex , and Tamra ) . Amplicon sizes were monitored and calculated using Genemapper 4 . 0 software ( Applied Biosystems , Foster City , CA , USA ) . The strain CO92 ( GenBank accession number: AL590842 ) was used as a reference to estimate the motif copy number of VNTR loci for each isolate . The copy number was calculated using the following formula: R = Rc+ ( L–Lc ) /U , where R is the motif copy number of test isolates of Y . pestis , Rc is the copy number in the allele of the strain CO92 , L is the allele length ( bp ) of test isolates , Lc is the allele length ( bp ) of the strain CO92 , and U is the base number of the motif . Three CRISPR loci ( YPa , YPb , and YPc ) of 102 Y . pestis isolates were amplified using primers that separately targeted their flanking regions as described by Cui et al [16] . The PCR products were sequenced by using the Sanger method , and sequence assembly was performed using the Seqman module in the DNAstar package . The spacer identification and analysis of each CRISPR locus sequence was performed using the online tool CRISPRfinder ( http://crispr . i2bc . paris-saclay . fr/ ) , referring to the most recently published CRISPR spacer dictionary [22] . The nomenclature and abbreviation of CRISPR spacers were as described previously [16] . Phylogenetic analyses introduced a two-step hierarchical strategy to explore the genetic diversity and population structure of Y . pestis in Qinghai Plateau . First , we constructed the minimum spanning tree ( MSTree ) , based on binary character data of 25 SNPs of 102 Y . pestis isolates . Second , for the dominant SNP-defined group 1 . IN2 , the MLVA cluster analyses were performed using the Ward method and a CRISPR dendrogram , rooted as the basal composition ( a1-a2-a3-a4-a5-a6-a7 , b1-b2-b3-b4 and c1-c2-c3 ) of CRISPR loci , was created manually . The clustering procedure based on CRISPR spacers was performed according to the hypothesis that these spacers were originally from bacterial phage that carried their homologous sequences , i . e . the bacteria strains that carried the same spacer array would have same exposure history to different lineages of phages [16] . Both the MSTree and MLVA dendrogram were built using the software BioNumerics 6 . 6 ( Applied Maths , Belgium ) . The geographic distributions of strains were mapped using ArcGIS 10 . 2 ( ESRI , Redlands , CA , USA ) .
In this study , we employed key SNPs selected from previous research [9 , 10] to screen 102 isolates from Qinghai Plateau , to understand the phylogenetic structure of Y . pestis in this region . Seven groups ( 0 . PE7 , 0 . PE4 , 2 . MED3 , 2 . ANT2 , 3 . ANT1 , 1 . IN1 , and 1 . IN2 ) were recognized according to the MSTree based on 25 SNPs ( Fig 1A ) . Of these isolates , 84 were attributed to 1 . IN2 , representing the dominant population ( ~82 . 3% ) of Y . pestis in Qinghai Plateau . We also identified eight isolates belonging to the group 1 . IN1 , which differed from group 1 . IN2 in terms of the ancestral state of the SNP s1201 ( S1 Fig ) . The remaining 10 isolates were independently attributed to the other five groups , with four and three strains in 0 . PE4 and 3 . ANT1 , respectively , and one isolate in each of the other three groups ( Fig 1A and S4 Table ) . Analysis of the geographic distribution of isolates showed that the majority were from central-eastern and southern regions of Qinghai Plateau , with few isolated from north-western parts of Qinghai Plateau and the Tanggula region ( Fig 1B and S3 Fig ) . Qinghai Plateau is surrounded by multiple natural plague foci ( S4 Fig ) [17] , including a Marmota himalayana plague focus in the Gangdisi Mountains ( Focus G ) , a Spermophilus dauricus alaschanicus plague focus of the Loess Plateau in Gansu and Ningxia provinces ( Focus J ) , a Marmota himalayana plague focus in the Kunlun Mountains ( Focus K ) , and a Microtus fuscus plague focus in Qinghai and Sichuan provinces ( Focus M ) . Strains from groups including 2 . ANT2 , 2 . MED3 , 2 . MED2 , 3 . ANT1 , and 0 . PE4 have frequently been isolated in these plague foci [9 , 10 , 13 , 14 , 16 , 17] . Therefore , the majority of the non-dominant populations identified , including 0 . PE4 , 2 . ANT2 , 2 . MED3 , and 3 . ANT1 , may have been introduced by trans-regional diffusion events between adjacent plague foci . Considering previous observations [10] , ten 1 . IN1 strains have been identified to date , of which nine have been isolated from Qinghai Plateau and only one from Xinjiang Province , suggesting that Qinghai Plateau is the main focus of group 1 . IN1 strains , with export to other regions only occurring occasionally . Of all 102 strains from Qinghai Plateau , only one was identified as belonging to group 0 . PE7 , the oldest extant lineage of Y . pestis . Until now , only three 0 . PE7 strains have been identified , and all were from Xinghai County in Qinghai Plateau , identified during the 1960s [10] . This limited number of identified 0 . PE7 strains suggests a very small population size , or even extinction of this ancient lineage of Y . pestis strains . To investigate population diversity amongst isolates of the dominant population , we applied MLVA and CRISPR methods to screen isolates from group 1 . IN2 . Based on the diversity of 19 VNTR loci , all eighty-four 1 . IN2 strains were clustered into three subgroups , named 1 . IN2A , 1 . IN2B and 1 . IN2C , and 72 genotypes were identified ( Fig 2A ) . The CRISPR analysis revealed lower resolution than MLVA , with only 16 genotypes identified , but the subgroup clustering results were largely consistent with MLVA ( Fig 2B and S4 Table ) . We also found that specific spacer composition , with a35 for 1 . IN2C and a1’ but not a35 for 1 . IN2B , can be used to distinguish the subgroups . All three 1 . IN2 subgroups showed a clear geographic clustering pattern , with only a few strains isolated far from the location of their major population ( Fig 3 ) . The majority of 1 . IN2A strains ( 19 of 28 , 67 . 9% , Fig 3A ) were isolated in the Yushu Plateau ( Region A in S3 Fig ) , with five of the remaining strains isolated at Tanggula region , the west plateau to the Yushu Plateau , and the other four strains at the southern part of Qinghai Lake ( Fig 3A ) . Four out of five of the 1 . IN2A strains ( 1 . IN2A_1–4 in Fig 3A ) , located in the southwest of Qinghai Plateau , formed a monophyletic cluster in the cladogram of MLVA . Concerning the isolation time of the four strains , this cluster of strains may have been sustained in the same locality for over 30 years . However , the four strains located in the region surrounding Qinghai Lake and the Huangnan region ( 1 . IN2A_16 , 1 . IN2A_20 , 1 . IN2A_21 and 1 . IN2A_26 in Fig 3A ) , scattered at different branches on the cladogram of MLVA , suggesting that these four strains were very likely to have spread from Yushu Plateau to the isolation location through independent events . Interestingly , three of four strains were isolated from M . himalayana , implying the role this species may have in long distance transmission of Y . pestis . Subgroup 1 . IN2B ( Fig 3B ) was distributed in two separate regions , one located at the southern foot of the Qilian Mountains ( Region C in S3 Fig ) and the second located in Huangnan region ( Region D in S3 Fig ) . Notably , one 1 . IN2B strain ( 1 . IN2B_12 in Fig 3B ) , which was isolated from Ovis aries ( Tibetan sheep ) , was located at the southern edge of Qinghai Plateau , which is a long distance from the other 1 . IN2B populations . As domestic livestock , Tibetan sheep have a very close relationship with humans , implying that the long distance spread of this strain may be related to human activity . Subgroup 1 . IN2C ( Fig 3C ) was mainly distributed encircling Qinghai Lake and expanded to both the west and east sides ( Region B in S3 Fig ) . Only one 1 . IN2C strain ( 1 . IN2C_13 in Fig 3C ) , sourced from humans was isolated at the most southern part of Qinghai Plateau . As the dominant group of Y . pestis , the number of 1 . IN2 strains isolated each year can be used as an indicator of plague prevalence in Qinghai Plateau . Accordingly , phylogeographic analysis of 1 . IN2 subgroups isolated during different time periods was conducted to explore the epidemiology of plague over the past 60 years in Qinghai Plateau ( Fig 4 ) . Evaluating trends in prevalence of the entire group of 1 . IN2 strains between 1954 and 2011 demonstrates the periodic plague outbreaks that have occurred in Qinghai Plateau in this time ( Fig 4A ) . The frequency of plague outbreaks increased between 1954 and 1980 , reaching its peak in the 1970s . Plague prevalence then declined during the 1980s and 1990s , with plague cases increasing again after 2001 . Our molecular epidemiologic analysis has also revealed an interesting dynamic fluctuation in prevalence of each subgroup of 1 . IN2 in different periods ( Fig 4B–4D ) . Only a few 1 . IN2A strains were isolated before 1970 ( Fig 4B ) . During 1971–1990 , this subgroup was frequently isolated in Yushu Plateau and several strains were identified at locations far from its major population . After the 1990s , the population of 1 . IN2A strains appears to have decreased substantially and was only isolated in Yushu Plateau . Both 1 . IN2B and 1 . IN2C subgroups seem to have shifted to new locations over recent decades . Before 1970 , most 1 . IN2B strains were located at the southern foot of the east Qilian Mountains ( Region C in S3 Fig ) , but during 1971–1990 , only two strains were isolated in Region B ( S3 Fig ) , and the subgroup appears to have shifted to Huangnan region ( Region D in S3 Fig ) . During the 1990s , only three 1 . IN2B strains were isolated in Region D ( S3 Fig ) , and after 2000 , no 1 . IN2B strains were isolated across the whole of Qinghai Plateau ( Fig 4A ) , suggesting that this subgroup of Y . pestis has become dormant or even extinct . Strains of 1 . IN2C were initially isolated to the east of Qinghai Lake , and the regions closely surrounding the Qinghai Lake ( 1954–1970 ) , and then spread to the west of Qinghai Lake during 1971–1990 . From 1990 onwards , 1 . IN2C strains appeared to have left the area surrounding Qinghai Lake and spread to regions to the west and south of it .
We have shown that there is a wide geographic distribution of Y . pestis in Qinghai Plateau . It is distributed mainly in the northeast , mid-east and southwest regions , including the southern foot of the Qilian Mountains , surrounding the Qinghai Lake region , the eastern Qaidam Basin , the Huangnan region , and Yushu Plateau ( Fig 1B and S3 Fig ) . This distribution of Y . pestis is related to the distribution of its primary reservoir , M . himalayana . It is speculated that M . himalayana played an important role in the evolution of Y . pestis from Y . pseudotuberculosis [23] . As expected , no Y . pestis strain was isolated from the Hoh Xil , the biggest nature reserve in China , situated in the west of Qinghai Plateau , because of its high altitude and low population density of M . himalayana . Interestingly , although a high M . himalayana population density has been observed in the southeast part of Qinghai Plateau , epidemiological surveillance failed to obtain any isolates of Y . pestis from the reservoirs in this region; only positive serologies of Y . pestis F1 antibody have been detected [24] . Further investigation is required to confirm the presence of a natural plague focus in this region . Qinghai Plateau played a critical role in ancient commercial exchange in China because it was the intersection between the main ancient trade routes , including the Silk Road , the Tang-Tibet Ancient Road , and the Tea Horse Road ( Delamu ) . Therefore , the coexistence of multiple phylogenetic groups of Y . pestis in Qinghai Plateau might be related to human activities , such as cultural and commercial exchange between Qinghai and Tibet , which transferred plague pathogens between regions [10] . However , the long-distance spread of Y . pestis by human activity appeared to have a less important role in shaping plague foci during the modern age . The occasional human-related long-distance Y . pestis transmission event observed in this study , such as 1 . IN2C_13 ( Fig 3C ) , did not expand the original plague focus or establish a new population after transmission . In the current era , the influence of human activities on the distribution of plague foci seems to act in a different way , by driving transmission of Y . pestis through changing its local niche and destroying the natural environment that is necessary for Y . pestis survival . Before the 1970s , plague outbreaks were reported mainly in the eastern part of Qinghai Plateau , but subsequently , frequency of epidemics in the region gradually reduced and their occurrence began to shift towards the west of Qinghai Lake . After the 1990s , no Y . pestis strains could be isolated from the east side of Qinghai Plateau and the region closely encircling Qinghai Lake , which is the current economic center of Qinghai Province and the location of its capital city ( Fig 4 ) . Possible reasons for the extinction of Y . pestis in this region include active anti-plague interventions that have been implemented there following each outbreak; agricultural development , such as rapeseed cultivation , and development of tourism in the Qinghai Lake region , such as the international Tour of Qinghai Lake cycling race , held since 2002 . All these activities reduce marmot population density , which in turn impacts the natural focus of Y . pestis , driving it towards the west of Qinghai Plateau , where there is much lower human population density , compared with the east . Understanding the diversity of Y . pestis and its phylogeographic distributions will help in the design of tailored interventions for plague control . In Qinghai Plateau , we found seven Y . pestis groups that coincided with those we previously reported based on SNP analysis [10] . Group 1 . IN2 is the dominant genotype in Qinghai Plateau , with a few minor groups scattered among the dominant ones . The non-dominant genotypes are genetically distinct from the dominant ones surrounding them , indicating that the non-dominant genotypes have possibly been transmitted from other regions , rather than having descended from the nearby dominant genotype . Our finding that one dominant group and several non-dominant groups coexist in Qinghai Plateau is consistent with a previous observation that major and minor genomovars of Y . pestis coexist in the majority of natural plague foci in China [17] . The dominant groups should play a greater role in sustaining a plague focus , while the non-dominant groups are sporadic and play a lesser role in maintaining its stability . MLVA and CRISPR methods provide higher resolution than 25 selected SNPs in distinguishing the dominant Y . pestis population . The MLVA method splits the 1 . IN2 group into three subgroups , interestingly , we observed a geographically clustered distribution of different Y . pestis subgroups in Qinghai Plateau . Host adaptation is one of the potential drivers to shape distribution of Y . pestis , however , in all 48 animal-derived 1 . IN2 strains , 79 . 17% are isolated from M . himalayana ( Fig 3 ) . The remaining 10 strains were from five different species of mammals and distributed throughout the phylogenetic tree , suggesting no obvious evidence for host adaptation-derived evolution for Y . pestis in this region . The geographically constrained bacteriophage might be another possible driver that has led to the current distribution of 1 . IN2 subgroups . It is known that the spacers in CRISPR loci are the legacy of the battle between a bacterium and bacteriophages [25] . Each geographically-specific 1 . IN2 subgroup could be determined by specific spacers , suggesting the bacteriophage that carried the spacer-identical sequences is present in the corresponding regions . To explore the role of phages in microevolution of Y . pestis further , large scale sampling and sequencing of bacteriophage is needed in future work . In addition to these findings , we observed periodic fluctuations in epidemics caused by different Y . pestis subgroups , such as 1 . IN2A and 1 . IN2C , suggesting that possible periodic variations in the size of each subpopulation might be influenced by many factors , including climate , vegetation , reservoir populations , vector distributions and bacteriophages . This emphasizes that future plague surveillance should collect a wider range of data than currently , so that improved and refined plague prevention and control measures can be designed and implemented . In summary , the plague-endemic region of Qinghai Plateau still has considerable risk of outbreaks , especially in central and western areas , threatening transmission to other regions of China and worldwide . Systematically understanding the phylogeographical features of Y . pestis in this region will help us to implement countermeasures to prevent and control this deadly disease . | Plague is a highly infectious disease caused by the Yersinia pestis bacterium . Since the first strain of Y . pestis was isolated in Qinghai in 1954 , confirmed plague cases have occurred nearly every year , and more than 240 people have died from plague over the past 60 years . In this study , we analyzed 102 Y . pestis strains collected from Qinghai Plateau between 1954 and 2011 . We determined their genetic diversity and inferred their spatial-temporal distribution , based on genetic markers including SNPs , VNTRs and CRISPRs . Our results indicate that 1 . IN2 is the dominant group of Y . pestis in Qinghai Plateau , and its three subpopulations revealed clear geographic clustering that might be driven by interaction with bacteriophages . We observed that the Y . pestis population has moved from the east of Qinghai Plateau to central and western regions over the past 60 years . We also found that the abundance and relative proportion of 1 . IN2 subgroups varied over time , leading to fluctuations in plague epidemics . These results extend our knowledge of the genetic diversity of Y . pestis , and its population dynamics in natural plague foci over a number of years . With ongoing risk of outbreaks , we recommend enhanced surveillance in this region . | [
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"synthet... | 2018 | Genetic diversity and spatial-temporal distribution of Yersinia pestis in Qinghai Plateau, China |
Influenza A viruses ( IAV ) are commonly used to infect animal cell cultures for research purposes and vaccine production . Their replication is influenced strongly by the multiplicity of infection ( MOI ) , which ranges over several orders of magnitude depending on the respective application . So far , mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields . To address this issue , we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection . This modification does not only enable the fitting of high MOI measurements , but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters . Furthermore , this model allows the investigation of defective interfering particle ( DIP ) propagation in different MOI regimes . The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release . Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while , in low MOI conditions , it is predominantly controlled by the de novo generation of DIPs . Overall , the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use .
Influenza A virus ( IAV ) is an enveloped , segmented , single-stranded RNA virus that infects humans , livestock and various wild animals . IAV has been in the focus of basic and applied research for decades , but still poses a considerable risk to public health . Current annual epidemics cause up to five million severe infections and at least half a million deaths worldwide [1] . Historically , influenza pandemics have the potential for hazardous impacts with up to one hundred million deaths [2] . Vaccination provides protection against infection but vaccine composition has to be adapted seasonally to the most prevalent strains . Influenza vaccine is manufactured mainly in embryonated chicken eggs , an established process dating back to the middle of the 20th century . The egg-based vaccine production is constrained by scale-up restrictions , low yields for some virus strains , and potential allergic reactions [3–5] . Cell culture-based production is considered as an alternative to overcome these limitations . Cell cultures provide scalability and controlled sterile process settings in bioreactors [3 , 4] . However , cell culture-based influenza vaccine production is still facing challenges regarding yields , process costs and the adaptation of seed viruses to the desired cell line . Deeper insights into the virus replication and spread in cell cultures in different infection conditions are vital to overcome these challenges . In general , infection spread in cell cultures is related to the number of infectious virus particles per cell ( multiplicity of infection , MOI ) while process yields are directly correlated to the cell concentration and the number of virions released by a cell ( cell-specific virus yield ) . Cells infected by IAV release a mixture of different particles , including ( I ) infectious , fully functional particles , ( II ) non-infectious particles with various defects prohibiting virus entry and reproduction , ( III ) non-infectious defective interfering particles ( DIPs ) that may impede viral infection and reduce virus yields [6–9] , and ( IV ) deformed , broken or empty virions [4] . Understanding the interplay between these particles and how they impact seed virus quality , infection conditions , and virus yields could be a key factor for the optimization of cell culture-based influenza vaccine production . Mathematical modeling has proven to be a valuable tool for the investigation and analysis of biological systems . IAV infection dynamics was examined in depth with model-based studies that predominantly focus on the cell population level disregarding processes inside an infected cell [10–13] . Such models mostly investigate the host cell immune response and the prevention of virus spreading , which limits their applicability for cell culture-based influenza vaccine production . Other studies investigated intracellular processes , i . e . virus entry and replication , with deterministic [14 , 15] and stochastic approaches [16 , 17] . However , by disregarding virus spread these models are not able to describe infection dynamics in a cell culture . Recently , mathematical models were employed to examine the effect of DIPs on IAV infection [18–20] . Furthermore , the periodic accumulation of DIPs was observed in a continuous cell culture-based virus production setup [3] . However , mathematical models of IAV replication have paid little attention to process and infection conditions , e . g . the MOI . Differences in the MOI can exert a significant influence on the infection dynamics of a single cell and the whole cell population . Furthermore , the MOI is an important factor for yields in animal cell cultures as shown in several experimental studies [21–23] . Testing different infection conditions for IAV production , high MOI regimes lead to a fast drop of viable cell concentrations and lower virus yields than infections in low MOI conditions . However , the optimal MOI for virus production is strain-dependent [22] and presumably affected by various process parameters and the cell lines used . Accordingly , a mathematical model that accounts for the influence of different MOIs on intra- and extracellular processes during IAV infection in cell cultures could certainly support the optimization of vaccine production processes . In our group a multiscale model describing IAV infection of mammalian cell cultures was developed recently [24] . Here , we present an extended version of this model that particularly considers effects of infection conditions on virus dynamics . To this end , we introduce new mechanisms that adjust virus-induced apoptosis , virus release , and inhibition of viral mRNA synthesis to describe infection dynamics in different MOI regimes . We demonstrate that the extended model closely describes previously published viral RNA , virus release , and cell population measurements from adherent MDCK cells infected at a high MOI [4] . We then use the model to successfully predict virus release dynamics for cell culture infections performed in lower MOI conditions . Finally , we show how the MOI in a cell culture evolves during the infection and how this influences DIP propagation .
The model presented in this publication is based on a multiscale model of IAV infection in animal cell cultures by Heldt et al . [24] . This original model combines intracellular virus replication with virus propagation on the cell population level . On the intracellular level , various steps of virus replication including viral RNA synthesis , viral protein translation , and virion release are implemented in detail . The extracellular level describes population kinetics of uninfected cells , infected cells , apoptotic cells , and free virions ( infectious virus particles ) . The two levels of viral infection are linked by connecting the intracellular dynamics to a segregated population of infected cells . This segregation considers the time that has passed since cells were infected , i . e . the infection age τ . Virion release on the intracellular level , which is dependent on the infection age , is linked to the segregated infected cell population . Thus , the intra- and extracellular level of IAV infection can be simulated simultaneously . The original model was calibrated to measurements from two separate experiments performed at a high MOI for the intracellular virus dynamics and a low MOI for the extracellular part . Fortunately , in a recent publication , both intra- and extracellular IAV replication dynamics in MDCK cells were analyzed in high MOI conditions [4] . To that end , an MOI of 10 PFU ( plaque forming units ) per cell was used , which correlated to an MOI of about 73 based on TCID50 assay results . This high virus load was applied to achieve a single-cycle infection and resulted in a rapid infection of cells . Consequently , all cells were infected in a confined time frame leading to similar cell infection ages . This enabled a profound investigation of the cell population dynamics over the time course of infection as we do not observe a mixture of cells at different stages of infection . A recalibration of the original model to measurements provided by this newer study showed mixed results . While levels of viral cRNA , vRNA ( S1 Fig ) and virion release ( Fig 1A ) could be reproduced , cell population and viral mRNA dynamics ( S1 Fig ) differed considerably from the experimental data . Additionally , model predictions performed with the original model fitted for high MOI measurements from [4] did not capture viral release dynamics observed in new infection experiments performed at lower MOIs ( Fig 1B and 1C ) . We therefore decided to augment the original model to enable the description of IAV infection dynamics for a wider range of MOI conditions , e . g . in low MOI regimes used in cell culture-based influenza vaccine production . First , we focused on the description of cell population dynamics during the IAV infection of cell cultures . On the extracellular level the model considers uninfected cells , infected cells and apoptotic cells of both cell populations . Previous measurements of a single-cycle IAV infection of adherent MDCK cells showed that apoptotic cells start to accumulate about 16 hours post infection ( hpi ) [4] . To describe this delay , which is not considered in the original model ( Fig 2A and 2B ) , we adjusted the apoptosis rate of infected cells ( Eq ( 9 ) ) . The original model assumed an exponential distribution of the survival time of infected cells , which was implemented by a stepwise increase of the apoptosis rate to a fixed value after infection [24] . In a previous study , Holder et al . suggested that normal distributions are well suited to describe the time cells spend in a specific state [25] . However , to introduce an infection age-dependent apoptosis rate , the cumulative density function of the normal distribution is required . This cumulative density function utilizes the Gauss error function and does not have a closed analytical form of estimation [26] . Therefore , we checked other approaches that approximate normal distribution-like dynamics while still being easy to handle , i . e . a logistic function , Hill kinetics , and a Gompertz function , based on their capability to introduce sigmoid dynamics . All of these functions described the time course of virus-induced apoptosis relatively well ( S3 Fig ) . Finally , we decided to use a logistic function to describe the apoptosis rate of infected cells ( Eq ( 9 ) ) as it provides two benefits . The logistic function allowed the best fit of the experimental data and , in contrast to a Hill kinetic , the introduced parameters can be readily interpreted , i . e . τApo as the time after cell infection at which the rate of virus-induced apoptosis reaches its half-maximum , and νApo as a factor that describes the time required from cell infection until the full activation of the apoptosis mechanism . Fig 2C shows the difference between both scenarios . The original model uses an apoptosis rate that instantly increases after cell infection while the extended model establishes a delayed , gradually increasing rate . Hence , the implementation of an infection age-dependent rate of virus-induced apoptosis enables a better description of cell population dynamics observed in a single-cycle IAV infection of adherent MDCK cells ( Fig 2A and 2B ) [4] . Next , we extended the original model to consider the number of infectious virions ( determined by infectivity assays , e . g . TCID50 ) and the total number of virus particles ( determined by the HA assay ) separately . This characteristic was not accounted for in the original model , which solely focused on “virions” ( infectious virus particles ) . However , the concentration of total virus particles holds valuable information for influenza virus production as it correlates with process yields for manufacturing of inactivated vaccines . Accordingly , we implemented the virus particle release of an individual infected cell ( Eq ( 2 ) ) and defined the release of infectious virions as a fraction of the overall release ( Eq ( 3 ) ) . As shown in [4] , measurements of virions released indicate that the ratio of infectious to total virus particles release is not constant over the course of infection . In MDCK cell cultures infected with A/Puerto Rico/8/34 ( PR8 ) at an MOI of 73 , the initial fraction of infectious virions released ( FIVR ) was about 4% and decreased over time to about 0 . 3% ( Fig 3A ) . As a result , most virions released during late infection were non-infectious . The variable FPar ( τ ) ( Eq ( 4 ) ) describes the FIVR by an infected cell at a certain infected cell age τ . Generally , the FIVR represents influencing factors that affect the quality of the released virus particles . Plausible effects impacting it are the accumulation of DIPs or limited precursors for viral protein and RNA synthesis due to a rapid replication of the virus . The dynamics of FPar is defined as a first order degradation to reproduce the observation that infected cells release higher percentages of infectious virus particles immediately after infection compared to later time points ( Fig 3A ) . Using this infection age-dependent variable , the model captures both infectious and overall virus particles released by an individual infected cell ( Fig 3B ) . To describe the total amount of virus particles on the population level , we introduced the variable PtotRel ( Eq ( 6 ) ) , which can be correlated to IAV production yields . Thus , these model extensions enable the description of both the cell-specific yield and overall viral titers during IAV production in cell cultures . The final step of model extension was to take details of intracellular viral mRNA dynamics into account . After implementation of the aforementioned changes , the description of viral mRNA dynamics still showed deviations from the measurements ( Fig 4A , dotted line ) . While the initial accumulation of viral mRNA and the resulting peak could be captured , its following degradation could not be fully reproduced . Previous experimental studies of IAV infection in high MOI conditions indicated that a complete shutdown of viral mRNA synthesis occurs at about 6 hpi [27] . The original model did not describe such a rapid shutdown and viral mRNA synthesis continued during late infection ( Fig 4B ) . To capture such dynamics another extension of the mathematical model , i . e . the inhibition of viral mRNA synthesis , was necessary . Viral mRNA is transcribed from viral genome templates by hijacking the mRNA transcription mechanism of the host cell . To that end , viral RNA-dependent RNA polymerase ( RdRp ) binds to the cellular polymerase 2 ( Pol II ) and snatches precursors of cellular mRNA , which are then used to transcribe viral mRNA [28] . Recent studies have shown that RdRp has two different mechanisms of interaction with Pol II [29 , 30] . When RdRp carries a copy of the viral genome , the binding to Pol II induces viral mRNA synthesis . However , binding of Pol II without this template leads to the specific degradation of Pol II . The reduction of Pol II levels impedes viral mRNA transcription and could lead to a shutdown as observed in [27] . Additionally , simulation results of RdRp accumulation coincide with the decrease of viral mRNA synthesis , further supporting this hypothesis . To keep kinetics simple , we implemented an inhibition of viral mRNA synthesis by free RdRp ( Eq ( 5 ) ) . This indirect approach was used as the inclusion of a variable for Pol II without supporting measurements for parameter estimation would have unnecessarily increased model complexity . As before , accounting for this mechanism in the extended model improved the fit to measurements for an MOI of 73 , which is affirmed by a lower sum of squared residuals and the Akaike information criterion [31] ( Table 1 ) . Furthermore , the extended model with inhibition of the viral mRNA synthesis allowed a more precise description of viral mRNA levels at time points later than 12 hpi ( Fig 4A ) and the simulated mRNA synthesis rate showed dynamics similar to previous studies [27] ( Fig 4B ) . The extended model containing the adjusted apoptosis dynamics , virus particle release , and inhibition of viral mRNA synthesis was calibrated to previously published measurements from IAV infections of adherent MDCK cells performed at an MOI of 73 [4] . Experimental data of the intracellular level ( i . e . viral RNA dynamics ) and the extracellular level ( i . e . cell population and viral release dynamics ) were utilized . The model was fit to both data sets simultaneously . Model simulation is in good agreement with the experimental data on both the intra- and the extracellular level ( S1 Fig ) . In particular , the early accumulation of viral mRNA and cRNA can be captured closely . However , the extended model underestimates the levels of vRNA between 3 to 8 hpi ( S1 Fig ) . The cell population dynamics , i . e . the fast progress of cell infection and initiation of virus-induced apoptosis , are described well . All cells are infected at 1 hpi due to the high MOI conditions and infected cells start to undergo apoptosis around 16 hpi . The model slightly overestimates the onset of viral release on the population level , but reproduces later measurements for infectious virus particles and the total number of virions . The delay between cell infection and first virus release at MOI 73 constitutes roughly 3 h ( Fig 3B ) , indicating that the high viral load results in fast uptake and intracellular replication of virions . In a next step , we investigated the predictive power of the extended model calibrated for MOI 73 measurements [4] , by challenging it with new experimental data from MDCK cell infections performed in lower MOI conditions . To that end , we conducted IAV infections at MOIs of 3 and 10−4 based on TCID50 to cover a broad range of infection scenarios . We performed the experiments according to the protocol used for the high MOI infections of MDCK cells [4] and measured time courses of infectious and total virus release . First , we simulated the infection dynamics of different MOI conditions by exclusively changing the initial amount of infecting virus particles . Unfortunately , this approach did not result in satisfying predictions ( Fig 5 , solid lines ) . While the total virus particle concentration for an infection at MOI 3 based on TCID50 could be described ( Fig 5B ) , other measurements differed significantly from model simulations in both dynamics and magnitude of viral release . Additionally , we observed clear differences in the FIVR dynamics between simulations and low MOI measurements . In experiments performed at MOI 3 and 10−4 ( based on TCID50 ) , the FIVR initially shows considerably higher values ( Fig 5A and 5D ) than at MOI 73 ( Fig 3A ) . This indicates that cells infected at lower MOIs have a significantly higher ratio of infectious to non-infectious virus particles released than cells infected at a high MOI . Consequently , to improve the model prediction , we adjusted the initial FIVR FPar ( 0 ) to low MOI conditions . To only introduce a single new FPar ( 0 ) for the low MOI conditions we tested different values and their effect on model predictions for MOI 3 and 10−4 based on TCID50 ( S5 Fig ) . We found an optimal value with FPar ( 0 ) = 0 . 26 , which provided the best description of the FIVR for both low MOI conditions ( Fig 5A and 5D ) . As a result , the model predictions for virus release using the adjusted initial condition are in good agreement with the two experiments performed at lower MOIs ( Fig 5 ) . In particular , the time delay before cells start to release considerable amounts of virus particles , which is heavily dependent on the MOI conditions , can be well described . Thus , by considering the influence of a critical initial condition , i . e . FPar ( 0 ) , during influenza virus infection our model captures the viral release dynamics of both high and lower MOI infections in MDCK cell cultures . In addition to viral titers the model is able to predict various intra- and extracellular processes during the IAV infection of cell cultures in different MOI conditions . As an example , we simulated the fraction of cells infected and the ratio of infectious virions to non-infected cells ( the effective MOI ) over the time course of virus replication at MOIs of 73 , 3 and 10−4 ( Fig 6 ) . The effective MOI can change considerably over the progress of infection and determines how many virions enter a cell at a specific time point influencing virus replication and release . In high MOI conditions , cells are infected rapidly until 1 hpi and the effective MOI increases instantly . Simulations with an MOI of 3 result in a slower progress of infection that shows two peaks indicating a second infection wave starting around 4 hpi . Here , the effective MOI stays constant up to 4 hpi and then increases until all cells are infected . Overall , most cells in infections performed at initial MOIs of 73 and 3 are infected at similar effective MOIs . The simulation of an infection performed at an initial MOI of 10−4 shows very different results . The infection progress is delayed considerably and the model prediction suggests that most cell infections occur not before 15 hpi . Additionally , the effective MOI decreases until 4 hpi and only then starts to increase gradually . The dynamics of the effective MOI progresses in multiple waves showing step-like behavior around 4 , 11 and 17 hpi . This strong variation of the effective MOI during the process induces very different infection scenarios for cells infected at different time points . Additionally , the majority of cell infections , around 66% , occur at an effective MOI of 3 or higher , despite the low initial MOI . In summary , the extended model predicts a rapid , uniform infection in high MOI conditions and a delayed progress of infection with variations in the effective MOI ( multiple waves ) in a low MOI scenario .
The mechanisms of influenza virus infection are governed by highly complex processes including various virus-host cell interactions , an adaptive viral defense against cellular immune responses , and a multi-layered regulation of virus replication and spread . Novel experimental and computational methods enable in-depth investigations of these processes . Here , we present an augmented mathematical multiscale model of IAV infection that closely captures experimental data for low and high MOI infection conditions on the intra- and extracellular level . With this model , we successfully predict virus release dynamics and show how the MOI affects the progress of infection . In contrast to the original multiscale model established by Heldt et al . [24] , the extended model was calibrated to intra- and extracellular data from the same experiment [4] . The high MOI infection conditions used in these experiments enabled the measurement of dynamics on both cellular levels . We used these measurements for parameter estimation and the resulting model simulation showed good agreement with the experimental data ( S1 Fig ) . Viral RNA dynamics and the percentage of cells in different states of infection and apoptosis were captured closely . However , between 3 and 8 hpi , model simulations underestimated the level of intracellular vRNA . Previous experimental studies also identified an accumulation of vRNA in this time frame [24 , 33] , which cannot be fully reproduced by the current model implementation . The various viral RNA species of IAV are highly interconnected , because vRNA serves as the template for viral mRNA and cRNA replication . The underestimation of a single viral RNA species indicated that an additional layer of regulation , which supports earlier vRNA accumulation without impacting the other species , may exist . Such a regulation could concern nuclear export processes of viral proteins , the depletion of precursors for viral RNA and protein synthesis impeding further replication or an increased degradation of vRNPs that enter the cytoplasm for release . To test such hypotheses , experimental approaches could analyze the metabolism of infected cells , the stability of vRNPs in the nucleus and cytoplasm , or the availability of specific resources for virus particle synthesis , preferably in a high MOI scenario . The most important step to reproduce infection dynamics in MDCK cell cultures at a high MOI was the description of apoptotic processes . In general , apoptosis is induced in infected cells as a defense mechanism aiming to reduce progeny virion release [34] . In infected cells , viral RNA and protein synthesis progress rapidly leading to an accumulation of viral molecules . This is detected by the cell and , as a reaction , apoptotic processes are induced that lead to controlled cell death to prevent further virus spread . In the original model [24] , infected cell apoptosis was described by a fixed rate ( Fig 2C ) , which resulted in an exponential distribution of the survival time of infected cells . However , experimental data of infected cells and their transition to an apoptotic state indicate a normal distribution of cell survival time ( Fig 2A and 2B ) [4] , which was discussed previously by Holder et al . [25] . To accommodate these characteristics , we implemented a logistic function ( Eq ( 9 ) ) for the description of apoptosis induction in infected cells . Logistic functions can approximate the dynamics described by a normal distribution and are relatively simple to apply [26] . Other approximation functions exist , which can provide an even closer representation of the normal distribution . However , these functions are more complex than the logistic function and not required to describe the experimental data . This approximation of the survival time of infected cells enabled the reproduction of the apoptosis dynamics measured in high MOI influenza A infection experiments [4] . Additionally , by utilizing a logistic function , we introduced a delay before significant amounts of cells undergo apoptosis , which was experimentally observed until 16 hpi ( Fig 2 ) . This delay is represented by the newly introduced parameter τApo , which describes the time frame in which an infected cell induces apoptosis . Therefore , a low value for τApo indicates that the cell can induce a fast response to the infection . However , viruses have developed mechanisms to interfere with the host cell apoptosis to prolong virus production [34] . Thus , the parameter τApo could also show how well a virus is adapted to the host cell . The delayed apoptosis induction described in our extended model is similar to the cell death dynamics during HIV infection described in [35] . For the latter , a piecewise-defined function that includes a specific time delay was utilized to achieve an effect similar to the one resulting from the use of a logistic function in our model . Nevertheless , dynamics of influenza-induced cell death is highly strain-dependent [36] , which could require different approaches based on the respective scenario . For model calibration , we utilized accumulated virus titer measurements , which show the amount of virus particles produced since the previous sampling time point . Thus , the figures presented do not show virus degradation and the estimated rate of virus degradation , kVDeg , is one order of magnitude lower ( Table 2 ) than described previously [13] . Furthermore , this approach leads to a slight underestimation of the cell-specific yield of infectious virions , because infectious virus particle degradation is not taken into account . To enable a description of infectious virus titer measurements that were not determined cumulatively , the parameter for virus degradation should be re-estimated or taken from the related literature [13] . Infected cells release infectious and non-infectious progeny virions , which can be determined via TCID50 and HA assay results . The ratio of released infectious virions to the total amount of virus particles released , which is described by the introduced variable FPar ( Eq ( 4 ) ) , is a measure for the efficiency of IAV replication in cell culture . The highest FIVR occurs during early infection , which is crucial to enable fast virus spreading before host defense mechanisms , i . e . apoptotic processes or cellular immune response , may interfere . Over time , the FIVR decreases and during later stages of infection non-infectious particles are released predominantly ( Fig 3A ) . A recent study [4] identified that during late infection ( starting at around 20 hpi ) the morphology of released particles changes , leading to more deformed or broken particles . This observation was linked to decreasing cell viability , the detachment of infected adherent cells and nuclear fragmentation indicating apoptotic processes in the infected cells . In addition , cellular metabolism is heavily affected by virus replication at later stages of infection [37] . Further investigations regarding the capability and bottlenecks of infected cells to release functional , infectious virions should be performed to determine the underlying mechanisms . Especially experiments that closer examine the composition of non-infectious particles , i . e . the fraction of DIPs , virions missing genomic information , and broken particles , in different MOI conditions would be valuable . This could advance the understanding of prerequisites for successful virus replication and release , which can support both vaccine production and antiviral strategies . Furthermore , the FIVR differed significantly between low and high MOI cultivations . In low MOI conditions , a considerably increased FIVR was observed ( Figs 3A , 5A and 5D ) . In particular , during experiments performed at an MOI of 10−4 based on TCID50 , the FIVR maintained a value above 20% until 24 hpi . This dynamics can be explained by the continuous infection of cells until 20 hpi ( Fig 6 ) which contributes with a high initial FIVR to the release characteristics of the infected cell population . Overall , the differences in viral release between low and high MOI infections have a significant impact on the infectious virus titer and the propagation of infections on the extracellular level ( Fig 1 ) . Although various factors influence the efficiency of virus replication and release , the presence of DIPs in high MOI seed virus and their accumulation over the time course of infection most likely play a key role regarding the observed disparity . Due to deletions in their genome , DIPs are non-infectious virus particles and , therefore , require the co-infection with an infectious virion for replication . During co-infection DIPs impair the replication of infectious virions , and co-infected cells predominantly release progeny DIPs [8] . In high MOI conditions nearly all cells are infected by more than one virus particle ( S4 Fig ) providing an optimal basis for DIP interference . Further support for a connection between the FIVR and DIP interference is provided by the correlation of DIP accumulation with the decrease of infectious virus particle release , which was observed in experiments performed at an MOI of 3 based on TCID50 ( S2 Fig ) . However , the influence DIPs exert on the molecular level of virus replication is still not fully understood . Additional studies examining DIP interference dynamics and the FIVR could elucidate critical factors controlling virus spread dynamics in cell populations , e . g . by testing the behavior of such properties for a wide range of infection conditions . MOI also heavily affects the progress of IAV infection in cell cultures . The higher the MOI , the more cells are infected and the earlier virus particles are released . Additionally , the initial MOI impacts the dynamics of the effective MOI resulting in highly different scenarios regarding the amount and the origin of infecting virions . In high MOI conditions , all cells are infected by the initially available virions in a single infection wave ( Fig 6 ) . In addition , such infections are with near certainty multiple-hit infections ( S4 Fig ) . Simulations of an infection at MOI 3 , however , show two different scenarios . Until 4 hpi cells are infected by infectious virions from the seed at an effective MOI that results in around 50% chance of inducing multiple-hit infections ( S4 Fig ) . In a second infection wave , starting at 4 hpi , progeny virions begin to infect cells at increasing effective MOIs leading to a higher multiple-hit infection probability . Model predictions for low MOI conditions ( 10−4 ) , which are applied typically in influenza vaccine production , show variations in the effective MOI that span 14 orders of magnitude ( Fig 6 ) . Using the MOI-sensitive modeling approach developed in this study enabled detailed analyses regarding such a scenario . Simulations show that effective MOI progresses in three infection waves starting at 0 , 4 and 11 hpi . In the first 4 hpi seed virions infect target cells exclusively in single-hit infections . From 4 to 15 hpi first and second generation progeny virions induce additional infection waves , in which still mostly single-hit infections occur as the effective MOI stays below one ( S4 Fig ) . Finally , the effective MOI increases steadily , so that 16 hpi mostly multiple-hit infections take place . Interestingly , over 64% of all cell infections occur after 16 hpi ( Fig 6 ) . Therefore , our model simulations predict that even in low MOI conditions the majority of cells are infected by multiple virions . Altogether , this indicates that cells in high MOI conditions are infected exclusively by the seed virus while in low MOI cultivations cells are infected almost exclusively by progeny virions . In both low and high MOI conditions mostly multiple-hit infections occur . However , during the initial phase of a low MOI infection predominantly single-hit infections take place . It would be valuable to assess these predictions by analyzing the dynamics of single- and multiple-hit infections in an infected cell culture using different initial MOIs . Furthermore , the model predictions provide interesting implications for the interference of DIPs with infectious virions for different MOI conditions . In our simulations , the majority of IAV-infected cells are hit by multiple virions , regardless of the MOI . Such conditions favor DIP replication as they increase the chance of co-infections by DIPs and infectious virions . Therefore , it could be argued that even low MOI conditions do not prevent DIP interference , but only postpone it to later infection stages ( Fig 6 ) . However , there is an important distinction between different MOI conditions regarding the seed virus . In high MOI conditions all cells undergo multiple-hit infections by virions exclusively from the seed virus . Therefore , the amount of DIPs in the seed virus determines the severity of interference . To achieve a low impact of DIPs in high MOI conditions a “clean” seed virus with very low DIP content is required . This is in particular relevant for experimental studies aiming for single-step virus growth to avoid artifacts . But even for studies in small scale cultures or laboratory scale bioreactors , the quality of the seed virus should be controlled carefully to avoid misinterpretation of experimental findings . In low MOI conditions , relevant for cell culture-based influenza vaccine production , virions from the seed virus infect cells almost exclusively in single-hit infections preventing DIP interference and replication at early cultivation time . In later infection stages , progeny virions of the second to third generation nevertheless induce multiple-hit infections . Thus , the amount of DIPs generated de novo [18] during progeny virion production has a higher impact on the extent of interference with virus yields and the DIP content of the seed virus only plays a minor role . Altogether , DIPs always affect the replication of IAV regardless of the initial MOI . But , the interference has a lower impact and is postponed until later infection stages in low MOI conditions . Another intriguing effect of different MOI conditions is their impact on viral mRNA dynamics . In high MOI conditions , viral mRNA accumulates rapidly ( Fig 4A ) , reaches a distinct peak around 4 hpi , and declines thereafter . Various studies showed similar findings in high MOI IAV infections in different cell lines [27 , 33 , 38] . In contrast , low MOI scenarios induce a considerably slower accumulation with less pronounced peaks around 8 hpi [24 , 39 , 40] . The fast accumulation in high MOI infections is most likely induced by the increased amount of available templates ( vRNA ) . The subsequent shutdown of viral mRNA synthesis is mediated by the export of vRNAs from the nucleus , which occurs around 4 hpi in high MOI experiments [4] . However , the first iteration of our extended model , which was modified by an adjusted apoptosis rate and the newly introduced FIVR , could not describe satisfactorily the shutdown of viral mRNA replication and its degradation ( Fig 4 ) . Model simulations showed a slow decrease of viral mRNA synthesis ( Fig 4B ) , which was counteracted by a high mRNA degradation rate ( kMDeg=0 . 6 ) to reproduce the drop of viral mRNA in the measurements . This rate is twice as high as a viral mRNA degradation rate determined previously [14] . Moreover , the dynamics of viral mRNA degradation is not captured fully ( Fig 4A ) . Thus , we implemented an interaction between viral and host cell mechanisms that was reported recently by Rodriguez et al . [29] and Martínez-Alonso et al . [30] . They showed that the binding of free viral RdRp to cellular Pol II leads to the degradation of the latter , which is proposed as a method for inhibiting host gene expression . In addition , this mechanism would impede viral mRNA transcription , which depends on Pol II activity , and provides an explanation for the observed shutdown . After implementation of this interaction , the extended model is in good agreement with the viral mRNA dynamics during a high MOI infection ( Fig 4A ) . Additionally , the viral mRNA degradation rate is now consistent with previous results ( kMDeg=0 . 3 ) . These findings indicate that the RdRp-mediated degradation of Pol II not only inhibits host gene expression , but also plays a role in the downregulation of the viral mRNA synthesis . In summary , we adjusted an existing mathematical multiscale model of IAV infection in animal cell cultures [24] . In contrast to previous models , it explicitly considers the infection age of a cell regarding apoptotic processes and the release of infectious virus particles , which enables the description of both high and low MOI scenarios relevant for basic research and vaccine production . MOI-sensitive models could also benefit research on other viruses , e . g . plant viruses , in which the MOI is theorized to have an impact on virus evolution [41] . Furthermore , this model can be used to examine specific steps in the IAV life cycle in relation to the maximum virus yield or regarding measures to efficiently intervene with viral spread in vivo . Given available experimental data , future work could encompass a multiscale DIP replication model advancing the understanding of DIP impact on viral replication at different MOIs and characterizing DIP production for establishment of antiviral therapies [42] . Additionally , the model could be used to describe infections in tissues and organs at the within-host scale–a scenario in which the spatiotemporal MOI fluctuates strongly . To accomplish the abovementioned goals , the incorporation of DIP propagation , the host immune response and a model expansion to the second or third spatial dimension have to be considered . Ultimately , the predictive multiscale model presented here is well suited to predict and optimize process performance of IAV production in cell cultures and provides a solid framework for further analysis of MOI-dependent virus infections in general .
The intracellular level of IAV infection is based on a model previously developed in our group [14] . In short , this model comprises a set of ordinary differential equations describing the essential steps of virus replication . These include virus entry , nuclear import , replication and transcription of viral RNA , protein synthesis , viral assembly and virus particle release ( Eqs ( S1 ) - ( S30 ) ) . Additionally , the model was modified according to [24] by adjusting the viral release rate to rRel ( τ ) =kRelVpM1CytVpM1Cyt+8KVRel∏jPjPj+NPjKVRel ( 1 ) where j ∈ {RdRp , HA , NP , NA , M1 , M2 , NEP} and τ denotes the infection age of a cell . Viral release is determined by the available viral proteins , Pj , and viral ribonucleoproteins ( vRNPs ) in the cytoplasm , VpM1Cyt . The parameters NPj and KVRel describe the number of viral proteins necessary for the formation of virus particles and the amount of viral components required to achieve half the maximum virus release rate , respectively . Based on this implementation , virus particle release is confined by the parameter kRel , which defines a maximum release rate . For a detailed discussion of the intercellular dynamics the reader is referred to the original publications [14 , 24] . We extended this model by assuming that an infected cell releases both infectious and non-infectious virus particles . Hence , we considered rRel as the release rate of all virus particles after intracellular replication and introduced the FIVR FPar ( τ ) to define the release of infectious virus particles . In the resulting equations rParRel ( τ ) =rRel ( τ ) ( 2 ) rInfRel ( τ ) =rRel ( τ ) FPar ( τ ) ( 3 ) rParRel ( τ ) and rInfRel ( τ ) describe the infection age-dependent release rates for all virus particles and infectious virions by an individual cell , respectively . Experiments in our group revealed that FPar ( τ ) is decreasing over time [4] . Consequently , we defined it as a first order degradation dFPardt=−kRedRelFPar ( 4 ) with kRedRel describing the observed decrease of infectious virus particle release . As the understanding of factors influencing the capability of cells to produce infectious particles is still limited [4] , we chose to combine such effects in one parameter , kRedRel . Furthermore , we introduced an additional step of viral RNA regulation , i . e . the inhibition of viral mRNA synthesis , suggested in [29 , 30] . The authors propose that free viral RdRp degrades cellular Pol II , which would impede viral mRNA synthesis . We implemented this interaction by adjusting the standard mRNA dynamics ( Eq ( S16 ) ) to dRiMdt=kMSynVpNuc8Li ( 1+PRdrpKR ) −kMDegRiM ( 5 ) with PRdRp as the concentration of free viral RdRp and KR describing the amount of free RdRp that has to be available to reduce mRNA synthesis by half . Synthesis and degradation rates of viral mRNA are defined by kMSyn and kMDeg , respectively . The amount of vRNPs in the nucleus , VpNuc , represents the available viral template and Li describes the segment-specific length of viral mRNA . The implementation of an inhibitory term based on the concentration of free RdRp enables a close description of the mRNA dynamics observed in high MOI conditions [4 , 33] by the extended model ( Fig 4A ) . The model of the extracellular level of IAV infection is based on conventional cell population balances and follows the approach introduced in [24] . In brief , a set of integro-partial differential equations is combined with a set of ordinary differential equations to describe extracellular interactions . These include an age-segregated infected cell population , the transition of uninfected cells to an infected or apoptotic state , cell growth and death , virion production as well as the attachment and endocytosis of virus particles to infect cells ( Eqs ( S31 ) - ( S47 ) ) . For the description of both infectious and total virus particles on the population level we adjusted the extracellular dynamics . The total amount of released virus particles was implemented as dPtotReldt=∫0∞rParRel ( τ ) I ( t , τ ) dτ ( 6 ) with I ( t , τ ) denoting the age-segregated infected cell population . To stay in line with the newly introduced release rates ( Eqs ( 2 ) and ( 3 ) ) the balance equation for infectious virions was reformulated as dVdt=∫0∞rInfRel ( τ ) I ( t , τ ) dτ−kVDegV+∑n[knDisVnAtt−kc , nAttBnV] ( 7 ) with n ∈ {hi , lo} , knDis and kc , nAtt defining the dissociation and association rates of infectious virus particles to uninfected cells , kVDeg as the rate of virion degradation , VnAtt referring to virions attached to the cell surface and Bn as the amount of free binding sites on the cell surface . As the experimental data provide measurements for the accumulated virus particle release , we introduced an additional equation describing the total amount of released infectious virus particles dVtotReldt=∫0∞rInfRel ( τ ) I ( t , τ ) dτ , ( 8 ) which disregards virus internalization and degradation . The apoptosis dynamics of the original model [24] was adjusted to comply with a normal distribution of the survival time of infected cells as indicated by recent experimental data [4] ( shown in Fig 2A ) . To this end , we employed a logistic function , which can be used to approximate dynamics induced by the cumulative density function of the normal distribution [26] , to describe the infection age-dependent cell apoptosis rate kIApo ( τ ) =KI1+exp ( −vApo ( τ−τApo ) ) ( 9 ) with KI as the maximum virus-induced apoptosis rate , τApo referring to the time after cell infection at which the rate of virus-induced apoptosis reaches its half-maximum and νApo as a factor that describes the time required from cell infection until the full activation of the apoptosis mechanism . The parameter τApo can be directly related to the mean μ of the normal distribution and νApo can be converted to the standard deviation σ of the normal distribution by calculating σ=1 . 62νApo . ( 10 ) Additionally , we tested Gompertz and Hill functions for the description of a delay in apoptosis induction ( S3 Fig ) as they can also approximate the cumulative density function of the normal distribution . Finally , we decided to implement a logistic function as it provides the best fit to our experimental data and enables a comprehensive adaptation to different scenarios including a step-like or smooth as well as an instant or delayed increase of the virus-induced apoptosis rate . For the purposes of this publication , a delayed and smooth rate increase of the infected cell apoptosis rate was utilized ( Fig 2C ) . Model simulation was handled according to [24] . In brief , the extra- and intracellular models were decoupled to reduce the necessary computational effort . To that end , we assumed that the intracellular dynamics in our model are independent of extracellular events and the time that passed since the cell culture was infected . The dynamics on the extracellular level are , however , dependent on the virus particle release on the intracellular level . The two levels are linked by utilizing a reduced version of the intracellular model that neglects virus entry . This process is handled instead on the extracellular level by Eqs ( S43 ) - ( S47 ) . Thus , for model simulations , we first evaluated dynamics of the intracellular model to determine rInfRel ( τ ) and rParRel ( τ ) . Then , the calculated viral release rates were applied in Eqs ( 6 ) – ( 8 ) to simulate the extracellular dynamics . The equations of the intracellular model ( Eqs ( S1 ) - ( S30 ) ) were solved numerically with the CVODE routine from SUNDIALS [43] on a Linux-based system . Model files and experimental data were processed with the Systems Biology Toolbox 2 [44] for MATLAB ( version 8 . 0 . 0 . 783 R2012b , TheMathWorks Inc . ) . The extracellular model ( Eqs ( S31 ) and ( S33 ) - ( S47 ) ) was solved with Euler's method at a step size of dt = 0 . 05 h . In Eqs ( S35 ) , ( S36 ) , ( S38 ) , ( S41 ) and ( S42 ) , the integrals were calculated by using Eq ( S37 ) in place of I ( t , τ ) and applying the rectangle method to approximate results . Model parameters were estimated by simultaneously fitting the intra- and extracellular model to the respective set of experimental data [4] . Measurements consisted of viral RNA dynamics ( Fig 4 , S1 Fig ) on the intracellular level as well as cell population dynamics and virus titers ( Figs 2 and 3 ) on the extracellular level . The reduced intracellular model , which is used to calculate the extracellular dynamics , utilized the same parameters as the complete intracellular model . To determine an optimal set of parameters , the global optimization algorithm fSSm for solving nonlinear problems [45] was employed . Individual estimation steps were evaluated by normalizing errors to their corresponding maximum measurement value . Then , the sum of errors for the intra- and extracellular level were divided by the respective number of measurements and finally combined to represent the overall goodness of fit . The first measurement value was applied as an offset to the simulated values of viral RNA to match a background signal observed in the real-time RT-qPCR experiments . Confidence intervals in Table 2 were determined by bootstrapping [32] based on the standard deviations obtained from three independent experiments ( S1 Fig ) . In S1 Table initial conditions for the parameter fit and model prediction are presented . The initial amount of virus particles in the intracellular model VEx ( 0 ) is based on the respective MOI with a minimum of one virion infecting a cell . Accordingly , the reduced intracellular model , which neglects virus entry , is initiated with VpCyt ( 0 ) =8moleculesvirionFFusVEx ( 0 ) ( 11 ) where FFus denotes the fraction of fusion-competent virions . The expression VpCyt ( 0 ) in the reduced intracellular model represents the amount of vRNPs that reach the cytoplasm after VEx ( 0 ) virus particles have infected a cell . We assume that at least one full set of vRNP infects a cell resulting in a minimum of VpCyt ( 0 ) = 8 molecules . All parameter values for the intra- and extracellular model are shown in S2 and S3 Tables , respectively . Predictions of viral release in low MOI conditions ( Fig 5 ) were performed by simulating the model with the parameters fitted for high MOI experimental data [4] . The initial FIVR was changed as newly collected measurements revealed significant differences between the percentages of infectious virus particles among the total release in high and low MOI conditions ( Figs 3A , 5A and 5D ) . For simulations at MOI 10−4 and 3 we applied the same initial FIVR , which was determined by testing a range of values from 0 to 1 to find an optimum for both conditions ( S5 Fig ) . The adherent MDCK cells ( ECACC , No . 84121903 ) used in infection experiments were grown in Glasgow’s minimum essential medium ( GMEM ) supplemented with 10% ( v/v ) fetal calf serum and 1% ( v/v ) peptone at 37°C and 5% CO2 atmosphere . The serum-free infection medium was composed of GMEM , 1% ( v/v ) peptone and trypsin ( 5 BAEE U/mL , Sigma-Aldrich , # T7409 ) . For the infections at MOI 3 and MOI 73 , the influenza virus strain A/Puerto Rico/8/34 ( PR8 ) obtained from the National Institute for Biological Standards and Control ( NIBSC ) was utilized . The infection at an MOI of 10−4 was performed with a PR8 strain from the Robert Koch Institute ( RKI ) . The seed virus titers were determined by TCID50 assay [46] as 1 . 29 × 109 virions/mL for the NIBSC and 1 . 1 × 109 virions/mL for the RKI strain . The virus infections at all MOIs were conducted according to the protocol in [4] . In the low MOI experiments , cells were infected at an MOI of 3 and 10−4 based on TCID50 . To achieve high MOI conditions , infections were performed at 10 PFU/cell [4] , which correlated to an MOI of about 73 based on TCID50 . Confluent cells in T75 flasks were washed twice with phosphate-buffered saline ( PBS ) and infected at an MOI of 10−4 , 3 or 73 in 3 mL infection medium for 1 h . The flasks were rocked every 20 min to maintain an even virus distribution and kept at 37°C in a 5% CO2 atmosphere . Then , the inoculum was removed and the cells were washed twice with PBS . For the analysis of viral RNA dynamics ( real-time RT-qPCR ) and the fraction of cells in different infection states ( imaging flow cytometry ) , performed at MOI 73 , 13 mL of infection medium was added to the flasks . One individual flask was used for assaying every sampling time point . For a detailed description of the real-time RT-qPCR and imaging flow cytometry procedures the reader is referred to [4] . To analyze viral release kinetics , 5 mL infection medium was added after washing . Every 4 h , the supernatant was harvested . After removal of the supernatant , another 5 mL infection medium was used to wash the cells and added to the harvest . Finally , the flask was resuspended with 5 mL fresh infection medium for further incubation . Samples of the harvest were stored at -80°C until further analysis . Infectious and total viral titers were determined by TCID50 assay [46] and HA assay [47] , respectively . The total virus particle titer is measured as log10 HA units per test volume ( log HAU/100 μL ) . By assuming that the number of erythrocytes ( 2 × 107 cells/mL ) used in this assay is directly proportional to the number of virus particles required for agglutination , the concentration of hemagglutinating particles per mL is given by [48]: cVirus=2⋅107⋅10 ( logHAU/100μL ) ( 12 ) | Influenza is a contagious respiratory disease that severely affects several million people each year . Vaccination can provide protection against the infection , but vaccine composition has to be adjusted regularly to remain effective against this fast evolving pathogen . While influenza vaccines are mostly produced in embryonated chicken eggs , cell culture-based vaccine production is developing as an alternative providing controlled process conditions in closed systems , better scalability , and a short response time in case of pandemic outbreaks . Here , we employ a computational model to describe underlying mechanisms during the IAV infection in adherent MDCK cells . Special attention was paid on the influence of the MOI on virus spread in cell populations . Although dynamics between infections with high and low amounts of infecting virions differ significantly , our model closely captures both scenarios . Furthermore , our results provide insights into IAV-induced apoptosis and the switch from transcription to replication in intracellular IAV replication . Additionally , model simulations indicate how virus particle release is regulated , and what impact defective interfering particles have on virus replication in different infection conditions . Taken together , we developed a computational model that enables detailed analyses of IAV replication dynamics in animal cell culture . | [
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"simula... | 2019 | Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions |
There is growing consensus that genome organization and long-range gene regulation involves partitioning of the genome into domains of distinct epigenetic chromatin states . Chromatin insulator or barrier elements are key components of these processes as they can establish boundaries between chromatin states . The ability of elements such as the paradigm β-globin HS4 insulator to block the range of enhancers or the spread of repressive histone modifications is well established . Here we have addressed the hypothesis that a barrier element in vertebrates should be capable of defending a gene from silencing by DNA methylation . Using an established stable reporter gene system , we find that HS4 acts specifically to protect a gene promoter from de novo DNA methylation . Notably , protection from methylation can occur in the absence of histone acetylation or transcription . There is a division of labor at HS4; the sequences that mediate protection from methylation are separable from those that mediate CTCF-dependent enhancer blocking and USF-dependent histone modification recruitment . The zinc finger protein VEZF1 was purified as the factor that specifically interacts with the methylation protection elements . VEZF1 is a candidate CpG island protection factor as the G-rich sequences bound by VEZF1 are frequently found at CpG island promoters . Indeed , we show that VEZF1 elements are sufficient to mediate demethylation and protection of the APRT CpG island promoter from DNA methylation . We propose that many barrier elements in vertebrates will prevent DNA methylation in addition to blocking the propagation of repressive histone modifications , as either process is sufficient to direct the establishment of an epigenetically stable silent chromatin state .
It has been proposed that genes and gene clusters are organized into chromatin domains that are maintained independent of their surroundings through the establishment of boundaries [1] , [2] . These boundaries may be variable in position , resulting from a balance between countervailing chromatin opening and condensing processes . Alternatively , chromatin boundaries of fixed position could be established by specific DNA sequence elements and their associated binding proteins . Such elements , collectively called insulators , possess a common ability to protect genes from inappropriate signals emanating from their surrounding environment [3]–[7] . The chicken β-globin genes are clustered within a thirty kilobase domain of nuclease accessible chromatin , the 5′ boundary of which is marked by a constitutive DNaseI hypersensitive site called HS4 ( Figure 1 ) . The HS4 element has two activities that functionally define insulators . First , it can block the action of an enhancer element on a linked promoter , but only when positioned between the two [8] . The protein CTCF mediates the enhancer blocking activity of the HS4 element [9] . Second , the HS4 insulator acts as a barrier to chromosomal position effect silencing [10] . The activities of HS4 have been mapped to a 275 bp “core” element that contains five protein binding sites revealed by DNase I footprinting [9]–[11] ( Figure S1 ) . The enhancer blocking and barrier activities of HS4 appear to have different underlying mechanisms as they are separable in assay systems . The CTCF binding site footprint II ( FII ) is necessary and sufficient for enhancer blocking , but can be deleted from HS4 without affecting barrier activity [9] , [12] , [13] . The four remaining protein binding sites are all essential for barrier activity ( FI , FIII , FIV and FV ) but dispensable for enhancer blocking activity [9] . We previously found that the binding of ubiquitous USF proteins to a single site in HS4 , footprint FIV , is a necessary component of its barrier activity [14] . USF serves to constitutively recruit several histone modifying enzymes , leading to the enrichment of a panel of histone modifications typically associated with transcriptionally active open chromatin , including H3ac , H4ac , H3K4me2 and H4R3me2as . Knock down of USF expression abolishes the recruitment of active histone modifications and leads to the encroachment of transcriptionally repressive chromatin marked by H3K9me2 and H3K27me3 into the β-globin locus [14] , [15] . While USF function is necessary , it is not sufficient for HS4's barrier activity . Deletion of any one of the three remaining HS4 binding sites FI , FIII or FV disrupts barrier activity without affecting USF-mediated recruitment of histone modifications to HS4 [12] , [14] . We hypothesized that the FI , FIII and FV sites may contribute to barrier activity by preventing a transcriptional silencing process other than that mediated by repressive histone modifications . We previously observed that transgenes lack promoter DNA methylation when shielded from chromosomal silencing by HS4 elements [16] . Similar results were seen with retroviral transgenes shielded by HS4 [17] . It was not clear from these studies whether the lack of DNA methylation was an indirect consequence of transcriptional activity of insulated transgenes , nor did these studies address whether particular DNA elements or proteins bound at HS4 are specifically responsible for this protection . We now use insulator mutations to demonstrate that HS4 does specifically protect a gene promoter from DNA methylation . We determine which HS4 sequence elements are responsible for protection from methylation and have purified the protein that recognizes these elements in vivo . We also demonstrate that these elements are able to mediate the demethylation of a CpG island promoter .
To investigate whether HS4 acts specifically to counter DNA methylation , we have studied transgenic cell lines that were previously established for the HS4 barrier assay . The assay construct consists of an IL-2R reporter gene driven by an erythroid enhancer and promoter randomly integrated into erythroid 6C2 cells . These transgenes are susceptible to chromosomal silencing over a period of 20–40 days in culture following the removal of selection , with transgenic promoters being subject to DNA methylation and subsequent recruitment of the Mi-2/NuRD co-repressor complex [12] , [16] . In contrast , transgenes flanked by wild-type HS4 insulators are protected from silencing and lack promoter DNA methylation [16] , [17] . It was unclear from these earlier results whether the lack of methylation was a consequence of transcriptional activity or whether particular HS4 activities mediate protection from methylation . We have performed clonal bisulfite sequencing on single/low-copy transgenes flanked by HS4 insulators that are either wild type or carry deletions in one of five protein binding site ‘footprints’ ( Figure 1 ) . We have studied the same stocks of the same cloned transgenic cells that have been characterized for long term expression and histone modification status [12] , [14] . The transgene promoter remains free of DNA methylation when insulated from chromosomal silencing by wild type HS4 elements , even following prolonged culture ( Figure 2B , WT ) . Strikingly , we find that transgenic promoters are subject to almost complete DNA methylation if any one of three footprinted sites , FI , FIII or FV , is deleted from the flanking HS4 insulators ( Figure 2 , Δ1 , Δ3 and Δ5 ) . These profiles of DNA hypermethylation are indistinguishable from those observed at non-insulated transgenes [16] . In contrast , deletion of the CTCF ( FII ) binding site from HS4 results in little de novo DNA methylation of the promoter ( Figure 2B , ΔII ) . The deletion of the CTCF site has no effect on barrier activity and the lack of methylation observed is consistent with the transcriptionally active state of this transgene ( Figure 2C , [12] ) . De novo DNA methylation of the transgene promoter has previously been observed to be a secondary consequence of chromosomal silencing in the absence of insulation [18] . This suggested that deletion of the USF binding site from HS4 , which abolishes HS4's barrier activity and results in the loss of active histone modifications [14] and transcriptional silencing [12] , should also result in promoter hypermethylation . To our surprise , deletion of the USF ( FIV ) binding site from HS4 results in little de novo DNA methylation of the promoter ( Figure 2B , ΔIV ) . These results show that the DNA methylation status of a promoter does not necessarily follow its histone modification and transcription status . They also strongly indicate that three protein binding sites at HS4 ( FI , FIII and FV ) have a specific role to mediate protection from silencing associated with de novo DNA methylation . We next sought to determine how HS4 footprint deletions affect the timing and level of methylation of the mutant insulators in comparison with their linked transgene promoters . The dogma established from previous studies posits that a barrier element like HS4 acts as a passive barrier to the propagation , or spreading , of chromosomal silencing . We therefore expect that when HS4 mutations compromise barrier activity , the mutant insualtors would become methylated either prior to , or coincident with , the transgene promoters they are shielding . We performed bisulfite sequencing of the HS4 elements that are located 5′ of the IL-2R transgenes ( Figure 3A ) . Analyses were made following 30 days of culture , typically the period at which epigenetic silencing of the transgene is being established in non-insulated lines , and after 90 days , at which point any silencing will be complete . We find that wild type HS4 elements remain unmethylated during long term culture , concordant with the lack of transgene methylation ( Figure 3C , WT ) . Deletion of the FI and FV sites results in partial methylation of HS4 ( Figure 3C , ΔI and ΔV ) . This is in line with the effects of these mutations on promoter methylation . The timing of methylation does not fit the spreading models however , as the transgene promoter becomes methylated prior to the flanking insulator ( compare ΔI at day 30 in Figure 2B with that in Figure 3C ) . Furthermore , deletion of the FIII site does not result in methylation of HS4 despite complete promoter methylation resulting from this mutation ( compare ΔIII at day 90 in Figure 3C with that in Figure 2B ) . We also found that deletion of either the CTCF or USF sites leads to partial methylation of HS4 , but with no methylation at the promoter ( compare ΔII and ΔIV at day 90 in Figure 3C with that in Figure 2B ) . We note that the patterns of partial methylation of mutant HS4 elements are heterogeneous , with none of the individually sequenced clones becoming densely methylated ( Table S2 ) . The partial methylation of mutant HS4 elements ( 20%–50% ) contrasts with the near total DNA methylation observed at the silenced promoters flanked by FI , FIII or FV site mutant insulators ( 90%–100% ) . These findings reveal a disconnect between the level and timing of de novo DNA methylation at a transgene and flanking insulators . The footprinted sequences FI , FIII and FV are specifically required for HS4's ability to protect against DNA-methylation-mediated silencing of a transgene . We wished to identify the factors that interact with each of the FI , FIII and FV sites to better understand this activity . We established gel mobility shift assays for insulator-binding activities using nuclear protein extracts of the chicken early erythroid cell line 6C2 ( the cell line in which the barrier assay is performed ) and adult chicken red blood cells ( an abundant source of nuclear protein for purification purposes ) . Complexes of similar mobility and intensity are observed between the two nuclear extracts and each of the FI , FIII and FV sites ( Figure 4 , 6C2 data not shown ) . Competition assays show that these complexes are all specific for homopolymeric dG-dC strings found in each site . Unlabelled wild type FI duplexes compete efficiently with the formation of the major complex with FI , whereas FI duplexes harboring mutations in the ( dG-dC ) 9 string are much less effective as competitors ( Figure 4A , arrow , compare lanes 2 , 4 and 5 ) . The major complex with FIII specifically interacts with the ( dG-dC ) 6 string at its center ( Figure 4B , compare lanes 1 , 5 , 6 and 7 ) and the major FV complex also specifically interacts with bases in both of its ( dG-dC ) strings ( Figure 4C , compare lanes 1 , 4 , 5 and 7 ) . The same proteins interact with the FI , FIII and FV sites . This is supported by the observation that the three sites can efficiently compete with each other . Unlabelled FIII duplexes compete for nuclear protein interactions with labeled FI ( Figure 4A , compare lanes 1 and 8 ) and FI duplexes efficiently compete for interaction with FIII ( Figure 4B , compare lanes 1 and 9 ) , for example . Mutational analysis shows that this cross-competition is dependent upon the dG-dC string bases within each footprint site ( data not shown ) . Competition assays also reveal that the relative affinity of nuclear proteins for the three sites differs somewhat . FI complexes form with approximately 2- and 5-fold greater affinity than FIII and FV complexes , respectively ( data not shown ) . Together , these observations of similar sequence specificity and comparable complex mobilities indicate that common nuclear proteins interact with all of these sites . We purified proteins that specifically interact with FI and FIII from chicken red blood cells by conventional chromatography . FI- and FIII-binding activities exactly co-fractionated following ion exchange chromatography with SP- and Heparin sepharose ( Figure 5 ) . The active elution peak from Heparin sepharose chromatography was split in two and fractionated in parallel by either FI or FIII DNA affinity chromatography . The resulting purified polypeptides were sequenced by tandem mass spectrometry ( Figure S2 ) . The proteins Hsp70 , VEZF1 , ZF5 and TEF1α were present in both FI and FIII DNA affinity eluates . The proteins SP1 and SP3 were additionally present in the FI DNA affinity eluate . We firstly cloned chicken VEZF1 ( Refseq NM_001037827 . 1 ) and determined whether it interacts with the G-rich footprinted sites of the HS4 insulator , as it was previously reported that human VEZF1 ( also known as DB1 ) interacts with similar G-rich sites [19] , [20] . We find that in vitro translation of chicken VEZF1 cDNA yields a 65 kDa protein that efficiently interacts with the FI , FIII and FV sites ( Figure 6A ) . The complexes formed with recombinant VEZF1 migrate slightly faster than those formed with nuclear extract . This may be a reflection of differing post translational modifications . Nonetheless , recombinant VEZF1 binds with an identical specificity to that observed for nuclear extract proteins . For example , competition of VEZF1 complexes with unlabelled FI and FIII duplexes is disrupted by mutations within their dG-dC strings ( Figure 6A , lanes 1–8 ) . The ( dG-dC ) strings present in the VEZF1 sites at HS4 are reminiscent of a site in the chicken βA-globin promoter that contains a ( dG-dC ) 16 string . A nuclear factor called Beta Globin Protein 1 ( BGP1 ) was previously characterized as interacting with this site [21] . This BGP1 binding site has no effect on transcription in transient assays or on chromatinized templates in vitro , but was considered to indirectly assist in activation by directing nucleosome placement [22]–[24] . BGP1 protein of 66 kDa can be purified using poly ( dG ) -poly ( dC ) affinity chromatography [25] . We have now sequenced a purified BGP1 sample ( a gift from J . Allan , University of Edinburgh ) and find it to be VEZF1 . We find that recombinant chicken VEZF1 interacts with the βA promoter site with an identical specificity to that of erythrocyte nuclear protein ( s ) ( Figure 6A , compare lanes 9–15 with 16–22 ) . At least seven contiguous homopolymeric dG-dC base pairs are required for the efficient formation of complexes between recombinant VEZF1 or nuclear proteins and the βA promoter site [25] . Consistent with this , interaction between VEZF1 and the βA site is competed by unlabelled FI which contains a ( dG-dC ) 9 string ( Figure 6A , compare lanes 16 and 20 ) . This competition is disrupted by mutation at the centre of the FI dG-dC string ( Figure 6A , compare lanes 20 and 21 ) . However , VEZF1 interaction with the βA site is also competed by FIII which contains only a ( dG-dC ) 6 string ( Figure 6A , compare lanes 16 and 22 ) . FIII contains a second short ( dG-dC ) 4 string , as does FV ( see figure 4D ) , which may compensate to form a bipartite recognition motif . Recombinant VEZF1 interacts with the contiguous dG-dC strings of the FI and βA sites with approximately 2- and 5-fold greater affinities than the bipartite dG-dC strings of the FIII and FV sites , respectively ( data not shown ) . Polyclonal antibodies were raised against a conserved C-terminal fragment of VEZF1 , which specifically recognize the 65 kDa VEZF1 polypeptide from chicken nuclear extracts ( Figure S7B ) . VEZF1 antibodies readily supershift/abrogate complexes between recombinant VEZF1 and the FI , FIII , FV and βA sites ( Figure 6B ) . Supershift analysis also reveals that VEZF1 is present in complexes between nuclear extracts and the FI , FIII , FV and βA sites ( Figure 6B ) . VEZF1 appears to be the only factor that interacts with the FIII and FV sites , whereas other factors also appear to bind to the FI and βA sites in vitro . Chromatin immunoprecipitation ( ChIP ) analyses were performed to analyze the binding of VEZF1 at the chicken β-globin locus in vivo . Chromatin was prepared from the early erythroid line 6C2 , which does not express β-globin and nucleated erythrocytes isolated from 10 day chicken embryos , a stage at which approximately 80% of erythrocytes are definitive and express the βA-globin gene . VEZF1 was found to strongly interact with HS4 in both 6C2 cells and erythrocytes , consistent with our in vitro analyses ( Figure 7A and 7B ) . VEZF1 binding to HS4 therefore does not coincide with transcription of the β-globin genes . VEZF1 does not interact with the 3′HS enhancer blocking element , which does not contain any dG-dC string like motifs and lacks barrier activity [12] . This is in contrast to CTCF , which interacts strongly with both the HS4 and 3′HS insulators in 6C2 cells and erythrocytes ( Figure 7A and 7B ) . We also find that VEZF1 strongly interacts with the βA promoter , consistent with gel mobility shift assays . In contrast to HS4 , VEZF1 binding to the βA promoter appears to be restricted to erythrocytes in which the βA gene is expressed ( Figure 7A and 7B ) . None of the other candidate HS4-binding proteins isolated by DNA affinity purification were found to bind in vivo ( described in Text S1 ) ( see also Figures S3 , S4 , S5 ) . We tested whether VEZF1 requires all three of its sites for binding to HS4 in vivo , as all three VEZF1 binding sites are required for protection from DNA methylation . We found that this was not the case , as VEZF1 remains tightly bound at HS4 when any one of its binding sites is deleted ( Figure S6 ) . VEZF1 also remains bound to mutant HS4 elements that have become partially methylated . Consistent with this , we found that VEZF1 binding to its three sites at HS4 is not affected by CpG methylation in vitro ( data not shown ) . We have attempted to disrupt VEZF1 function at HS4 following knockdown by RNAi . We strived to achieve substantial knockdown of VEZF1 to disrupt its binding to the high affinity sites at HS4 . Prolonged knockdown was also required as we have previously found that the de novo DNA methylation of these transgenes is a gradual process that takes many days to establish [18] . We were able to knockdown VEZF1 protein to 3% of wild type levels following 2 weeks of stable miRNA expression . However , ChIP analysis revealed that VEZF1 binding to HS4 was not significantly affected following this prolonged and substantial knockdown ( Figure S7 ) . Consequently , we observed no de novo DNA methylation of the HS4 element and no change in HS4's ability to protect a transgene from silencing during this period ( data not shown ) . The inadequacy of RNAi to strip constitutive transcription factor binding from high affinity sites has also been observed for CTCF [26] . Unfortunately , we are unable to study the role of murine Vezf1's role in protection from DNA methylation in Vezf1 null ES cells , as we recently found that they are defective for de novo DNA methylation due to the requirement of Vezf1 for full transcriptional activity of the Dnmt3b gene in these cells [27] . To address whether VEZF1 elements also protect CpG island ( CGI ) promoters from DNA methylation , we investigated VEZF1 binding to the APRT gene promoter and its effects on methylation . SP1-like binding elements have been shown to be required to prevent methylation of the mouse and hamster APRT CpG island elements: two earlier papers have shown that deletion of the SP1-like elements is sufficient to induce methylation in these islands [28] , [29] . Furthermore , pre-methylated fragments of the hamster APRT CGI that contain three SP1-like elements are subject to demethylation upon integration into mouse ES cells [28] . The SP1 transcription factor itself is not required for the unmethylated state of CGIs however , with the APRT gene remaining unmethylated and expressed normally in Sp1 null ES cells and embryos [30] . Given that VEZF1 recognizes G-rich sequences that are similar to SP1 motifs , we hypothesized that VEZF1 may interact with the APRT CGI elements ( Figure 8A ) . We performed ChIP analyses for the binding of SP1 and VEZF1 to the 720 bp hamster APRT CGI stably integrated into mouse ES cells ( Figure 8B ) . We found SP1 binding at both sites 1/2 and site 3 , but site 3 was also occupied by VEZF1 . Supershift analysis also shows VEZF1 interaction with site 3 in vitro ( Figure S8 ) . Site 3 contains a motif ( CCCCCCTTTCCCC ) that is reminiscent of the VEZF1-specific bipartite footprint III site found at the HS4 insulator element ( CCCCCCGCATCCCC ) . To address whether VEZF1 elements could protect the APRT CGI from methylation , we replaced each of the three SP1-like elements with the VEZF1-specific FIII element from the HS4 insulator ( Figure 8A ) . ChIP analysis shows that VEZF1 binding replaces that of SP1 at the mutant APRT CGI integrated into ES cells ( Figure 8B ) . Supershift analysis also shows that VEZF1 interacts with the mutant sites 1&2 and site 3 , while SP1/SP3 binding is lost ( Figure S8 ) . We then tested the ability of wild type and mutant APRT CGIs to resist DNA methylation . Firstly , we confirmed that the de novo DNA methylation machinery functioned normally in the ES cells as non-island sequences from the APRT gene body succumb to de novo methylation ( Figure 8C ) . Consistent with previous results [28] , [29] , we find that the wild type APRT CGI is protected from DNA methylation when stably integrated into ES cells ( Figure 8D ) . Furthermore , a pre-methylated wild type APRT CGI is demethylated upon integration ( Figure 8E ) . It has previously been shown that mutation of the SP1-like elements results in the de novo methylation of the APRT CGI [28] . Our results show that substitution of the SP1-like elements with VEZF1-specific FIII elements from HS4 restores the ability of the mutant APRT CGI to be both protected from de novo methylation and to remove pre-methylation ( Figure 8E and 8F ) . VEZF1 elements from HS4 are therefore sufficient to mediate the demethylation and protection of a CGI from DNA methylation .
We have previously demonstrated that the HS4 insulator acts as a barrier to the spread of histone methylation marks associated with repressive chromatin [14] , [15] . While we found that active histone modifications recruited by USF proteins are an essential component of HS4's barrier activity , they are not sufficient [14] . Three addition protein binding sites are essential for barrier activity but are not required for the recruitment of active histone modifications [12] , [14] . These findings , summarized in Figure 9 , indicated that there was an additional and separable component to HS4's barrier activity . Here , we show that all three sites are bound by VEZF1 and are required for HS4's ability to protect a linked promoter from de novo DNA methylation . It was previously shown that the transgenes used in this study become marked by dense promoter DNA methylation upon chromosomal position effect silencing [16] . Promoter DNA methylation occurred subsequent to histone deacetylation and transcriptional inactivation of the promoter [18] . While flanking HS4 elements perfectly shield the transgene from silencing and DNA methylation , it was unclear from these experiments whether the lack of promoter methylation was simply a readout of the promoter's transcription status . We show that VEZF1-mediated protection from DNA methylation of a transgene promoter is retained even when USF site mutations at HS4 lead to histone deacetylation and transcriptional silencing ( Figure 9 , ΔUSF ) . The separation of DNA methylation protection from a promoter's histone modification and transcriptional status is a strong indication that the VEZF1 sites at HS4 possess a bona fide activity that is protective against DNA methylation . Determining the source of de novo DNA methylation is key to our understanding of how VEZF1 binding at HS4 could protect a promoter from epigenetic silencing . Previous studies using the same transgene system studied here found that non-insulated transgenes , regardless of integration site , are consistently subject to promoter methylation upon chromosomal silencing , and that flanking with HS4 elements can shield transgenes from this methylation [16] , [18] . The simplest explanation of these results is that HS4 is acting as a barrier to the encroachment , or spreading , of a silencing mechanisms that results in DNA methylation . The ability of repressive histone modifications and associated chromatin factors to mediate the spreading of gene silencing is well documented for many systems [31] . In the case of the chicken β-globin locus , the spreading of repressive histone modifications is observed upon perturbation of active histone modification recruitment at the HS4 barrier [14] , [15] . Analyses of progressive CpG island methylation during tumor progression are consistent with models that describe the spreading of DNA methylation [32] , [33] . Should de novo DNA methylation arise via spreading from the chromosomal integration site in our transgene system , we would expect to see high levels of methylation at compromised mutant insulators either coincident with , or prior to promoter methylation . However , we observe that promoters become methylated prior to the insulators , which remain unmethylated or become partially methylated . The observed independence of methylation states between insulator and promoter argue against spreading and clearly show that there can be no single mechanism that controls the methylation state of both the insulator and promoter . It remains possible that VEZF1 elements at HS4 are acting as a barrier to the spreading of a DNA methylation mechanism , but that additional processes prevent the accumulation of methylation at HS4 itself . An alternative possibility is that DNA methylation does not result from spreading , and that the insulator directly interacts with the promoter to deliver VEZF1 co-factors that prevent promoter methylation . In this model , the promoter itself would have its own program to recruit de novo DNA methylation , and VEZF1 would act as a factor that mediates inhibition of methylation . This would distinguish the activity of VEZF1 from those of USF1/USF2 , which bind elsewhere in the insulator element and recruit a number of enzymes that deliver active histone modifications to the reporter gene [14] , [15] , [34] . It will be of interest in future to determine whether VEZF1 elements potentiate the expression of nearby genes through the control of DNA methylation . The 275 bp “core” HS4 element comprises a CpG island ( CGI ) that is free of DNA methylation regardless of neighboring gene expression [11] , [35] , [36] , as well as when it is inserted into the mouse Igf2/H19 domain [37] . Consistent with this , we found that wild type transgenic HS4 elements remain unmethylated during long term culture . The processes that maintain the unmethylated state of the insulator appear to be complex , as we find that the mutation of all insulator protein binding sites results in some degree of de novo DNA methylation of HS4 . It has previously been shown that DNA binding proteins can prevent the methylation of their binding sites simply by steric hindrance of de novo DNA methyltransferases ( DNMTs ) [38] . It is possible that the HS4 deletions studied here disrupt cooperative interactions between insulator proteins , thus permitting DNMT access . We performed ChIP experiments on HS4 mutants and found that deletion of any one insulator binding site does not lead to the loss of binding of another ( Figure S6 ) . This is in agreement with functional assays which also found that deletion of any one insulator protein binding site does not lead to the loss of function associated with another site [9] , [12] , [14] . These findings argue against a simple steric protection of HS4 DNA from DNMTs by transcription factor binding . The degree of methylation at mutant HS4 elements was typically moderate ( 20–50% ) and did not increase to the near total methylation seen at the transgene promoter ( 90–100% ) following long term culture . These observations are consistent with a balance between activities that add and remove DNA methylation at HS4 . All constitutively expressed genes and ∼40% of genes with tissue-restricted expression have CGI promoters [39] . CGIs are typically unmethylated , especially in the germ line , which ensures that these CpGs are not subject to mutation by spontaneous deamination . It remains to be determined how CGIs resist global de novo methylation during early development , and how they remain hypomethylated irrespective of transcriptional status . Recent epigenomic profiling studies have begun to reveal a significant portion of CGIs that are subject to varying degrees of tissue-specific methylation in human somatic tissues [40]–[42] . These findings point to the existence of processes that protect CGIs from de novo methylation , which can be selectively inactivated during development and may become defective during cancer progression [43] . Definition of the cis-regulatory elements and trans-acting factors that control CGI methylation status is key to unraveling these processes . We have revisited the well established example of the APRT gene CGI promoter . It was previously shown that SP1-like binding elements are required to prevent CGI methylation [28] , [29] , although surprisingly the SP1 transcription factor itself is not required [30] . These findings suggested that other factors function at the G-rich SP1-like motifs , which are commonly found at CpG islands [44] . We show that VEZF1 interacts with site 3 of the hamster APRT CGI . A promoter-less APRT CGI fragment containing only site 3 remains protected from DNA methylation [28] . We were able to abrogate SP factor binding while retaining VEZF1 binding by introducing VEZF1-specific elements . The VEZF1-specific mutant retained its ability to mediate demethylation and protection from de novo DNA methylation . Thus , VEZF1 binding elements can protect a CGI from DNA methylation . We attempted to definitively address the requirement for VEZF1 , but discovered that global de novo DNA methylation mechanisms are defective in Vezf1 null ES cells [27] . We also show that VEZF1 interacts with the CGI promoter of the DHFR gene ( Figure S5 ) . Furthermore , ChIP-array analysis in somatic human cells reveals that VEZF1 predominantly interacts with CGI promoters and regulates genes with diverse functions ( R . S . and A . W . , unpublished observations ) . It remains to be determined whether VEZF1 plays a widespread role in the control of DNA methylation and what contribution this epigenetic control makes to developmental gene regulation and cancer progression . Experimental evidence has demonstrated that chromatin barrier elements can employ a number of different mechanisms to limit the spread of transcriptionally repressive chromatin; including tethering , nucleosome gaps/masking and histone code manipulation [6] . The constitutive recruitment of histone modifications such as acetylation is considered to be sufficient to establish barrier activity in eukaryotes that do not methylate their genomes , as demonstrated at synthetic barriers in yeast [45] . Our finding that HS4 also mediates protection from de novo DNA methylation adds another tier to understanding the mechanism of barrier elements in vertebrates . It is well established that densely methylated DNA abrogates transcription factor binding and is sufficient to establish all the features of repressive chromatin , including repressive histone modifications [46] , [47] . We propose that a barrier element in higher eukaryotes must be capable of preventing de novo DNA methylation in addition to blocking the propagation of silencing histone modifications , as either event , if not inhibited , is sufficient to direct the establishment of an epigenetically stable silent chromatin state . We have shown here that a fully effective vertebrate barrier combines both of these properties in a single multi-component element .
Chicken 6C2 erythroleukemia cells carrying IL-2R reporter transgenes ( 8103 , wild type HS4; 10401 , ΔFI; 10506 , ΔFII; 10615 , ΔFIII; 10901 , ΔFIV; 8d5 , ΔFV ) were cultured and assayed for IL-2R expression by FACS as described previously [12] , [14] . Genomic DNA was extracted from cell lines after 30 and 90 days of culture and bisulfite modified ( EZ Methylation , Zymo Research , CA ) . The upstream double-core HS4 elements and the IL-2R promoter were PCR amplified from each line . We were unable to amplify bisulfite modified double-core HS4 elements from the 10506 , ΔFII line to sufficient levels to provide representative sequence data . We therefore opted to amplify the outermost HS4 copy only . The 8d5 , ΔFV line also only contains one copy of HS4 in the upstream location [12] . All PCR products were gel purified and cloned , followed by sequencing ( GATC Biotech , Konstanz , Germany ) of 10 clones for each region of interest . Gel mobility shift assays were performed as described previously [14] . Recombinant VEZF1 , SP1 , SP3 and ZF5 were produced by in vitro translation using rabbit reticulocyte lysate ( Promega ) . FI- and FIII-binding proteins were purified from adult chicken red blood nuclear protein extracts by ion exchange chromatography . Throughout the purification , eluate fractions were analyzed for FI- and FIII-binding activity with gel mobility shift assays . The binding specificity of partially purified proteins was checked by competition analysis after each purification step . FI- and FIII-binding activities co-fractionated following ion exchange chromatography with SP XL and Heparin sepharose ( GE Healthcare ) . Phosphocellulose , Q and Sephacryl S300 columns were used in early purification attempts to resolve FI- and FIII-binding activities but they co-fractionated in each case ( data not shown ) . FI- and FIII-binding activities both eluted in two distinct fractions of approximately 200 and 400 kDa following gel filtration ( data not shown ) . The active fractions eluted from heparin sepharose were pooled then split into two and fractionated in parallel by FI or FIII DNA affinity as described previously [14] . Polypeptides electrophoresed on 7% Tris-acetate gels ( Invitrogen ) were excised , digested with trypsin and sequenced at the Harvard Microchemistry Facility by microcapillary reverse-phase HPLC nano-electrospray tandem mass spectrometry ( μLC/MS/MS ) on a Finnigan LCQ DECA quadrupole ion trap mass spectrometer . Chicken VEZF1/BGP1 cDNA was cloned following RT-PCR from 6C2 cell total RNA based on an assumption of conservation of 5′ cDNA sequence with human VEZF1/DB1 . The oligonucleotide Adaptor-A 5′CATGCCGCTCGAGCGGTTTTTTTTTTTTTTTTT was used in first strand cDNA synthesis with Superscript II reverse transcriptase ( Invitrogen ) . The primers VEZF1_5′ , 5′CCATGACCCATGGGCAGAGCCAAAGT and Adaptor 5′CATGCCGCTCGAGCGG were used to amplify a full length chicken VEZF1 cDNA by PCR which was TA cloned into pCRII ( Invitrogen ) . VEZF1 cDNA was sub-cloned into pCITE4b ( Novagen ) to generate p4bVEZFfull for the purpose of in vitro transcription . cDNA encoding chicken ZF5 was isolated by RT-PCR from 6C2 cell total RNA using primers designed from the published sequence ( U51640 ) . We found that the bases CpG 1306-7 in the published sequence were GpC in our clone , causing codon 436 to translate as alanine instead of arginine . We obtained the vectors pCDNA3-ZF5 and pEVRFO-ZF5 ( a kind gift of W . Stumph , San Diego State University ) and we also found the CpG to GpC conflict with the published sequence . Full length chicken SP1 and SP3 cDNAs cloned in the pBluescript-based vectors pH-SP1 and pH-SP3-3 were a kind gift from Marc Castellazzi ( INSERM , Lyon ) . Polyclonal antibodies were raised ( Rockland Immunochemicals ) against chicken VEZF1 ( Ser376-Ala547 ) and chicken ZF5 peptides ( Ser131-Lys248 ) produced in E . coli ( QIAexpress , Qiagen ) . Peptides were produced in M15 [pREP4] E . coli followed by rapid lysis with B-PER reagent ( Pierce ) . ZF5 peptide was soluble and purified on Ni-NTA agarose ( Qiagen ) . VEZF1 peptide was insoluble and resulting inclusion bodies were prepared using B-PER reagent ( Pierce ) , solubilized with 6M guanidium hydrochloride and immobilized on TALON Sepharose resin ( Clontech ) at pH 7 . VEZF1 peptide was renatured in a stepwise manner with 6 , 4 , 3 , 2 , 1 and 0 . 5 M guanidium hydrochloride prior to elution . VEZF1 and ZF5 polyclonal IgG antibodies ( Rockland Immunochemicals ) were purified from rabbit serum using PROSEP-A media ( Montage , Millipore ) . Anti-full length chicken VEZF1 antibodies were raised previously [27] . Antibodies raised against CTCF ( 06-917 ) , SP1 ( PEP2X , H-225X ) , SP3 ( D20X ) and USF1 ( B01 ) were obtained from Millipore , Santa Cruz Biotechnology and Abnova , respectively . ChIP analysis of transcription factor binding in chicken cells was performed using formaldehyde crosslinked chromatin prepared from chicken 10 day embryonic erythrocytes isolated from fertilized White Leghorn eggs ( CBT Farms , Chestertown , MD ) or cultured 6C2 erythroleukemia cells . 10 day erythrocytes were washed and resuspended in 25 mls of PBS ( 2×107 cells/ml ) and fixed with a final concentration of 0 . 25% formaldehyde at room temperature for 30 seconds . 6C2 cells were harvested in mid-exponential growth phase , divided into 30 ml aliquots containing 1×108 cells in fresh media and fixed with a final concentration of 0 . 8% formaldehyde at room temperature for 5 minutes . Reactions were stopped by adding glycine to a final concentration of 0 . 125 M . The cells were washed in PBS and resuspended in cell lysis buffer ( 0 . 25% Triton X-100 , 10 mM EDTA , 0 . 5 mM EGTA , 10 mM Tris pH 8 . 0 ) to isolate nuclei . Chromatin was prepared following washing ( 0 . 2 M NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 10 mM Tris pH 8 . 0 ) and lysis ( NLB: 50 mM Tris-HCl pH 8 . 0 , 10 mM EDTA , 0 . 5% SDS ) of nuclei . Crosslinked chromatin was fragmented by sonicaton ( Misonix ) for a total time of 10 minutes in regular 10 second pulses at 4°C . Debris was removed by centrifugation at 15000 g for 10 minutes and chromatin was diluted in 10 volumes of X-ChIP buffer ( 1 . 1% TX-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris pH 8 . 1 , 167 mM NaCl ) . Agarose gel electrophoresis was used to confirm that chromatin fragments were ∼500 bp in length on average . Chromatin was pre-cleared with 100 µl of protein A agarose ( 50% slurry in X-ChIP buffer , Millipore ) and 50 µg of normal rabbit IgG ( Santa Cruz ) for 3 hours at 4°C on a rotating wheel . Aliquots of chromatin were taken to generate input DNA and protein for western analysis . Individual ChIPs were performed using chromatin from 1×107 cells in a total volume of 1 ml by diluting the pre-cleared chromatin with modified X-ChIP buffer ( 1 part NLB to 9 parts X-ChIP ) . Incubation with antibodies was performed overnight at 4°C on a rotating wheel . Between 10 and 30 µg of specific antibodies or 10 µg of normal rabbit IgG ( Santa Cruz ) were used per ChIP . Chromatin was precipitated with protein A agarose ( 50 µl slurry in X-ChIP , Millipore ) for 4 hours at 4°C with rotation . Immunoprecipitated chromatin was collected , washed , eluted and crosslinks reversed . DNA was then extracted by phenol-chloroform and ethanol precipitated in the presence of 10 µg glycogen . Relative DNA enrichments were quantified by TaqMan real-time qPCR using the comparative Ct method relative to input DNA and normalized to the primer set 10 . 35 within the 16 kb condensed chromatin region upstream of the chicken β-globin locus as described previously [14] . The TaqMan primer sets 10 . 35 ( “16 kb” ) , 21 . 54 ( HS4 core , “end HS4” ) , 39 . 806 ( beta-adult promoter ) , 50 . 861 ( 3′HS ) and PGI5′ ( 5′ HS4 elements on IL-2R transgene , “trans HS4” ) were used in this study . 10 . 35_For: GGAACAAGTTGGCAAGGTCCTAT 10 . 35_Rev: TCTTCTGCCCTGCCCGTAT 10 . 35_TM: FAM-TGCAGTTCCCTGTTCATGTGCTTTTCG-TAMRA 21 . 54_For: TCCTGGAAGGTCCTGGAAG 21 . 54_Rev: CGGGGGAGGGACGTAAT 21 . 54_TM: 6FAM-CCCAAAGCCCCCAGGGATGT-TAMRA 39 . 8_For: CTGTGGTCTCCTGCCTCACA 39 . 8_Rev: AGGCTGGGTGCCCCTC 39 . 8_TM: FAM-CAATGCAGAGTGCTGTGGTTTGGAACTG-TAMRA PGI_5′_For CACAGGAAACAGCTATGACATGATT PGI_5′_Rev TCTGCCTTCTCCCTGATAACG PGI_5′_TM 6FAM-AATTCCTGCCCACACCCTCCTGC-TAMRA ChIP analysis of transcription factor binding in murine E14Tg2A . 4 ES cell lines was performed using formaldehyde crosslinked chromatin prepared from 1×109 cells treated with 1% formaldehyde for 5 minutes . Chromatin was prepared as described above , where fragments sizes ranged from 500–700 bp . Semi-quantitative PCR was performed using the following primers APRT1/2_For: AAAGGCGTGCGGGAGCCAGAAAT APRT1/2_Rev: CCTTGGTAGGTGGGG APRT3_For: CCCTGTTCCTGGGCTCC APRT3_Rev: TGACTGGCCAGGAGG ChIP analysis from human embryonic kidney 293-T cell line SD5 that contains a stably integrated copy of the 275 bp HS4 core chicken insulator was performed as described for cultured chicken cells above . SD5 cells were crosslinked with 1 . 6% formaldehyde for 5 minutes . The following primers were used in SYBR quantitative PCR analysis: HS4_21 . 726_F: CGGGATCGCTTTCCTCTGA HS4_21 . 726_R: CCGTATCCCCCAGGTGTCT P_DHFR_F: TCGCCTGCACAAATAGGGAC P_DHFR_R: AGAACGCGCGGTCAAGTTT Control VEZF1_CDS_F: GACAGCAGCCGAACTTCGTT VEZF1_CDS_R: TGGTGCCCGAGGAAGATG APRT elements . The following elements were amplified from Hamster liver genomic DNA: ∼720 bp wild type and mutant APRT CpG islands and an 870 bp non-island region of the APRT gene were cloned into pUC19 . Each element was PCR amplified , half of which was subject to in vitro CpG methylation by M . SssI methyltransferase ( New England Biolabs ) . In vitro methylation was validated by HpaII and MspI digestion . 1 µg of each APRT element was transfected into murine E14Tg2A . 4 ES cells ( BayGenomics ) with 1 µg of the XbaI fragment of pREP7 ( Invitrogen ) . Hygromycin resistant clones were grown in LIF without feeder co-culture . Genomic DNA was extracted after two weeks of culture . Probes for Southern blotting were generated using Ready-to-go dCTP beads ( GE Healthcare ) . Oligonucleotides were generated on an ABI 394 DNA synthesizer . The top strand sequences for each of the duplexes used for gel mobility shift analyses were: FI wt 5′ GGAGCTCACGGGGACAGCCCCCCCCCAAAGCCCCCAGGGA , FIII wt 5′ aggcgcgccCCGGTCCGGCGCTCCCCCCGCATCCCCGAGCCGGggcgcgcct , FV wt 5′ CCTGCAGACACCTGGGGGGATACGGGGAAAAAGCTTTAGG , Sp1 5′ ATTCGATCGGGGCGGGGCGAGC , glo wt 5′ AATTGCAGAGCTGGGAATCGGGGGGGGGGGGGGGGCGGGTGGTGGTGTGG , glo 7G 5′ AATTGCAGAGCTGGGAATCGGGGGGGCGGGTGGTGGTGTGG , glo 6G 5′ AATTGCAGAGCTGGGAATCGGGGGGCGGGTGGTGGTGTGG . Asc I restriction sites used for cloning FIII sites in an earlier study are shown as lower case . All FI and FIII oligonucleotides were identical to the wt sequences above except for mutations indicated in Figure 1E . DNA affinity columns were prepared using the following oligonucleotides FI-DA TOP 5′ gatcTCACGGGGACAGCCCCCCCCCAAAGCCCCCA FI-DA BOTTOM 5′ gatcTGGGGGCTTTGGGGGGGGGCTGTCCCCGTGA FIII-DA TOP 5′ gatcGGTCCGGCGCTCCCCCCGCATCCCCGAGCCGGCA FIII-DA BOTTOM 5′ gatcTGCCGGCTCGGGGATGCGGGGGGAGCGCCGGACC PCR primers used in bisulfite sequencing were HS4 5′ double forward 5′ GGTATTAGAGTAGATTGTATTGAGAGTGTA HS4 5′ double reverse 5′ CATAACTATTTCCTATATAAATCCCC HS4 5′ single forward 5′ AGAGTAGATTGTATTGAGAGTGTATTATA HS4 5′ single reverse 5′ ACATCCCTAAAAACTTTAAAAAAAA IL-2R forward 5′ GTTAAGGTTGGGGGTTTTTT IL-2R reverse 5′ AAAACTCTACCTAACAACCAAACAC PCR primers used in RT-PCR gene expression analysis were GgVEZF805anti 5′CAGTGCACGTTTGGCATTTGAAG GgVEZF716sense 5′GAAAAGGCTTCTCGAGGCCTGATC GgGAPDH-247T 5′-6FAM-TCCAGGAGCGTGACCCCAGCA-TAMRA GgGAPDH-226F 5′ ATGGGCACGCCATCACTATC GgGAPDH-302R 5′ AACATACTCAGCACCTGCATCTG GgB-ACTIN_F 5′ TGCTGCGCTCGTTGTTGA GgB-ACTIN_R 5′ CATCGTCCCCGGCGA GgB-ACTIN_T 5′-6FAM-TGGCTCCGGTATGTGCAAGGCC-TAMRA The primers used for methylation specific PCR analysis of the APRT non-island element were: APRTNI_5′ TCTAGATTGCTAGGAGTAGC APRTNI_3′ TCTAGAACCACCCCTAGC | DNA sequences known as chromatin insulator or barrier elements are considered key components of genome organization as they can establish boundaries between transcriptionally permissive and repressive chromatin domains . Here we address the hypothesis that barrier elements in vertebrates can protect genes from transcriptional silencing that is marked by DNA methylation . We have found that the HS4 insulator element from the β-globin gene locus can protect a gene promoter from DNA methylation . Protection from DNA methylation is separable from other insulator activities and is mapped to three transcription factor binding sites occupied by the zinc finger protein VEZF1 , a novel chromatin barrier protein . VEZF1 is a candidate factor for the protection of promoters from DNA methylation . We found that VEZF1-specific binding sites are sufficient to mediate demethylation and protection of the APRT gene promoter from DNA methylation . We propose that barrier elements in vertebrates must be capable of preventing DNA methylation in addition to blocking the propagation of silencing histone modifications , as either process is sufficient to direct the establishment of an inactive chromatin state . | [
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] | 2010 | VEZF1 Elements Mediate Protection from DNA Methylation |
Zika virus ( ZIKV ) , first isolated in Uganda in 1947 , is currently spreading rapidly through South America and the Caribbean . In Brazil , infection has been linked with microcephaly and other serious complications , leading to declaration of a public health emergency of international concern; however , there currently are only limited data on the virus ( and its possible sources and manifestations ) in the Caribbean . From May , 2014-February , 2015 , in conjunction with studies of chikungunya ( CHIKV ) and dengue ( DENV ) virus infections , blood samples were collected from children in the Gressier/Leogane region of Haiti who presented to a school clinic with undifferentiated febrile illness . Samples were initially screened by RT-PCR for CHIKV and DENV , with samples negative in these assays further screened by viral culture . Of 177 samples screened , three were positive for ZIKV , confirmed by viral sequencing; DENV-1 was also identified in culture from one of the three positive case patients . Patients were from two different schools and 3 different towns , with all three cases occurring within a single week , consistent with the occurrence of an outbreak in the region . Phylogenetic analysis of known full genome viral sequences demonstrated a close relationship with ZIKV from Brazil; additional analysis of the NS5 gene , for which more sequences are currently available , showed the Haitian strains clustering within a monophyletic clade distinct from Brazilian , Puerto Rican and Guatemalan sequences , with all part of a larger clade including isolates from Easter Island . Phylogeography also clarified that at least three major African sub-lineages exist , and confirmed that the South American epidemic is most likely to have originated from an initial ZIKV introduction from French Polynesia into Easter Island , and then to the remainder of the Americas . ZIKV epidemics in South America , as well as in Africa , show complex dissemination patterns . The virus appears to have been circulating in Haiti prior to the first reported cases in Brazil . Factors contributing to transmission and the possible linkage of this early Haitian outbreak with microcephaly remain to be determined .
Zika is a mosquito-borne flavivirus initially isolated in the Zika forest of Uganda in 1947 [1] . There were periodic human cases reported from Africa and Asia in the intervening decades , but it was not until 2007 that a major epidemic was reported , on Yap Island , Federated States of Micronesia [2] . Zika infections were subsequently identified in other parts of Asia , with a shift toward the Americas presaged by an outbreak on Easter Island in May , 2014 [3] . In March , 2015 , cases were identified in Bahia , Brazil [4] , with subsequent rapid spread through multiple Brazilian states [1 , 5] , and other countries in South America and the Caribbean [1 , 5]: as of January , 2016 , locally-transmitted cases had been reported by the Pan American Health Organization in Puerto Rico and 19 countries/territories in the Americas . Infection with Zika virus ( ZIKV ) has traditionally been associated with asymptomatic or mild illness . Clinical manifestations , when they occur , include acute onset of fever , headache , maculopapular rash , arthralgias , myalgias , and/or non-purulent conjunctivitis [1 , 2] . In an outbreak in French Polynesia in 2013–14 , there were , for the first time , reports of neurological and auto-immune complications , such as Guillain-Barré syndrome , in the setting of co-circulating dengue ( DENV ) and chikungunya ( CHIKV ) viruses [6 , 7] . With the progression of the Brazilian outbreak in 2015 , the Brazilian Ministry of Health noted a striking concurrent increase in the number of infants born with microcephaly in areas with ZIKV transmission . Multiple subsequent studies have provided further documentation of the link between ZIKV and microcephaly and other birth defects , as well as with Guillain-Barré syndrome [8–14] . Based on the “strongly suspected” causal link between Zika virus and the observed fetal brain abnormalities , WHO has declared the current Zika epidemic a “public health emergency of international concern” [15] . As a step in monitoring and understanding spread of the epidemic , we report here the isolation of ZIKV from three children in Haiti in December , 2014 , before the first reported Brazilian cases .
As virus-specific CPE were observed in LLC-MK2 and Vero E6 cells inoculated with plasma , but the identity of the agent unknown , spent cell growth media was treated with cyanase nuclease to degrade nucleic acids external to that packaged ( and thus protected ) in virions using a Nucleic Acid Removal Kit ( RiboSolutions , Inc . , Cedar Creek , Texas ) , and vRNA once again extracted from the treated material using a QIAamp Viral RNA Mini Kit . A panel of PCR and RT-PCR tests were performed; for RT-PCR , first-strand synthesis was performed using random 9-mers and Accuscript High Fidelity 1st strand cDNA kit ( Agilent Technologies , Santa Clara , CA ) . The presence of flavivirus RNA was detected using the Flav100F-200R , and Zika virus RNA effectively detected [20] using RT-PCR systems ZIKVF9027-ZIKVR9197c [21] , 9271–9373 [22] , and 835 – 911c [17] . For confirmation , PCR amplicons were purified and sequenced . Sequencing of the complete Zika virus genome of one isolate ( from the first patient ) , designated Haiti/1225/2014 , was accomplished using a genome walking strategy with the PCR primers described in S1 Table . Briefly , targeted overlapping sequences ( approximately 800 bp amplicons ) were amplified using Accuscript High Fidelity reverse transcriptase in the presence of SUPERase-In RNase inhibitor ( Ambion , Austin , TX ) , followed by PCR with Phusion Polymerase ( New England Biolabs ) with denaturation steps performed at 98°C . To obtain the 5′ and 3′ ends of the viral genome , a 5′ and 3′ system for the Rapid Amplification of cDNA Ends ( RACE ) was used per the manufacturer's protocols ( Life Technologies , Carlsbad , CA , USA ) . PCR amplicons were purified , sequenced bidirectionally using Sanger Sequencing , and the sequences assembled with the aid of Sequencher DNA sequence analysis software v2 . 1 ( Gene Codes , Ann Arbor , MI , USA ) . The GenBank accession number is KU509998 . All available ZIKV nucleotide sequences were downloaded from NCBI ( http://www . ncbi . nlm . nih . gov/ ) and four data sets were assembled ( S1 Table ) using the following inclusion criteria: ( 1 ) sequences were published in peer-review journals; ( 2 ) known sampling time; ( 3 ) city/state was known and clearly established in the original publication . The first data set included all ZIKV complete genome sequences available in NCBI ( 23 sequences ) and the Haiti complete genome sequence obtained in the present study . The second data set included 109 NS5 gene region reference sequences as well as NS5 sequences of the three Haitian isolates obtained in the present study . The third data set included 58 ENV gene region reference sequences as well as ENV sequences of the three Haitian isolates obtained in the present study . The fourth data set included 21 NS3 gene region reference sequences as well as the NS3 sequence of the Haitian isolate fully sequenced in the present study . Sequences in each dataset were aligned using ClustalW [23] followed by manual optimization using Bioedit [24] . The best fitting nucleotide substitution model for each data set was chosen in accordance with the results of the hierarchical likelihood ratio test ( HLRT ) implemented with the Modeltest software version 3 . 7 [25] . Detailed phylogenetic and phylodynamic methods are included in the supplemental material . In brief , the phylogenetic signal in each data set of aligned nucleotide sequences was investigated by likelihood mapping , which evaluates the tree-like signal in all possible groups of four sequences ( quartets ) [26]; . The NS5 data set , which included the largest number of sequences and the largest number of phylogenetic informative sites ( S1 Table ) , was used to investigate ZIKV phylogeographic patterns with the Bayesian coalescent framework implemented in Beast v 1 . 8 [27] . The maximum likelihood credibility ( MCC ) tree was chosen from the posterior distribution of trees with the TreeAnnotator program in the BEAST package . Statistical support for branching patterns in the MCC tree was obtained by calculating the posterior probability along each internal branch . The MCC tree with reconstructed ancestral states ( ancestral locations inferred by Bayesian phylogeography ) was manually edited in FigTree for display purposes . The protocol for sample collection was approved by the University of Florida IRB and the Haitian National IRB . Written parental informed consent was obtained from parents or guardians of all study participants .
Zika virus was identified in plasma from three students seen in the Christianville Foundation Schools clinic . Patient #1 ( described below ) appears to have been infected simultaneously with DENV-1 . The three case patients were from two different schools within the four-school Christianville school system; all lived in different towns/neighborhoods , within a radius of approximately 20 miles . All case patients presented within a one-week period in December , 2014 . Cases of DENV-1 had been identified among children in the school clinics in the weeks before occurrence of the ZIKV cases , which , in turn , were followed by a small cluster of DENV-4 cases . The first patient was a 15 year-old boy who presented to the clinic on December 12 , 2014 , with a history of subjective fever , headache , and generalized arthralgias , myalgias and asthenia . When seen , temperature was 37 degrees C , with a pulse of 92 and respiratory rate of 24 , weight 51 . 5 Kg . He had no rash , and physical exam was unremarkable . The second patient was a 7 year-old girl who was seen on December 15 , 2014 at the clinic for subjective nocturnal fever , abdominal pain , anorexia , and cough . Temperature was 37 degrees C , pulse 116 , RR 28 , and weight 22 . 8 Kg . There was no rash , and physical exam was again unremarkable . The third patient was an asymptomatic 4 year-old boy who came in December 17 , 2014 for follow up after being treated for tonsillitis on November 25 , when he had presented with a fever of 39 degrees C . In none of the cases would it have been possible to have identified the illness as a ZIKV infection based on clinical presentation , rather than DENV or CHIKV ( and , as indicated , one child was simultaneously infected with DENV ) . All patients received supportive care for reported symptoms , in keeping with standard practices within the clinic . In tissue culture , viral agents from all three patients induced subtle CPE within 4–8 days post-inoculation of human ( A549 , HeLa , and MRC-5 ) and more pronounced CPE in simian ( LLC-MK2 and Vero E6 ) cells incubated at either 33° and 37°C . Prior to cell death , the CPE consisted of perinuclear vacuoles ( Fig 1 ) . Electron microscopy revealed features typical of flavivirus-infected cells , such as the formation of paracrystalline arrays/convoluted membranes ( Fig 2A ) , crystalline arrays of nascent virus cores in association with double-membrane vesicles ( Fig 2B ) , multi-membraned “whorls” ( autophagosomes ) , individual 55–59 nm vesicles containing 40 nm virus particles , and virus particles in packets . As mentioned ( Materials and Methods ) , direct tests of the plasma sample using RT-PCR system Flav100F-200R yielded negative results . However , specific amplicons were obtained when vRNA from cyanase-treated spent media from LLC-MK2 or Vero cells were tested with the same primers . On sequence analysis , viral agents from all three patients were identified as ZIKV . On phylogenetic analysis , likelihood mapping showed that all data sets ( full genome alignment and gene-specific alignments ) displayed relatively low phylogenetic noise ( <20% , S2 Table ) and no recombination signal was detected . The full genome alignment was , as expected , the one with the lowest phylogenetic noise ( 0 . 3% ) , while the NS5 alignment contained the highest number of informative sites , as well as the largest number of available sequences ( S2 Table ) . Therefore , these two data sets were used to investigate further the phylogenetic and phylogeographic patterns of ZIKV . Maximum likelihood ( ML ) ( Fig 3 ) and Neighbor-joining ( NJ ) ( S1 Fig ) trees inferred from full genome sequences consistently show two major ZIKV clades: one including African , the other one including Asian , South American and the Haitian strains . In the ML tree ( Fig 3 ) , the earliest lineage in the African clade leads to a Ugandan strain , in agreement with the scenario of ZIKV emergence in the Eastern African country [1] . Moreover , both ML and NJ trees show three highly supported monophyletic clades within the African lineage , indicating a somewhat more complex pattern than a split between West African and Nigerian strains , as recently described [17 , 28] . Indeed , one clade includes Nigeria/Senegal sequences; a second one includes only Central Africa strains , while a third one includes two well-supported sub-clades , one with Ugandan and the other with Senegalese strains . South American/Haitian sequences cluster within the Asian clade and clearly branch out from a sequence circulating in Easter Island , which originated in turn from French Polynesia . The Haitian sequence clusters with a Brazilian sequence in a monophyletic clade related , in turn , to sequences from Suriname and the recently isolated strains from Guatemala and Puerto Rico [29] ( Fig 3 ) . The pattern is confirmed by the Bayesian phylogeographic analysis showing the Asian origin of the South American sequences ( Figs 4 and S2 ) , as well as the close phylogenetic relationship between Haitian , Brazilian , Suriname and Puerto Rican strains , clustering within a larger clade of isolates from Easter Island . While not statistically significant , this latter analysis , based on the NS5 region , does show slight separation of Haitian strains and the strains from Brazil , Suriname , Puerto Rico and Guatemala . The molecular clock calibration indeed shows that the most recent common ancestor ( MRCA ) of the Haitian clade existed at least one year earlier ( mid-2013 , 95% high posterior density interval December 2012 , June 2013 ) than the other South American lineages with the exception of the Easter Island ( Chile ) strains , which appear to be the oldest ( Fig 4 ) . The MRCA of the Asian lineage dates back to 1956 ( 95% high posterior density interval 1954–1958 ) , while ZIKV MRCA in Africa circulated , consistently with previous estimates [27] , since at least the early 1900s ( 95% high posterior density interval 1890–1925 ) .
Our data are consistent with the occurrence of an outbreak of Zita virus infection in rural areas of Haiti west of Port-au-Prince in December of 2014 . Virus was isolated from three students , coming from two different schools and different towns , suggesting that the infection was relatively widespread in the community . In keeping with prior descriptions of ZIKV infection [2] , illness was mild . Two patients reported subjective fevers prior to presentation at the clinic , but were afebrile on exam ( possibly due to use of local herbal antipyretics ) ; the third patient had had a temperature of 39 degrees three weeks before ( diagnosed as tonsillitis ) , but was asymptomatic at the time of blood collection . However , the outbreak was tightly bounded in time , with all cases occurring within a single week; we maintained similar surveillance methods across a 10 month period , and this one week was the only time that ZIKV was isolated . In keeping with reports from French Polynesia , cases occurred at a time when there was co-circulation of CHIKV and DENV , with cases immediately preceded by a cluster of DENV-1 cases ( with both DENV-1 and ZIKV isolated from the first patient identified ) , and followed by DENV-4 . Officially , no cases of ZIKV infection were reported by the Haitian Ministry of Public Health and Population ( MSPP ) until January 6 , 2016 , when 5 cases were confirmed in patients in the metropolitan Port-au-Prince area , based on RT-PCR assays performed at the Caribbean Public Health Agency “CARPHA” Laboratory at Trinidad and Tobago . While it is difficult to assemble an accurate timeline for Zika in Haiti , given the close similarity in symptoms with DENV and CHIKV cases and their apparent co-circulation , we would hypothesize that there was an initial “wave” of ZIKV cases in the late fall of 2014 in the Leogane/Gressier region , possibly emanating from near-by Port-au-Prince . Case numbers may have been reduced by relatively low rainfall amounts at that time , with persistence in the population and , in the setting of heavy rains in the fall of 2015 , occurrence of a larger epidemic in the fall of 2015/spring of 2016 . Alternatively , there may have been a reintroduction of the virus in late 2015; analysis of additional sequence data , from Haiti as well as from other countries , will be necessary to reconstruct the geographic progression of strains . Our phylogenetic analysis highlights the relative indolence of the global Zika epidemic prior to its introduction into Asia and the south Pacific in 2007 . In agreement with previous reports , the virus probably emerged in Africa at the beginning of the 20th century [28] , where it diversified in several regional sub epidemics that , according to our analysis , span the entire equatorial Africa from Uganda , to Central Africa to Senegal . ZIKV Asian lineages , on the other hand , are of more recent origin , dating back 50–60 years ago , and the recent epidemic outbreaks in South America are probably the result of a limited introduction from French Polynesia via Easter Island no more than 3–4 year ago . The factors responsible for the rapid spread of the virus , and it’s apparent trophism for neural tissue and ability to cause severe birth defects [10–12] , remain to be determined . The close association of ZIKV with the regional CHIKV epidemic , and epidemics of DENV , as we observed in Haiti , raises questions about immunologic interactions among these viruses , and/or the possibility that co-infection facilitates viral transmission or severity . Our observations highlight the critical ongoing need for careful epidemiologic and basic science research to guide public health interventions in Haiti and elsewhere where ZIKV is now epidemic . | Zika virus is currently spreading rapidly through the Americas , including the Caribbean , where it has emerged as a major public health problem due to the linkage with birth defects , including microcephaly . We report the isolation of Zika virus from 3 children in rural Haiti in December , 2014 , as part of a study of acute undifferentiated febrile illness that was being conducted by our research group; from one of these children , we also isolated dengue virus serotype 1 . On analysis of nucleotide sequence data from these and Zika strains from other locales , the South American/Haitian sequences cluster within the Asian clade and clearly branch out from a sequence circulating in Easter Island , which originated , in turn , from French Polynesia . On further analysis of one specific gene sequence for which more data were available , there appeared to be slight separation of Haitian strains and the strains from Brazil , Suriname , Puerto Rico and Guatemala , with molecular clock analysis suggesting that Zika virus was present in Haiti as early as mid-2013 . These findings raise questions about the origin of Zika virus in the Caribbean , and subsequent patterns of circulation of the virus within the Americas . | [
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... | 2016 | Zika Virus Outbreak in Haiti in 2014: Molecular and Clinical Data |
When energy is needed , white adipose tissue ( WAT ) provides fatty acids ( FAs ) for use in peripheral tissues via stimulation of fat cell lipolysis . FAs have been postulated to play a critical role in the development of obesity-induced insulin resistance , a major risk factor for diabetes and cardiovascular disease . However , whether and how chronic inhibition of fat mobilization from WAT modulates insulin sensitivity remains elusive . Hormone-sensitive lipase ( HSL ) participates in the breakdown of WAT triacylglycerol into FAs . HSL haploinsufficiency and treatment with a HSL inhibitor resulted in improvement of insulin tolerance without impact on body weight , fat mass , and WAT inflammation in high-fat-diet–fed mice . In vivo palmitate turnover analysis revealed that blunted lipolytic capacity is associated with diminution in FA uptake and storage in peripheral tissues of obese HSL haploinsufficient mice . The reduction in FA turnover was accompanied by an improvement of glucose metabolism with a shift in respiratory quotient , increase of glucose uptake in WAT and skeletal muscle , and enhancement of de novo lipogenesis and insulin signalling in liver . In human adipocytes , HSL gene silencing led to improved insulin-stimulated glucose uptake , resulting in increased de novo lipogenesis and activation of cognate gene expression . In clinical studies , WAT lipolytic rate was positively and negatively correlated with indexes of insulin resistance and WAT de novo lipogenesis gene expression , respectively . In obese individuals , chronic inhibition of lipolysis resulted in induction of WAT de novo lipogenesis gene expression . Thus , reduction in WAT lipolysis reshapes FA fluxes without increase of fat mass and improves glucose metabolism through cell-autonomous induction of fat cell de novo lipogenesis , which contributes to improved insulin sensitivity .
White adipose tissue ( WAT ) is the main energy store of the body in mammals . In the fed state , under the influence of insulin , WAT stores excess energy as triacylglycerols ( TGs ) in the lipid droplet of adipocytes . When energy is needed between meals or during physical exercise , WAT delivers fatty acids ( FAs ) to be oxidized in peripheral tissues . Lipolysis is the process by which stored TGs are released as nonesterified FA ( NEFA ) [1] . It involves different regulators such as lipases , co-lipases , and proteins that coat the lipid droplet . It is now largely accepted that the enzymatic breakdown of TG is initiated by adipose triglyceride lipase ( ATGL ) and leads to the formation of diacylglycerols ( DGs ) that are in turn hydrolyzed by hormone-sensitive lipase ( HSL ) [2] , [3] . HSL also shows TG hydrolase activity . The final step of this catabolic process is the hydrolysis of monoacylglycerols by monoglyceride lipase , leading to the release of one molecule of glycerol and three molecules of FA . Obese individuals are at increased risk of type 2 diabetes and cardiovascular disease . Insulin resistance is viewed as a cornerstone of the underlying pathogenic processes , and there is a wide disparity in insulin resistance among obese individuals [4] . FAs have been postulated to play a critical role in the development of insulin resistance [5] . Plasma NEFA levels and fluxes are not directly determined by the amount of body fat and are partly controlled by WAT lipolysis [6] . Furthermore , the relationship between circulating NEFA concentrations and insulin sensitivity in vivo is not straight forward . In that context , the influence of variation in fat cell lipolysis and lipase expression on insulin sensitivity and glucose metabolism remains elusive . Different consequences of diminished WAT lipolysis that are not mutually exclusive can be hypothesized . It could favour the development of obesity through retention of TG within adipocytes . It can also be viewed as a mechanism limiting an excess of FA release and alleviating the development of insulin resistance and metabolic abnormalities . This effect may be direct through the deleterious action of FA on insulin-sensitive tissues [7] . An indirect action may be considered via the modulation of WAT inflammation-induced insulin resistance as FAs produced by adipocytes stimulate cytokine production by macrophages [8] . Moreover , there is no clear picture of the relation between WAT lipolysis and glucose metabolism in WAT , skeletal muscle , and liver . Mice with full knockout of HSL and ATGL show complex phenotypes including marked alterations of fat mass that preclude conclusions on the effect of WAT lipolysis inhibition on insulin sensitivity [9] . Therefore , a major question of clinical relevance that remains unanswered is the role of FA release from WAT on insulin sensitivity and glucose metabolism and whether this control operates with an effect on fat mass . By a combination of clinical , animal , and cellular studies , we investigated the role of WAT lipolysis , including the specific contribution of HSL , in the control of insulin sensitivity and glucose metabolism . HSL level is a determinant of lipolytic capacity in human WAT [10] . Reduction of HSL activity and blunted stimulated lipolysis has been observed in obesity [11] , [12] . To gain mechanistic insights in this relationship , we investigated HSL haploinsufficient ( HSL+/− ) mice that showed impaired HSL expression and enzymatic activity and diminished WAT lipolysis . When fed a high-fat diet ( HFD ) , HSL+/− mice did not become more obese than wild type ( WT ) littermates and their WAT was not more inflamed . Lipid metabolism studied dynamically in vivo revealed a global reduction of FA fluxes in HSL+/− mice . The slowdown of FA turnover was accompanied by an amelioration of glucose metabolism , including glucose uptake and de novo lipogenesis in insulin-sensitive tissues . Insulin tolerance was improved both in mice with HSL haploinsufficiency and in mice treated with a HSL inhibitor . Improved adipocyte insulin-stimulated glucose uptake , observed both in vivo in mice and in vitro in human adipocytes , led to cell autonomous induction of de novo lipogenesis , which may contribute to improved insulin sensitivity when WAT lipolysis is decreased . The relevance of these observations was established in humans . Investigation of several cohorts of individuals with a wide range of BMI showed a positive association between fat cell lipolytic rate and indexes of insulin resistance and a negative association between lipolysis and expression of genes involved in fatty acid synthesis including the lipogenic transcription factor ChREBP ( carbohydrate responsive element-binding protein ) . Moreover , chronic treatment of obese individuals with an antilipolytic drug resulted in up-regulation of WAT de novo lipogenesis gene expression .
To investigate the relationship between WAT lipolysis and insulin sensitivity in humans , we first studied a large cohort of subjects presenting a wide range of BMI . A positive association was found in 367 individuals between spontaneous glycerol release measured ex vivo on WAT explants obtained after an overnight fast and the homeostasis model of indirect assessment of insulin resistance ( HOMA-IR ) ( Figure 1A ) . Variation in lipolysis explained 28% of the variance in HOMA-IR . When adjusted for age , gender , and BMI , 8% of the variance in HOMA-IR remained explained by lipolysis ( p<0 . 0001 ) . Dividing the cohort according to WHO criteria for normal weight , overweight , and obesity ( i . e . , 18 . 5<BMI<25 , 25<BMI<30 , 30<BMI<35 , 35<BMI<40 , BMI>40 kg/m2 ) , a correlation coefficient of 0 . 3 was found for each range of BMI ( p<0 . 01 ) , indicating that the relationship between high lipolysis and insulin resistance exists across the spectrum of fat mass . We then assessed insulin tolerance in 126 subjects , after an intravenous bolus injection of insulin . A negative association was found between insulin tolerance and lipolysis ( Figure 1B ) . To gain further insight , this relationship was investigated in 25 morbidly obese individuals who underwent bariatric surgery ( Figure 1C ) . WAT lipolysis and HOMA-IR were measured before and 2 years after bariatric surgery . A correlation was found between the change in lipolysis and the change in HOMA-IR ( Figure 1D ) . The higher the decrease in lipolysis , the stronger the improvement of insulin resistance . Altogether , these data suggest , in both cross-sectional and longitudinal studies , that , in humans , WAT lipolytic capacity may contribute to the control of insulin sensitivity . In search for an animal model with diminished WAT lipolysis and no marked alterations of WAT development , HSL+/− mice were generated by mating WT and HSL−/− mice . HSL ( lipe ) mRNA expression was 50% lower in epididymal WAT of HSL+/− mice compared to WT mice fed chow and HFD ( Figure S1A and Figure 2A , respectively ) . A similar difference between genotypes was obtained in subcutaneous WAT ( unpublished data ) . mRNA expression of ATGL ( pnpla2 ) , its co-activator CGI-58 ( abhd5 ) , and perilipin 1 ( plin1 ) was similar in WT and HSL+/− mice , suggesting that no compensatory mechanism occurred as a result of the reduction in HSL expression ( Figure 2A and Figure S1A ) . HSL protein expression was also reduced by 50% in HSL+/− compared to WT mice and undetectable in HSL−/− mice ( Figure 2B ) , whereas ATGL protein expression was not significantly altered in HSL+/− mice ( Figure 2C ) . In vitro total hydrolase activities against cholesterol ester ( Figure 2D and Figure S1B ) and TG ( Figure 2E and Figure S1C ) were reduced in WAT of HSL+/− mice , indicating that reduced expression of HSL had the expected impact on cognate enzymatic activities . HSL-specific TG hydrolase activity ( expressed as total activity minus activity in the presence of the HSL inhibitor ) was significantly decreased in HSL+/− compared to WT mouse WAT ( 1 . 45±0 . 11 versus 2 . 61±0 . 52 nmol/min . mg prot , p<0 . 05 , respectively ) . ATGL activity determined in the presence of the HSL inhibitor was not affected ( Figure 2E ) . In HFD-fed mice , consistent with reduced expression and activity of HSL in WAT , adipocytes had reduced in vitro lipolytic response to isoproterenol stimulation ( Figure 2F ) . In addition , in vivo β-adrenergic-stimulated lipolysis was also significantly reduced in HSL+/− mice ( Figure 2G ) . Therefore , decreased expression of HSL alters lipolytic function at a cellular as well as at a organism level . On chow diet , the evolution of body weight ( Figure S1D ) and fat mass ( Figure S1E ) were similar in WT and HSL+/− mice . The consequences of diminished HSL function were then investigated in mice fed HFD . As previously reported , HSL−/− mice were paradoxically resistant to diet-induced obesity [13] . WT and HSL+/− mice gained weight at a comparable rate and both became obese ( Figure 3A ) . After 12 weeks of HFD , WT and HSL+/− presented similar fat mass , while fat mass of HSL−/− mice was markedly lower ( Figure 3B ) . Fat pad weights were similar between WT and HSL+/− mice ( Figure 3C ) . Accordingly , plasma levels of leptin were identical in the two genotypes ( Table 1 ) . No difference in fat cell morphology was observed by histochemistry ( Figure 3D ) . Mean adipocyte diameter was not modified in HSL+/− mice compared to WT mice ( Figure 3E ) . Differentiation of WAT-derived progenitor cells into adipocytes was similar in WT and HSL+/− mice but markedly reduced in HSL−/− mice ( Figure 3F ) . Profound alterations in WAT gene expression involving PPARγ targets , FA synthesis and esterification , and retinoid and oxidative metabolisms have been reported in HSL null mice [13]–[16] . No alteration was observed in HSL+/− mice ( Figure S2 ) . There was no difference in WAT TG , DG , cholesterol ester , or free cholesterol contents between HSL+/− and WT mice ( Figure 3G ) . The assessment of energy balance showed similar food intake and energy expenditure in HSL+/− and WT mice ( Figure 3H and Figure 3I ) . Therefore , the data show that decreased expression of HSL , unlike complete lack of the enzyme , neither influences adipocyte differentiation and fat mass nor alters energy imbalance during diet-induced obesity . Since FA released from adipocytes may modulate WAT inflammation [8] , [17] , we investigated WAT macrophages and inflammatory molecules . The number of macrophages in the stroma-vascular fraction of WAT did not differ between HSL+/− and WT mice fed a HFD ( Figure 4A ) . Accordingly , mRNA levels of macrophage markers were not different ( Figure 4B ) . Gene expression of inflammatory markers was similar in WAT from HSL+/− and WT mice ( Figure 4C ) . Therefore , the decreased lipolytic capacity of HSL+/− mice does not induce a decrease in WAT inflammation . The decreased expression of HSL did not affect fasting plasma parameters ( Table 1 ) . NEFA , glycerol , TG , and total cholesterol levels were similar in both genotypes . As no noticeable alteration of metabolic parameters was observed in steady-state measurements , we determined FA fluxes in HFD-fed mice by stable perfusion of radiolabelled palmitate . Global tracer clearance that represents exit of the radioactive tracer from the blood compartment ( FA disappearance ) is an index of the combined ability of the tissues to take up FAs [18] . Clearance was markedly decreased in HSL+/− mice , indicating that partial HSL depletion has reduced peripheral FA uptake ( Figure 5A , left panel ) . The data suggest that the decreased rate of appearance of FAs in the blood due to decreased WAT lipolysis is , in a steady-state situation with constant plasma NEFA levels , matched by a similar rate of disappearance of FAs ( i . e . , decreased FA clearance ) . Global FA oxidation represented by radioactive water measured in plasma was reduced ( Figure 5A , right panel ) . Total radioactive FA storage , deduced from these measurements , was markedly decreased in HSL+/− mice ( Figure 5A , right panel ) . Global FA turnover estimated through the evolution of plasma radioactive palmitate isotopic dilution ( which is influenced by the clearance and dilution by cold and radioactive FA released from WAT in the fasted state ) was in turn reduced in HSL+/− mice compared to WT mice ( Figure 5A , right panel ) . Therefore , since the mice have been studied in steady-state condition , this suggests that the production rate ( WAT lipolysis in accordance with direct measurements shown on Figure 2F , Figure 2G , and liver TG production ) was lower as well . Tissue-specific radioactive FA incorporation into the TG pool was evaluated and showed reduction in WAT , heart , and soleus muscle of HSL+/− mice ( Figure 5B ) , further supporting the presumed decrease in peripheral FA uptake . Total TG content was not affected in WAT of HSL+/− mice , whereas it was decreased in soleus muscle , heart , and liver ( Figure 5C ) . Hence , the decreased lipolytic capacity in WAT induced by partial HSL deficiency provokes a diminution in FA uptake and storage in peripheral tissues . These changes take place without influencing total WAT mass . As a negative association was observed in humans between WAT lipolysis and insulin sensitivity ( Figure 1 ) , we next investigated peripheral insulin tolerance in HSL+/− mice . Insulin and glucose tolerance tests performed repeatedly on HFD-fed mice revealed that partial HSL depletion improved insulin and glucose tolerance in vivo with no difference in body weight between genotypes ( Figure 6A and Figure 6B ) . However , a gain in peripheral insulin sensitivity could not be observed during hyperinsulinemic euglycemic clamp at 6 mU kg−1 min−1 of insulin ( unpublished data ) . During clamp studies , insulin is infused at a constant rate to reach a steady state in which WAT lipolysis , which is highly sensitive to the antilipolytic action of the hormone , is strongly suppressed . Indeed , plasma NEFA levels measured during the clamp represented less than one third of fasting levels and were identical in HSL+/− and WT mice ( unpublished data ) . This chronic suppression may explain the lack of differences between genotypes during the clamp studies . Time course insulin tolerance tests first performed on animals fed a chow diet and then challenged after 6 wk of HFD confirmed the protective effect of HSL haploinsufficiency ( Figure 6C ) and indicated that the relationship between high lipolysis and insulin resistance exists across the spectrum of fat mass in agreement with human data when various ranges of body mass index are considered as reported above . To confirm that HSL haploinsufficiency protects against the development of insulin resistance , WT and HSL+/− mice were fed a high fructose diet known to appreciably alter insulin sensitivity ( Figure S3 ) . Insulin tolerance tests performed after 45 wk of diet showed that HSL+/− mice remained more insulin tolerant than WT mice ( Figure 6D ) , supporting the data obtained previously with the fat-enriched diet . In a therapeutic perspective , we tested the effect of pharmacological inhibition of HSL on insulin tolerance . Twelve-week HFD-fed mice were treated per os with a specific HSL inhibitor or vehicle for 7 d [11] , [19] . HSL inhibitor-treated animals showed an improvement of insulin sensitivity as shown by QUICKI ( Table 1 ) and of insulin tolerance ( Figure 6E ) compared to vehicle-treated mice without an effect on body weight and fat mass ( Figure 6F ) . Similar data were obtained on another genetic background ( Figure 6G ) . Treatment with the HSL inhibitor was also performed for 2 wk in 6-wk-old Leprdb/Leprdb mice that carry homozygous mutation of the leptin receptor and are genetically prone to obesity and diabetes . The treatment induced no change in body weight . Glycemia was comparable between HSL inhibitor- and vehicle-treated animals at different time points during glucose tolerance test ( Figure 6H ) . However , plasma insulin level at 15 min was lower in mice treated with the HSL inhibitor , indicating a better control of glycemia by insulin when HSL is inhibited ( Figure 6I ) . Similarly to partial genetic HSL depletion , pharmacological HSL inhibition protects mice from insulin and glucose intolerance . The improvement of insulin sensitivity in HFD-fed HSL+/− and HSL inhibitor-treated mice could result from changes in levels of adipokines with action on insulin signalling . Adiponectin plasma levels were not different from control animals in mice with partial genetic or pharmacologic inhibition of HSL ( Table 1 ) . Similarly , plasma concentrations of retinol binding protein 4 ( RBP4 ) , an adipokine involved in the development of insulin resistance [20] , were not modified in HSL+/− and HSL inhibitor-treated mice compared to control mice . To shed light on the origin of the global improvement in insulin tolerance in HSL+/− mice , in vivo insulin-stimulated glucose uptake was determined in various tissues . Glucose uptake was increased in soleus ( oxidative ) muscle and showed a tendency to increase in biceps femoris ( glycolytic ) muscle ( Figure 7A ) . An increase in glucose uptake was also observed in WAT . Furthermore , glucose oxidation measured ex vivo in soleus muscle was increased ( Figure 7B ) . There were no differences in DG and glycogen contents between HSL+/− and WT skeletal muscle ( unpublished data ) . Interestingly , respiratory quotient was increased in HSL+/− mice showing a shift from FA to glucose as energy substrate ( Figure 7C ) . To determine whether liver was involved in the improvement of glucose and insulin tolerance in HSL+/− mice , we administered a bolus of insulin and measured the effects on the hepatic insulin signalling pathway . In agreement with an improved insulin tolerance , the decrease of glycemia was greater in HSL+/− mice than in WT mice ( Figure 7D ) . In response to exogenous insulin administration , levels of phosphorylated insulin receptor substrate 1 and Akt were increased in the liver of HSL+/− mice supporting an improvement of insulin signalling ( Figure 7E ) . HSL+/− mice infused with radiolabelled glucose showed a rise in glucose carbon incorporation into hepatic lipids—that is , de novo lipogenesis—compared to WT mice ( Figure 7F ) . Pyruvate tolerance test revealed that gluconeogenesis was reduced in HSL+/− mice ( Figure 7G ) . The reduced hepatic glucose production was associated with decreased glucose storage as demonstrated by reduced glycogen content in HSL+/− mice ( Figure 7H ) . Liver DG content was unchanged in HSL+/− mice ( unpublished data ) . As changes in the capacity of the pancreas to produce insulin could modify glucose tolerance , we then investigated pancreatic function in vivo and in vitro . Neither arginine tolerance test performed on 12-wk HFD-fed animals ( Figure S4A ) nor in vitro glucose-stimulated insulin secretion of isolated pancreatic islets ( Figure S4B ) revealed changes in insulin secretion in HSL+/− compared to WT mice , arguing against a direct effect of HSL haploinsufficiency on the capacity of pancreatic β cells to secrete insulin . Altogether , adaptations in WAT , skeletal muscle , and liver explain the global improvement in glucose and insulin tolerance observed in mice with HSL haploinsufficiency . In order to determine whether modifications of fat cell metabolism observed in vivo in HSL+/− mice were cell autonomous , expression of HSL was knocked down in human hMADS adipocytes . Adipocytes transfected by HSL siRNA showed a 70% reduction in LIPE mRNA expression compared to GFP siRNA transfected cells ( Figure 8A , upper panel ) . HSL protein expression was also reduced in HSL-silenced adipocytes ( Figure 8A , lower panel ) . As observed at the whole body level in HSL+/− mice ( Figure 5A ) , HSL gene silencing resulted in a decrease of FA oxidation in human adipocytes ( Figure 8B ) . HSL gene silencing also led to an increase in insulin-stimulated glucose uptake ( Figure 8C ) as shown in vivo in HSL+/− mouse WAT ( Figure 7A ) , accompanied by a rise in insulin-stimulated glucose oxidation ( Figure 8D ) . The enhanced influx of glucose was associated with an increase in glyceroneogenesis and de novo lipogenesis in adipocytes knocked down for HSL ( Figure 8E and Figure 8F ) . Expression of the glucose transporter GLUT4 and of glycolytic and fatty acid synthesis genes showed coordinated up-regulation in HSL-silenced adipocytes ( Figure 9A and Figure 9B ) . RBP4 mRNA ( 1 . 4-±0 . 2-fold siHSL/siGFP , n = 9 , p<0 . 05 ) and secreted protein levels ( 1 . 2-±1 . 0-fold siHSL/siGFP , n = 11 , p<0 . 001 ) were slightly but significantly increased in hMADS adipocytes transfected with HSL siRNA ( Figure S5 ) . This piece of data does not support a role of this adipokine in the improvement of insulin action and glucose metabolism when HSL expression is lowered . As glucose uptake and fatty acid synthesis were upregulated in vitro in human adipocytes with low HSL level , we investigated correlations between lipolysis and expression of key genes of these pathways in vivo in human WAT . Simple regression analysis showed negative associations between GLUT4 , fatty acid synthase or the lipogenic transcription factor ChREBP mRNA levels , and WAT lipolytic rates ( Figure 9C ) . The negative associations persisted in multiple linear regression analyses with BMI as the covariate ( partial R = 0 . 3 , p<0 . 05 ) . No association was found between mRNA level of SREBP1c , another transcription factor controlling expression of FA synthesis genes , and lipolysis ( p = 0 . 96 ) . As further proof supporting the link between human fat cell lipolysis and fat cell de novo lipogenesis , 8-wk treatment of obese male individuals with nicotinic acid , which inhibits lipolysis through activation of a fat cell Gi protein-coupled receptor [1] , resulted in up-regulation of adipocyte de novo lipogenesis gene expression ( Figure 9D ) .
Insulin resistance is a critical pathogenic process linking obesity to type 2 diabetes and cardiovascular diseases . To date , there is convincing evidence in humans that FAs cause deleterious effects on insulin signalling in peripheral organs [21] . The working models are based on overload of lipids from exogenous sources—that is , dietary FA for HFD or FA produced by lipoprotein lipase-mediated hydrolysis of TG during lipid and heparin infusion [7] , [22] . However , little is known on the effect of FAs released by WAT lipolysis on the modulation of fat mass and insulin sensitivity . The physiological significance of lipolysis may be seen in two , not necessarily mutually exclusive , ways . Impaired lipolytic capacity could contribute to the development of obesity through impairment in the mobilization of fat stores . Alternatively , the defect may protect against excessive FA release and ensuing deleterious action of FAs on insulin sensitivity . To address this clinically relevant question , we used a translational approach combining human and animal studies together with cellular investigations . As we previously showed that the level of HSL expression controls lipolytic rate in human fat cells and is altered in obesity [10]–[12] , the association between WAT lipolysis , fat mass , and insulin sensitivity was investigated in mice with HSL haploinsufficiency . To address this relationship , lipase total knockout mouse models may not prove suitable . HSL null mice show impaired development of WAT when fed a HFD and marked WAT inflammation in the absence of obesity [13] , [23] . Metabolic defects are observed in multiple organs of ATGL global knockout mice , causing a complex modulation of insulin sensitivity [9] , [24]–[26] . WAT-specific ablation of ATGL shows markedly altered thermogenesis [27] . Therapeutic relevance was provided by studies on mice treated with a specific HSL inhibitor . Cell-autonomous effects of HSL deficiency were investigated in hMADS adipocytes , a validated model for the study of human fat cell metabolism [2] , [28] . The relationship between WAT lipolysis and glucose metabolism was further explored in four independent cohorts of individuals with a wide range of BMI . The results are summarized on Figure 10 . Phenotyping obese mice with reduced HSL activity in WAT first revealed that a partial defect in lipolysis did not modify adiposity and , hence , that chronic inhibition of FA release from WAT did not contribute to the development of obesity . Strikingly , complete HSL deficiency leads to resistance to HFD-induced obesity [13] and impaired adipogenesis ( present work ) . However , the increase in body fat mass , weights of fat depots , and adipocyte size was not compromised in HSL+/− mice fed HFD . It may be hypothesized that the presence of an active allele is sufficient to compensate for the defect in adipogenesis , which has been linked to an impaired production of signalling lipolytic by-products [16] , [29] . The identical fat mass in HSL+/− and WT mice was supported by similar food intake , energy expenditure , and leptin levels in the two genotypes . The normal development of WAT in a condition of diminished adipose lipolysis raised questions on the dynamics of lipid fluxes . Using a fluxomics approach [18] , we show that HSL+/− mice presented altered global FA turnover , decreased WAT lipolysis being balanced by reduced FA esterification in WAT . Accordingly , we have previously reported a coupling between FA release and re-esterification in vitro in human adipocytes [2] . The decreased FA oxidation observed in human adipocytes with altered lipolytic capacity may result from decreased FA availability . In heart and brown adipose tissue , it has been postulated that FA need to be esterified into TG and then mobilized for being properly oxidized in mitochondria [30] , [31] . Moreover , enhanced de novo lipogenesis in adipocytes knocked down for HSL results in an increase of the levels of malonyl-CoA , which inhibits the rate-limiting step in FA oxidation , carnitine palmitoyl transferase 1b [32] . Therefore , partial inhibition of WAT lipolysis results in a modification of FA fluxes in vivo and in fat cells without alteration of fat mass . Our data in humans show , on a large number of individuals , a strong association between WAT lipolysis and indexes of insulin resistance independently of fat mass . This relation was confirmed in a longitudinal study . Two years after bariatric surgery in morbidly obese individuals , the diminution of lipolytic rate was positively correlated with the improvement of insulin sensitivity . In obese HSL haploinsufficient mice , we observed an improvement of glucose metabolism and insulin tolerance . It happens with a global slowdown of FA turnover and unchanged plasma FA levels in the overnight fasted state , a condition observed in many obese individuals [6] . Results from insulin and glucose tolerance tests were supported by organ-specific adaptations . In the liver , insulin action was improved as revealed by an increase in tyrosine phosphorylation of insulin receptor substrate 1 and serine phosphorylation of Akt . An improvement in hepatic insulin sensitivity has been reported in one model of total HSL deficiency [33] . Hepatic glucose fluxes were modified in HSL haploinsufficient mice . The decrease in glucose production , which could partially result from the reduced glycerol availability , is balanced by a decrease of glucose storage as glycogen . The shift in respiratory quotient could sign an increase in carbohydrate usage and decreased lipid utilisation but could also result from the conversion of carbohydrate to lipid and its subsequent oxidation as this process has the same respiratory quotient as direct oxidation of carbohydrates . Accordingly , soleus muscle glucose oxidation and hepatic de novo lipogenesis were increased in HSL+/− mice . In vivo insulin-stimulated glucose uptake was increased in WAT and skeletal muscles of HFD-fed HSL+/− mice , suggesting an improvement in peripheral insulin sensitivity . It is now well established that lipid-induced insulin resistance in skeletal muscle stems from defects in insulin-stimulated glucose transport [7] . Moreover , a better glucose uptake in fat cells may participate in the amelioration of insulin sensitivity at the whole body level . Indeed , adipocyte-specific invalidation of GLUT4 abolishes insulin-stimulated glucose uptake in WAT and impairs insulin action in liver and skeletal muscle [34] . RBP4 was described as a potential mediator of insulin resistance in these mice [20] . However , in vivo data in mice as well as in vitro data in human adipocytes show that it is unlikely that RBP4 contributes to the improvement of insulin tolerance and glucose metabolism promoted by diminished WAT lipolysis . The rise in insulin-stimulated glucose uptake was also observed in a cell-autonomous manner in human adipocytes with diminished HSL expression . It was accompanied by increased glucose oxidation and glucose carbon incorporation into FA and glycerol—that is , increased de novo lipogenesis and glyceroneogenesis . Glucose uptake controls adipocyte de novo lipogenesis , which has recently appeared as a major determinant of whole body insulin sensitivity [35] , [36] . The negative correlations between GLUT4 , ChREBP , or fatty acid synthase mRNA levels and WAT lipolytic rate in vivo in humans strongly support that notion . As further proof of concept , chronic inhibition of WAT lipolysis with nicotinic acid resulted in an increase of fat cell de novo lipogenesis gene expression in obese individuals . In HFD-fed obese HSL+/− mice , the concomitant up-regulation of de novo lipogenesis in WAT and liver may play an important part in the favourable metabolic profile [35] , [37] , [38] . Altogether , the modifications in FA metabolism resulting in enhanced fat cell glucose uptake and de novo lipogenesis are therefore likely to contribute to the improvement of insulin sensitivity . Increase in WAT macrophage number and expression of immune cell-derived cytokines and chemokines may contribute to obesity-induced insulin resistance [39] . As FAs released from 3T3-L1 adipocytes have been shown to stimulate pro-inflammatory cytokine production by RAW264 macrophages , it could be hypothesized that inflammation would be diminished in WAT from obese HSL+/− mice [8] . However , neither macrophage number nor gene expression of macrophage markers and inflammatory factors were modified , indicating that chronic inhibition of lipolysis , unlike acute stimulation , does not modify the content of macrophages in WAT [17] . In HSL+/− mice , the lack of alteration in WAT inflammation is , however , in line with the lack of change in fat mass . This work has relevance on therapeutic strategies aimed at preventing the development of obesity-associated insulin resistance . The interest in antilipolytic drugs has been shown with nicotinic acid , which has been used for decades as a lipid-lowering drug . This compound acts through a G-protein-coupled receptor with antilipolytic action in fat cells [1] . However , the receptor is expressed in other cell types than adipocytes , and nicotinic acid shows receptor-independent effects in the liver . The use of the drug has been restricted due to upper-body skin flushing . Moreover , data on insulin sensitivity are conflicting [40] . A search for alternative drugs with an antilipolytic effect has led to synthesis of several series of HSL inhibitors [41] . The compounds are highly selective in part because of the low homology between HSL and known mammalian lipases [42] . We show here that chronic pharmacological inhibition of lipolysis using a selective HSL inhibitor improved insulin action in HFD-fed mice and genetically obese LepRdb/LepRdb mice . From the clinical data presented here , it appears that insulin-resistant individuals with a high lipolytic rate will benefit the most from treatment with antilipolytic molecules such as HSL inhibitors . An additional clinical advantage with HSL inhibition is the lack of effect , at least in mice , on fat mass . As discussed earlier , plasma levels of FA poorly correlate with insulin sensitivity [6] . Our data do not dispute this notion but suggest that it is FA fluxes rather than FA levels that determine insulin sensitivity . In summary , a decrease in WAT lipolysis results in a slowdown of FA turnover associated with improved insulin tolerance and glucose metabolism and no change in fat mass ( Figure 10 ) . Long-term moderate inhibition of WAT lipolysis can therefore be beneficial in the treatment of obesity-related insulin resistance .
Clinical studies were approved by the ethics committee of Karolinska Institute University Hospital and Toulouse University Hospitals . Informed consent was obtained from each participant . All experimental procedures on mice were performed according to INSERM and Genotoul Anexplo animal core facility guidelines for the care and use of laboratory animals . Targeted disruption of the HSL gene and generation of HSL−/− mice have been described elsewhere [43] . Four- to 5-wk-old B6D2 mice were fed chow diet ( 10% kCal fat , D12450B , Research Diets Inc . ) for 6 or 28 wk , HFD ( 45% kCal fat , D12451 , Research Diets Inc . ) for 12 to 16 wk , or fructose-enriched diet ( D11743 , Research Diets Inc . ) for 45 wk . C3H/HeJ mice were purchased from Jackson Laboratories . They were fed a HFD for 7 wk . For fasting-refeeding experiments , 12-wk HFD-fed WT and HSL+/− male mice were overnight fasted and refed for 18 h before sacrifice . For chronic HSL inhibition , the specific HSL inhibitor synthesized by IDEALP PHARMA was given orally at 70 mg/kg/d [19] . B6D2 and LepRdb/LepRdb mice were , respectively , treated once daily during 7 or 13 consecutive d . Body weight was measured weekly . Food intake was measured daily during 4 d in animal housed individually . Body mass composition was evaluated by quantitative nuclear magnetic resonance system ( EchoMRI 3-in-1 , Echo Medical Systems ) . In vivo lipolytic challenge by intraperitoneal ( ip ) injection of the β-adrenergic agonist isoproterenol ( 10 mg/kg ) on mice fasted for 7 h was performed as previously reported [44] . For ip insulin tolerance tests , an injection of 0 . 6 U/kg insulin was given to 6-h–fasted mice . For oral glucose tolerance tests , an oral administration of 1 . 5 g/kg D-Glucose was given to 16-h–fasted mice with , in LepRdb/LepRdb mice , supplemental blood sampling performed at 15 min for insulin measurement . For pyruvate tolerance test , an ip injection of 2 g/kg pyruvate was performed on 16-h–fasted mice . Blood glucose levels were monitored from the tip of the tail vein with a glucometer ( Accucheck , Roche ) . Arginine tolerance test was performed on WT and HSL+/− mice fed a HFD for 12 wk . The 16-h–fasted mice anesthetized with isoflurane were ip injected with 3 g/kg of L-arginine ( Sigma ) . Blood sampling for insulin measurement was performed at the retro-orbital sinus . For the measurement of in vivo glucose utilization in individual tissues , an intravenous bolus of 50 µCi 2-deoxy-D-[3H] glucose ( Perkin Elmer ) was given during euglycemic hyperinsulinemic clamp ( 6 mU kg−1 min−1 of insulin ) . Disappearance of plasma 2-deoxy-D-[3H] glucose and glucose concentrations were determined in 5 µl blood samples from the tail vein . Different tissues were dissected and dissolved in 1 M NaOH during 1 h . 2-deoxy-D-[3H] glucose 6-phosphate and 2-deoxy-D-[3H] glucose were differentially precipitated by the use of zinc sulfate ( 0 . 3 M ) , barium hydroxide ( 0 . 3 M ) , and perchloric acid solutions ( 6% ) . For the measurement of in vivo hepatic de novo lipogenesis , livers were harvested after an euglycemic hyperinsulinemic clamp ( 1 . 5 mU kg−1 min−1 of insulin ) with D-[3-3H]glucose ( Perkin Elmer ) infusion at 15 . 9 kBq/min . After homogenization in a lysis buffer , lipids were extracted by a Folch method and the organic phase was counted for radioactivity . For in vivo measurements of phospho-insulin receptor substrate 1 and phospho-Akt , animals fed a HFD for 12 wk were fasted for 6 h before ip injection of 10 U/kg insulin . Liver was harvested 15 min after injection . Liver proteins were solubilised in RIPA buffer containing protease and phosphatase inhibitors . Protein samples were resolved by SDS-PAGE , blotted , and incubated with anti-phospho insulin receptor substrate 1 ( Tyr612 ) , phospho Akt ( Ser473 ) , total insulin receptor substrate 1 , or total Akt antibodies ( Cell Signaling ) . Equal loading was confirmed using anti-GAPDH protein . To investigate FA fluxes , a catheter was inserted into the femoral vein in HSL+/− and WT animals under isoflurane anaesthesia . Mice were allowed to recover for 4–5 d before assessing FA fluxes in awake free-moving mice . Mice were then infused in fasting conditions ( during 6 h from 08:00 ) with a tracer solution freshly prepared each day . Infusate was prepared with 50 µM palmitate and [9 , 10-3H] palmitic acid . Infusions were performed at a constant rate of 0 . 2 µCi/4 µl/min for 2 h . Blood samples ( 30 µl ) were collected from the tip of the tail at −30 , 0 , 60 , 75 , 90 , and 120 min . Tracer infusion was stopped at 120 min , and additional blood samples were collected at 125 , 130 , and 140 min . Blood samples were rapidly centrifuged to prepare plasma , which was kept at −80°C until biochemical measurements . Total plasma NEFA and TG were measured with a colorimetric enzymatic method ( respectively , NEFA C , Wako , and TG PAP150 , Biomerieux ) . Lipid extraction and separation procedure were performed to determine plasma 3H2O , 3H-NEFA and 3H-TG as described by Oakes et al . [18] . During a first step , 3H2O was separated from lipid phase using an isopropanol-hexane-H2SO4 0 . 5 M mixture . In a second step , TGs were separated from neutral lipids using an alkaline methanol solution . Finally , NEFAs were extracted from neutral lipid phase using an acid hexane solution . Radioactivity was counted in each fraction . Whole body rates of FA clearance , appearance , oxidation , and storage were determined as described by Oakes et al . [18] . At 140 min , the last blood sample was collected from retro-orbital sinus . Mice were euthanized with an intravenous bolus of pentobarbital . Different tissues were collected and weighed before freezing in liquid nitrogen and storage at −80°C for total TG content assessment . A sample of WAT , soleus muscle , and heart was homogenized in water . Non-3H2O products were extracted using an isopropanol-hexane-H2SO4 0 . 5 M mixture , and radioactivity was counted . NEFA incorporation rates into tissue-specific storage products were calculated between 120 and 140 min . Oxygen ( VO2 ) and carbon dioxide ( VCO2 ) production was measured using a four-chamber oxylet system ( Bioseb ) . Temperature was maintained at 21±1°C , and the light was on from 07:00 to 19:00 . System setting included a flow rate of 0 . 3 l/min , a sample purge of 5 min , and a measurement period of 5 min every 25 min . Twenty-four h prior to data collection , mice were placed in separate calorimetry chambers ( each with a volume of 2 . 5 l ) , with free access to food and water . The respiratory quotient was calculated as the ratio of VCO2/VO2; results were expressed as percent of relative cumulative frequency along the measurement period [45] . For measurements of plasma parameter concentrations , insulin , NEFA , and glycerol were determined by ELISA ( Mercodia ) , an enzymatic colorimetric reaction ( NEFA C , Wako ) , and with the hydrazine buffer method or with a commercial kit ( Sigma ) , respectively . TG and cholesterol were determined with a COBAS-MIRA + analyzer ( ABX Diagnostics ) . Adiponectin , leptin , and RBP4 were measured with commercial ELISA kits ( R&D Systems ) . Overnight fasted mice were euthanized at various weeks of age depending on experiments , blood was collected in EDTA tubes , and various tissues were removed , immediately weighed , frozen in liquid nitrogen , and stored at −80°C . Epididymal fat samples were fixed in 1% formalin ( Sigma ) , embedded in paraffin , and processed to hematoxylin and eosin staining . Digital images were captured using light microscope coupled to a camera and analyzed using a morphometric programme ( Lucia IMAGE , version 4 . 81; Laboratory Imaging ) . Adipocyte size was determined on a histological preparation measuring area of at least 200 adipocytes . In adipogenesis test , the stromavascular fraction ( SVF ) was isolated from subcutaneous WAT of 12-wk HFD-fed WT , HSL+/− , and HSL−/− mice . WAT from 2–3 mice were pooled together . SVF cells were plated at a density of 70 , 000 cells per well , in a 48-well plate , in 10% FBS-endothelial cell basal medium . Media were replaced at Day 1 and Day 4 . At Day 5 , the medium was replaced with 2% FBS—endothelial cell basal medium supplemented with 20 nM insulin , 0 . 2 nM T3 , 100 nM cortisol , 0 . 01 mg/ml transferrin , and then changed every 2 d . At Day 14 , lipid accumulation was estimated through Oil Red O staining normalized by total DNA amount measured using PicoGreen ( InVitrogen ) . For flow cytometry analysis of WAT , SVF cells were obtained by collagenase digestion as previously described [46] . After digestion , the suspension was filtered through 150 µm sieves and centrifuged ( 100 g , 10 s ) to collect the infranatant containing the SVF . The lower phase was centrifuged ( 400 g , 10 min ) , and the pellet containing the SVF was incubated for 10 min in erythrocyte-lysis buffer ( 155 mM NH4Cl , 5 . 7 mM KH2PO4 , and 0 . 1 mM EDTA ) , filtered through 40 µm sieves , and centrifuged again ( 400 g , 10 min ) . The pellet was then resuspended in PBS containing 2 mM EDTA and 0 . 5% bovine serum albumin . The total number of cells was counted using Trypan blue ( Gibco , Courbevoie , France ) and a Neubauer hematocytometer ( Poly Labo , Paul Block & Cie , Strasbourg , France ) . The cell count was confirmed by DNA determination using fluorometric assay ( Picogreen , Invitrogen , Cergy Pontoise , France ) . We incubated 100 , 000 cells of the SVF with FITC-conjugated antibodies ( F4/80 ) , PerCP-conjugated antibody ( CD45 ) , PE-Cy7-conjugated antibody ( CD11b ) , and respective isoptype control . Analyses were performed using a FACSCanto flow cytometer and the BD FACS Diva software ( BD Bioscience ) . The total number of each cell population present in the fat depot was calculated as a product of the percentage of each cell type determined by the flow cytometry analyses and the total number of SVF cells . Results were presented per milligram of WAT . In vitro triolein and cholesterol oleate hydrolase activities were performed as previously described [2] . Briefly , total protein from gonadal WAT were extracted and mixed with 14C-labelled triglyceride or cholesterol ester analogue , and subsequent radiolabelled fatty acids were extracted and counted . In order to estimate the enzymatic activity resulting from other lipases than HSL in these assays , the HSL-specific inhibitor was used at 1 µM [11] . For gene expression analysis , WAT was homogenized in Qiazol buffer ( Qiagen ) using Precellys tissue homogenizer . Total RNA from WAT was extracted using RNeasy kit ( Qiagen ) . RNA concentration and purity were assessed spectrophotometrically using NanoDrop ( DigitalBio ) . After treatment with DNase I ( Invitrogen ) and reverse transcription of 1 µg of total RNA with Superscript II ( Invitrogen ) or Multiscribe Reverse Transcriptase ( Applied Biosystems ) , real-time quantitative PCR was performed with Applied Biosystems Step One Plus real-time PCR system . A standard curve was obtained using serial dilutions of WAT cDNA prior to mRNA quantitation . 18s rRNA and HPRT mRNA were used as controls to normalize gene expression . For neutral lipid measurement , small pieces of tissues were homogenized in 1 ml 5 mM EGTA water∶Methanol ( 1∶2 ) using Precellys tissue homogenizer . Lipids were extracted with a mixture Methanol/Chloroform/Water ( 2 . 5/2 . 5/1 . 7 volume ) purified with SPE column , dried , and dissolved in ethyl acetate . The fraction was measured by gas chromatography . For glycogen measurement , liver and hind limb muscles were dissected from mice either fasted for 24 h or fasted and refed for 18 h and snap frozen in liquid nitrogen . Tissues were then dissolved in 200 µl of 1 N NaOH 1 h at 55°C . Digestion is neutralized by 200 µl of 1 N HCl and centrifuged to remove cell debris . Amyloglucosidase ( 50 U/ml ) is then added and incubated for 1 h at 55°C . Glucose content is then measured by the RTU kit method ( Biomerieux , France ) . Results are normalized by mg of tissue . To determine ex vivo glucose oxidation , soleus skeletal muscles were homogenized with a polytron homogenizer in a buffer containing 0 . 25 M Sucrose , 1 mM EDTA , 1 µM Tris-HCl , and 2 mM ATP at pH 7 . 4 . We incubated 80 µl of homogenized sample at 37°C for 2 h with 0 . 2 mM cold D-glucose , 1 µCi/ml [U-14C]glucose , 0 . 5% BSA , 125 mM Sucrose , 25 mM Potassium phosphate monobasic , 200 mM Potassium chloride , 2 . 5 mM Magnesium chloride , 2 . 5 mM L-Carnitine , 0 . 25 mM Malic acid , 20 mM Tris-HCl , 2 . 5 mM DTT , 0 . 25 mM NAD+ , 4 mM ATP , and 0 . 125 mM Coenzyme A . Following incubation , 40 µl of 70% perchloric acid was added to trap CO2 production for 1 additional hour at room temperature . We counted 200 µl of NaOH , containing trapped CO2 , using a scintillation counter ( Tri-Carb2100TR; Pakard ) . An acidified portion was collected , placed at 4°C overnight , centrifuged at 15 , 000 g for 15 min at 4°C , and the supernatant was counted . A sample of the incubation medium was used to quantify specific activity . To measure ex vivo glucose-stimulated insulin secretion by pancreatic islets , WT and HSL+/− mice fed a HFD for 12 wk were anesthetized by ip injection of 50 mg/kg of pentobarbital . The common bile duct was catheterized and pancreas infused with a pre-oxygenated fixative solution [2 mg/ml of liberase ( Roche ) in Hanks–Hepes buffer] . Mice were sacrificed , and the pancreas was removed and digested for 8 min at 37°C . Digestion was stopped by addition of Hanks buffer supplemented with 2% BSA . After rinsing , the pellet was dissolved in 20 ml of Histopaque ( Sigma ) and 20 ml of Hanks/BSA buffer before being centrifuged 20 min at 1 , 000 g at room temperature . Pancreatic islets were collected by aspiration at the interphase . After purification , pancreatic islets were selected according to morphology and size and incubated three per well in a ACROPREP 96-well filter plate ( VWR , France ) in 200 µl of a medium containing 2 . 8 or 16 . 7 mM of glucose for 90 min at 37°C in an incubator . Insulin was assessed in the medium after centrifugation of the filter plate . hMADS cells were maintained in proliferation medium ( Dulbecco's modified Eagle's medium low glucose , 10% fetal bovine serum , 2 mM L-glutamine , 10 mM Hepes buffer , 50 units/ml of penicillin , 50 µg/ml of streptomycin , supplemented with 2 . 5 ng/ml of human fibroblast growth factor 2 ) . The cells were inoculated in 100 mm dishes at a density of 525 , 000 cells and kept at 37°C in 5% CO2 . Six days postseeding , fibroblast growth factor 2 was removed from proliferation medium . On the next day ( day 0 ) , the cells were incubated in differentiation medium ( DM: serum-free proliferation medium/Ham's F-12 medium containing 10 µg/ml of transferrin , 5 µg/ml of insulin , 0 . 2 nM triiodothyronine , 100 µM 3-isobutyl-1-methylxanthine , 1 µM dexamethasone , and 100 nM rosiglitazone ) . At day 3 , dexamethasone and 3-isobutyl-1-methylxanthine were omitted from DM , and at day 10 , rosiglitazone was also omitted . For glucose metabolism experiments , insulin concentration was 10 nM from day 7 . The experiments were carried out between days 12 and 15 . RNA interference was achieved by small interfering RNA . Briefly , on day 7 of differentiation , hMADS cells were detached from culture dishes with trypsin/EDTA ( Invitrogen ) and counted . Control siGFP or gene-specific small interfering RNAs for HSL ( Applied Biosystems ) were delivered into adipocytes with a microporator ( Invitrogen ) with the following parameters: 1 , 100 V , 20 ms , 1 pulse . The targeted sequences , flanked with dTdT overhangs , are: GFP , 5′-GCAGCACGACUUCUUCAAG-3′; HSL , 5-AGGACAACAUAGCCUUCUU-3′ . Human fibroblast growth factor 2 , insulin , triiodothyronine , transferrin , 3-isobutyl-1-methylxanthine , and dexamethasone were from Sigma; L-glutamine , penicillin , and streptomycin from Invitrogen; Hepes , Dulbecco's modified Eagle medium low glucose , and Ham's F-12 medium from Lonza; and rosiglitazone from Alexis Biochemicals . To determine FA oxidation , cells were incubated during 3 h in 1 ml Krebs Ringer Buffer containing 3% BSA , 1 mM L-Carnitine , 80 µM oleic acid , and 20 µM [1-14C] oleic acid ( PerkinElmer ) . For medium-trapped 14CO2 extraction , medium was transferred and acidified with 1 M sulfuric acid in closed vials containing a central well filled with benzethonium hydroxide . After 2 h incubation , trapped 14CO2 was measured by liquid scintillation counting . Cells were washed twice with PBS and then scraped in cold buffer ( 0 . 25 M sucrose; 10 mM Tris HCl; 1 mM EDTA; 1 mM dithiothreitol , pH 7 . 4 ) . Neutral lipids and aqueous soluble metabolites were separated by adding 5 volumes chloroform/methanol ( 2∶1 ) and 0 . 4 volume 1 M KCl/HCl . Acid soluble products , 14C-labeled oxidative intermediates , were measured in aqueous phase by liquid scintillation counting . Specific activity was measured and used to calculate total oxidation as equivalent of oxidized oleic acid . Glucose uptake was measured using 2-deoxy-D-glucose . The day before the assay , insulin was removed from culture medium . After two washes with PBS , cells were incubated 50 min at 37°C with or without 100 nM insulin . Then , 125 µM 2-deoxy-D-glucose and 0 . 4 µCi 2-deoxy-D-[3H] glucose per well were added for 10 min incubation . Culture plates were put on ice and rinsed with 10 mM glucose in ice-cold PBS and then with ice-cold PBS . Cells were scraped in 0 . 05 N NaOH , and 2-deoxy-D-glucose uptake was measured by liquid scintillation counting of cell lysate . To determine glucose oxidation , insulin was removed from culture medium the day before the assay . Cells were incubated for 3 h in Krebs Ringer buffer supplemented with 2% BSA , 10 mM HEPES , 2 mM glucose , and 1 µCi D-[14C ( U ) ]glucose ( PerkinElmer ) with or without 100 nM insulin . A 2×2 cm Whatman 3M paper was placed on top of each well and wet with 100 µl 1 N NaOH . After incubation , filter-trapped 14CO2 was measured by liquid scintillation counting . Medium-trapped 14CO2 was measured as described previously for oleate oxidation . Specific activity was counted and used to determine the quantity of oxidized glucose equivalent . To determine glucose carbon incorporation into fatty acid and glycerol , neutral lipids were extracted after glucose oxidation as described above . They were dried and hydrolyzed in 1 ml 0 . 25 N NaOH in chloroform/methanol ( 1∶1 ) for 1 h at 37°C . The solution was neutralized with 500 µl 0 . 5 N HCl in methanol . FA and glycerol were separated by adding 1 . 7 ml chloroform , 860 µl water , and 1 ml chloroform/methanol ( 2∶1 ) . Incorporation of 14C into glycerol and FA was measured by liquid scintillation counting of upper and lower phases , respectively . Specific activity was counted and used to determine the quantity of incorporated glucose equivalent . Results from metabolic measurements were normalized to total protein content of cell extracts and expressed relative to siGFP condition without insulin . DNA microarray and reverse transcription-quantitative PCR analysis were performed as previously described [47] . The first clinical study cohort was comprised of 295 women and 72 men aged 18–65 years ( mean 38 years ) , with a BMI range of 19–63 kg/m2 ( mean 35 kg/m2 ) . Thirty-eight were treated for type 2 diabetes , hypertension , and/or dyslipidemia . The remaining subjects were healthy according to self-report . They came to the laboratory in the morning after an overnight fast . A venous blood sample was obtained for the determination of fasting plasma glucose and insulin [48] , which were used to calculate HOMA-IR . Thereafter , a subcutaneous fat biopsy was obtained from the abdominal area using a needle biopsy technique [49] . In 97 women and 29 men , an intravenous insulin tolerance test was performed immediately after the biopsy in order to directly estimate insulin sensitivity [48] . In the second clinical study , 25 obese individuals were investigated before and 2 years after bariatric surgery with gastric banding . At the time of the first investigation , none had undergone a slimming attempt for at least 1 year and body weight had been stable for at least 3 mo according to self-report . The third cohort , described in detail in [50] , comprised 56 healthy women ( age 23–72 years; mean 43 years ) with a large interindividual variation in BMI ( 20–53 kg/m2; mean 33 kg/m2 ) . They were investigated in the morning after an overnight fast as for the first cohort above . DNA microarray and reverse transcription-quantitative PCR analyses were performed in 45 individuals as previously described [50] . In the fourth clinical study , 24 obese men ( mean BMI 32 . 7 , [29 . 3; 36 . 5] ) were randomly assigned to two groups and received placebo or nicotinic acid for 8 wk . Nicotinic acid was administered as Niaspan LP in progressive doses , reaching 2 , 000 mg from the fifth week to the end of the period . The study was registered in Clinical Trials NCT01083329 and EudraCT 2009-012124-85 . A needle biopsy of subcutaneous WAT was performed before and after the treatment . Adipocytes were isolated by collagenase digestion for total RNA preparation . Fat cell de novo lipogenesis gene expression was determined using reverse transcription-quantitative PCR with microfluidic qPCR device ( Biomark Dynamic Array , Fluidigm ) [51] . In mouse studies , in vitro lipolysis on isolated adipocytes was performed as previously reported [44] . In human studies , WAT was brought to the laboratory and was immediately subjected to investigation . One portion of the tissue was used for incubation in vitro exactly as described [48] . At the end of the incubation , an aliquot was removed for analysis of glycerol levels as lipolysis index . After incubation , lipids were extracted from the lWAT . In a parallel sample , isolated fat cells were prepared and mean fat cell weight determined [48] . Glycerol release was related to number of fat cells incubated by dividing total lipid weight of the sample with the mean lipid weight of a fat cell . Results were expressed as mean ± SEM . Student's t tests , nonparametric Mann Whitney and Wilcoxon tests , and repeated measures ANOVA were used for comparisons between WT and HSL+/− mice . When experiments also involved HSL−/− mice , one-way ANOVA with Welch correction test was applied . Simple and multiple linear regression analyses and ANCOVA were used to analyze clinical data . Differences were considered significant for p<0 . 05 ( * ) , p<0 . 01 ( ** ) , and p<0 . 001 ( *** ) . | In periods of energy demand , mobilization of fat stores in mammals ( i . e . , adipose tissue lipolysis ) is essential to provide energy in the form of fatty acids . In excess , however , fatty acids induce resistance to the action of insulin , which serves to regulate glucose metabolism in skeletal muscle and liver . Insulin resistance ( or low insulin sensitivity ) is believed to be a cornerstone of the complications of obesity such as type 2 diabetes and cardiovascular diseases . In this study , our clinical observation of natural variation in fat cell lipolysis in individuals reveals that a high lipolytic rate is associated with low insulin sensitivity . Furthermore , partial genetic and pharmacologic inhibition of hormone-sensitive lipase , one of the enzymes involved in the breakdown of white adipose tissue lipids , results in improvement of insulin sensitivity in mice without gain in body weight and fat mass . We undertake a series of mechanistic studies in mice and in human fat cells to show that blunted lipolytic capacity increases the synthesis of new fatty acids from glucose in fat cells , a pathway that has recently been shown by others to be a major determinant of whole body insulin sensitivity . In conclusion , partial inhibition of adipose tissue lipolysis is a plausible strategy in the treatment of obesity-related insulin resistance . | [
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... | 2013 | Partial Inhibition of Adipose Tissue Lipolysis Improves Glucose Metabolism and Insulin Sensitivity Without Alteration of Fat Mass |
Mass drug administration ( MDA ) programs have dramatically reduced lymphatic filariasis ( LF ) incidence in many areas around the globe , including American Samoa . As infection rates decline and MDA programs end , efficient and sensitive methods for detecting infections are needed to monitor for recrudescence . Molecular methods , collectively termed ‘molecular xenomonitoring , ’ can identify parasite DNA or RNA in human blood-feeding mosquitoes . We tested mosquitoes trapped throughout the inhabited islands of American Samoa to identify areas of possible continuing LF transmission after completion of MDA . Mosquitoes were collected using BG Sentinel traps from most of the villages on American Samoa's largest island , Tutuila , and all major villages on the smaller islands of Aunu'u , Ofu , Olosega , and Ta'u . Real-time PCR was used to detect Wuchereria bancrofti DNA in pools of ≤20 mosquitoes , and PoolScreen software was used to infer territory-wide prevalences of W . bancrofti DNA in the mosquitoes . Wuchereria bancrofti DNA was found in mosquitoes from 16 out of the 27 village areas sampled on Tutuila and Aunu'u islands but none of the five villages on the Manu'a islands of Ofu , Olosega , and Ta'u . The overall 95% confidence interval estimate for W . bancrofti DNA prevalence in the LF vector Ae . polynesiensis was 0 . 20–0 . 39% , and parasite DNA was also detected in pools of Culex quinquefasciatus , Aedes aegypti , and Aedes ( Finlaya ) spp . Our results suggest low but widespread prevalence of LF on Tutuila and Aunu'u where 98% of the population resides , but not Ofu , Olosega , and Ta'u islands . Molecular xenomonitoring can help identify areas of possible LF transmission , but its use in the LF elimination program in American Samoa is limited by the need for more efficient mosquito collection methods and a better understanding of the relationship between prevalence of W . bancrofti DNA in mosquitoes and infection and transmission rates in humans .
Lymphatic filariasis ( LF ) caused by the diurnally subperiodic form of the mosquito-borne parasitic nematode Wuchereria bancrofti is endemic to American Samoa , a United States territory composed of the easternmost islands of the Samoan archipelago ( Figure 1 ) . LF is also endemic in the archipelago's western islands which comprise the independent nation of Samoa [1] , [2] . In the Samoan archipelago , Aedes ( Stegomyia ) polynesiensis Marks and Aedes ( Finlaya ) samoanus ( Grünberg ) are the major vectors of LF [3] , [4] . Natural infections have also been detected in Aedes ( Stegomyia ) upolensis Marks and Aedes ( Finlaya ) tutuilae Ramalingam and Belkin , but these species are not considered to be as epidemiologically important due to their relatively low abundances in human landing catches [5] , [6] . Aedes polynesiensis is widespread in the South Pacific , inhabiting islands south of the equator from Tuvalu and Fiji eastward to the Marquesas and Pitcairn Island [7] . It breeds in a wide range of natural and artificial containers [8] , [9] , [10] and feeds primarily in the daytime [11] , [6] . Aedes polynesiensis is believed to be a weak disperser , rarely traveling as far as 92 m [12] , [11] . Aedes samoanus occurs only in American Samoa and Samoa , breeding primarily in water collecting in leaf axils of the forest climber Freycinetia reineckei in American Samoa , and in axils of F . reineckei and Pandanus spp . in Samoa [5] , [13] . Aedes samoanus females feed at night [5] , [6] . The dispersal capabilities of Ae . samoanus have not been investigated . Other mosquito species abundant in Samoa and American Samoa are Culex ( Culex ) quinquefasciatus Say , Culex ( Culex ) annulirostris Skuse , Culex ( Culex ) sitiens Wiedemann , Aedes ( Stegomyia ) aegypti ( L . ) , Aedes ( Finlaya ) oceanicus Belkin , and Aedes ( Aedimorphus ) nocturnus ( Theobald ) [7] , [14]; however , none of these species have been found to play a significant role in LF transmission in the Samoan islands [12] , [5] , [14] , [15] . During the years 2000–2010 , the American Samoa Department of Health undertook a campaign to eliminate LF through annual mass drug administration ( MDA ) using diethylcarbamazine and albendazole [16] . The campaign ran in conjunction with similar campaigns in other South Pacific countries and territories , including neighboring Samoa , under the Pacific Programme to Eliminate Lymphatic Filariasis [17] . Population coverage by MDA was 24–52% in the first three years and improved to 65–71% in the subsequent four years [16] . Infection prevalence before , during , and after MDA has been monitored primarily by an immunochromatographic ( ICT ) test , which detects circulating filarial antigen ( CFA ) released into the blood by adult W . bancrofti [18] . The testing was done across all age groups . Prevalence of CFA in a baseline survey in 1999 was 16 . 5% [19] , and subsequent testing in four sentinel villages found CFA declining from 11 . 5% in 2001 to 0 . 95% in 2006 [20] . Prevalences in an additional four villages surveyed in 2006 were higher , ranging from 2 . 1% to 4 . 6% [20] , [21] , and a territory-wide serosurvey in 2007 found 2 . 3% CFA prevalence . Additional MDA activities took place during 2007–2010 , but the level of MDA coverage during those years is unclear . Testing the human population for CFA can provide information about prevalence of W . bancrofti infection , and antibody testing can provide a sensitive indicator of levels of exposure to W . bancrofti [22] . In addition , one can sample the human population indirectly by sampling mosquito species known to feed on human blood . Molecular xenomonitoring ( MX ) , the detection of parasite DNA or RNA in mosquitoes using the polymerase chain reaction ( PCR ) , allows the testing of pools of mosquitoes and can be more efficient and more sensitive than dissections , especially when large numbers must be examined to detect evidence of W . bancrofti when prevalence is low [23] , [24] , [25] . The ability to test large numbers of mosquitoes also depends on the availability of efficient collection methods for local species . The development of the BG Sentinel trapping system has , for the first time , made trapping large numbers of Ae . polynesiensis over large geographic areas feasible in American Samoa [26] . It is important to recognize that MX cannot provide a direct measurement of ongoing transmission unless the PCR method used specifically targets the infective third stage larva ( L3 ) of W . bancrofti [27] . Instead , it provides an indirect assessment of human infection . Fischer et al . [28] and Erickson et al . [29] , studying Brugia malayi , found that parasite DNA could be detected in both vector and non-vector mosquito species long after ingestion of microfilariae , even when those microfilariae did not survive in the mosquito . Workers wishing to assess transmission directly still need to measure vector biting rates and use dissection or reverse transcriptase-PCR to specifically detect L3 in the vector mosquitoes . In 2006 , a pilot study evaluated the use of MX and traditional xenomonitoring concurrently with serological testing of humans in three villages in American Samoa . Trapped mosquitoes were examined by PCR or dissection , and village residents were tested for CFA and antifilarial antibody [21] . ( The Bm14 antibody test used is an indicator of infection or exposure and may give a positive result prior to development of patent infections [30] , [31] , [32] . ) The serological tests found 3 . 7–4 . 6% of residents of the three villages were positive for CFA and 12 . 5–14 . 9% positive for antifilarial IgG4 antibody to the recombinant Bm14 antigen [21] . Dissection of approximately half of the Ae . polynesiensis catch found infection prevalences of 0–0 . 23% , while PCR testing of the remainder gave estimates of 0 . 52–0 . 90% prevalence [25] . In summary , mosquito dissection proved relatively insensitive , while antigen and antibody testing and MX all gave similar results . All three indicated LF infections occurring at low levels in all three villages . In 2011 , a territory-wide transmission assessment survey ( TAS ) was conducted according to the World Health Organization [18] guidelines for monitoring and assessment of MDA in LF elimination programs [33] . The TAS consisted of antigen and antibody testing of 6–7 year olds in the territory's elementary schools . Overall CFA prevalence in the survey was below the threshold at which the guidelines would recommend additional MDA [33] . The TAS results provide guidance to determine whether or not to restart MDA at the territory level . But if LF infection is uneven across subpopulations or across geographic areas , then some groups or areas may require additional MDA even though aggregate LF prevalence is below a level deemed necessary to sustain the infection in the population . The limited dispersal ability of the major LF vector Ae . polynesiensis and its susceptibility to the BG Sentinel trap suggested that MX using mosquitoes trapped from throughout American Samoa may be a useful adjunct to the school-based TAS for detecting areas of possible continuing LF transmission . We here describe the results of PCR testing for W . bancrofti DNA in mosquitoes captured from villages throughout American Samoa . Results of the TAS will be described elsewhere .
The mosquito collections were conducted on the islands of Tutuila , Aunu'u , Ofu , Olosega , and Ta'u ( Figure 1 ) . These are the only islands in American Samoa that have been continuously inhabited in recent years . The five islands are located between 14° 9′ and 14° 22′S and 169° 25′ and 170° 51′W . The largest , Tutuila Island , comprises 68% of the territory's 199 km2 total land area and contains approximately 97% of its total population of 55 , 519 [34] . Aunu'u Island had 436 residents by the 2010 census [34] . Many of Aunu'u's residents commute by boat to nearby Tutuila for work or school . The more distant Ofu , Olosega , and Ta'u Islands , which together comprise the Manu'a group , had 176 , 177 , and 790 inhabitants , respectively , according to the 2010 census [34] . Much of the territory's land is forested , steep , and rugged , with about half the area having 70% or greater slope and over half covered by rainforest [35] , [36] . Human settlement is mostly along the coastlines , with the exception of the Tafuna-Leone plains and the Aoloau-Aasu uplands areas in the southwest portion of Tutuila Island . Trapping was conducted within residential areas of all major villages of the four smaller islands and 34 randomly selected villages out of the 67 on Tutuila . These randomly selected villages contained approximately 57% of Tutuila's population and 52% of its land area [34] . In some cases , 2–4 adjacent selected villages on Tutuila Island were combined and treated as single village areas for trapping and analysis . In one case , leaders in a selected village were not available to assist during the trapping time , so a nearby village was used instead . In the TAS , only two children were identified as CFA positive [33] . These children both attended a school located in a village on Tutuila that was not among those randomly selected for mosquito trapping . As a result , additional trapping was conducted in and around the school grounds using the same procedures as in the selected villages . Because the school was not located in one of the selected villages , data from these traps were not included in the larger data set but are reported separately . In each village ( or group of contiguous smaller villages ) ten BG-Sentinel traps baited with BG Lure ( Biogents AG , Regensburg , Germany ) were placed throughout the village and operated for approximately 24 or 48 h , depending on catch rate . Exceptions occurred in the combined area of Alega and Avaio villages where only six traps were placed , and Amaua village where four traps were placed . Traps were removed after 24 h if it appeared that the catch had reached a target of 200 Ae . polynesiensis females . The traps were placed on the ground in locations protected from direct sunlight and rain , often under eaves of houses or outbuildings such as unused open-sided traditional cookhouses . Placements were determined in consultation with village leaders and individual families while attempting to spread the traps evenly throughout the residential area of each village . Although village lands may be extensive , often spanning areas from the coast to the interior ridgetops , in most cases the residential areas are largely confined to lands near the coast or near major roads . Mosquitoes were removed from the traps twice per day at approximately 10:00 am and 6:30 pm following peak feeding times of the major vector Ae . polynesiensis [11] , [6] . In one village ( Vatia ) the second trap check scheduled for 10:00 am had to be postponed to 4:30 pm due to a tsunami warning and village evacuation , so the Vatia traps ran for approximately 30 . 5 h rather than 24 or 48 h . Mosquitoes collected during the first day of trapping in Taputimu and Vailoatai villages were lost , so only the second day's catch was used from these two villages . In the laboratory , the mosquitoes were anaesthetized with carbon dioxide and identified on a tray resting on an ice pack under a stereomicroscope using the taxonomic keys of Ramalingam [14] and Huang [37] . The few mosquitoes that could not be identified due to damage or that were missing substantial parts of the head , thorax , or abdomen were not included in the analysis . Female mosquitoes were placed in pools of ≤20 ( range 1–20 ) into microcentrifuge tubes separated by species , trap , location , and collection date and time . After freezing to ensure all mosquitoes were dead , the tubes were left open in an oven to dry at 75°C overnight , then closed and stored in a sealed plastic box with dessicant at 23°C until they were shipped for PCR analysis at Smith College , Massachusetts , USA . Trapping was conducted February 21–April 8 , 2011 on Tutuila and Aunu'u and June 7–16 , 2011 on the more remote Ofu , Olosega , and Ta'u islands . DNA extraction was done using a modification of the commercial DNeasy kit protocol ( Qiagen , Hilden , Germany ) and methods adapted from Fischer et al . [38] and Laney et al . [27] . Briefly , a 4 . 5 mm zinc-plated bead and 180 µl phosphate-buffered saline ( pH 7 . 2 ) were placed in each round-bottom 2-ml Eppendorf tube ( Eppendorf North America , Hauppauge , NY , USA ) containing up to 20 dried mosquitoes . The tube was capped and vortexed at high speed in a horizontal position for 15 min and again for an additional 5–10 min if necessary for complete maceration . The tube was centrifuged briefly before adding 20 µl proteinase K and 200 µl of Buffer AL . The mixture was vortexed gently for 3 sec , then incubated at 70°C for 10 min . After brief centrifugation , another 20 µl proteinase K was added and mixed with brief gentle vortexing before incubating for 1 h at 56°C . The mixture was then centrifuged at high speed , and the supernatant from each tube was added to a 1 . 5-ml Eppendorf tube containing 200 µl of 95–98% ethanol and mixed using the pipet . The entire mixture from each tube was then applied to a DNeasy kit column and centrifuged at 8 , 000 g for 1 min . The column was transferred to another 1 . 5-ml tube , and the DNA was washed twice with 500 µl of Buffer AW1 , with each wash followed by a 1 min centrifugation at 8 , 000 g . The column was then transferred to another 1 . 5 ml tube , 500 µl Buffer AW2 was added , and the tube spun at 8 , 000 g for 3 min . The waste solution was discarded , and the column spun an additional 3 min at maximum speed to dry the column . The column was then transferred to a 1 . 5-ml microfuge tube and the DNA was eluted twice with 125 µl of Buffer AE followed by 2 min centrifugation , first at 8 , 000 g , and then at 10 , 000 g . The samples were held at 4°C until the qPCR was completed , then stored at −20°C . Real-time PCR was done using a 7300 Real-Time PCR System ( Applied Biosystems , Foster City , California , USA ) . Each reaction contained 1 µl of template DNA and 24 µl of qPCR master mix including 10 µM each of forward and reverse primers and taqman probe . The primers were designed to amplify a fragment of the “long dispersed repeat” of W . bancrofti ( LDR; GenBank accession no . AY297458 ) [39] . The sequence of the primers and probe were as follows [39]: forward primer ( Wb-LDR1 ) 5′-ATTTTGATCATCTGGGAACGTTAATA-3′ , reverse primer ( Wb-LDR2 ) 5′-CGACTGTCTAATCCATTCAGAGTGA-3′ , and probe ( Wb-LDR ) 6FAM-ATCTGCCCATAGAAATAACTACGGTGGATCTCTG-TAMRA . The cycling conditions were 50°C for 2 min and 95°C for 10 min , followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min . Four different controls were used: a negative extract control consisting of a DNA extract from 20 uninfected mosquitoes; positive PCR controls using 1 ng , 100 pg , or 10 pg DNA of W . bancrofti; a negative PCR control using the same ddH2O as used in the master mix; and a PCR inhibitor control comprised of 5 pg of W . bancrofti DNA added to 10 µl of negative extract control . The negative extract and PCR inhibitor controls were run periodically throughout the course of sample processing . Positive and negative PCR controls were run with every sample batch . Samples were run in duplicate , and qPCR results with Ct≥39 were checked by running two additional qPCR reactions on the same extract template . If the sample was positive at least once more , and all controls were as expected , then the sample was considered positive . If both verification reactions were negative , then the sample was considered negative . Geographic coordinates were recorded for each trap location using a Trimble GeoXT 2005 Series Pocket PC handheld global positioning system ( GPS ) device ( Trimble Navigation Ltd . , Sunnyvale , California , USA ) . For 16 out of the 310 trap locations , the Trimble device was unable to record the positions due to topography , tree cover , weather conditions or satellite positions at the time , so a Garmin GPSmap 60CSx ( Garmin International , Inc . , Olathe , Kansas , USA ) device was used instead for those locations . The positions were mapped using ArcGIS 10 . 1 software ( Environmental Services Research Incorporated , Redlands , California , USA ) , and village boundaries were obtained from the 2010 U . S . Census Bureau's TIGER/Line “Places” shapefile for American Samoa [40] . In a few cases , traps were placed in locations which were inside the village boundaries as indicated by village leaders , but which fell outside the boundaries on the Census Bureau map . Point estimates and 95% confidence intervals for the percentage of mosquitoes containing W . bancrofti DNA were calculated for each mosquito species for the overall sample and for the most abundant species , Ae . polynesiensis , within each of the villages . The program PoolScreen ( version 2 . 0 . 3 ) was used to calculate maximum likelihood point estimates of prevalence , and confidence intervals were determined by the likelihood ratio method [41] .
A total of 22 , 014 female mosquitoes were collected and sorted into 2 , 629 pools of ≤20 individuals each for PCR testing . PCR results for the most abundant species in the traps are shown in Table 1 , and relative abundances of the three most numerous species having >1 positive pool are shown in Figure 2 . Members of the Aedes ( Finlaya ) group of species occurring in American Samoa include Ae . oceanicus , Ae . samoanus , and Ae . tutuilae . They were difficult to distinguish due to their morphological similarity and the loss of scales in the traps , so were combined for PCR testing and analysis . Only one out of the 267 pools of Ae . ( Finlaya ) spp . was positive by PCR . Other species captured in lower numbers were Ae . nocturnus , Cx . annulirostris , and Cx sitiens . Wuchereria bancrofti DNA was not detected in these species ( n = 68 pools ) . Aedes polynesiensis , Cx . quinquefasciatus , Ae . aegypti , and Ae . ( Finlaya ) group species all produced positive pools ( Table 1 ) . Estimated prevalence was highest in Ae . aegypti , although the 95% confidence interval for prevalence in this species overlapped with that for Ae . polynesiensis ( Table 1 ) . There were no positive pools of any species collected from the five major villages of the Manu'a Islands of Ofu , Olosega , and Ta'u . For Ae . polynesiensis , the most abundant species captured there , the upper limit for the one-sided 95% confidence interval estimate of prevalence across all three Manu'a Islands was 0 . 066% ( n = 212 pools ) . On Tutuila and Aunu'u islands , 38 out of 260 total trap placements produced at least one positive pool . Positive mosquitoes were detected in the majority ( 16 out of 27 ) of the village areas sampled on these two islands . Areas producing positive mosquitoes on Tutuila Island were widely distributed throughout the island ( Figure 3 ) . Aedes polynesiensis was by far the most abundant mosquito species trapped overall , and prevalence estimates for Ae . polynesiensis from the villages are depicted in Figure 4 . There was no evidence of a positive relationship between prevalence estimate and number of Ae . polynesiensis females or mean pool size ( Figure 5 ) , suggesting that the number of mosquitoes collected affected the breadth of confidence intervals as evident in Figure 4 , but not prevalence point estimates . Nine traps which produced no positive pools of Ae . polynesiensis did produce positive pools of Cx . quinquefasciatus ( 5 traps ) , Ae . aegypti ( 6 traps ) , or Ae . ( Finlaya ) spp . ( 1 trap ) . At the village level , two villages with no positive Ae . polynesiensis catches had positive Cx . quinquefasciatus ( Onenoa and Vailoatai ) or Ae . aegypti ( Vailoatai ) pools . Of the ten traps placed in and around the grounds of the elementary school attended by two children who tested positive for CFA in the TAS , five of the traps produced positive mosquito pools . Two of these traps had positive Ae . polynesiensis , two had positive Ae . aegypti , and one trap had both positive Ae . polynesiensis and positive Ae . aegypti . Prevalence estimates were 2 . 8% with a 95% confidence interval of ( 0 . 55–8 . 0% ) ( n = 107 females ) for Ae . polynesiensis and 8 . 6% with a 95% confidence interval of ( 2 . 2–20 . 8% ) ( n = 55 females ) for Ae . aegypti . Pools of the 84 Cx . quinquefasciatus and four Ae . ( Finlaya ) spp . females collected around the school were all negative .
Molecular xenomonitoring of mosquitoes trapped from villages throughout American Samoa found evidence of low but widespread occurrence of W . bancrofti infections on Tutuila and Aunu'u islands which together are home to 98% of the territory's population . The study did not find evidence of infections on Ofu , Olosega , and Ta'u islands . The ability to detect very low W . bancrofti prevalences was limited , however , due to the low numbers of mosquitoes collected in many of the villages . This lack of sensitivity was reflected in the wide confidence intervals on prevalence estimates for many of the villages ( Figure 4 ) . Mosquito collection efforts and the number of pools that could be tested were limited by the resources available for the project . The type of mosquito collection method used may also have affected the sensitivity of xenomonitoring [42] . Female mosquitoes can contain W . bancrofti DNA only after they have completed at least one blood meal . The BG Sentinel traps used in this study are designed to capture host-seeking females , many of which may be nullipars seeking their first blood meal . Collections with gravid traps targeting ovipositing females [43] , [44] can help ensure that a larger portion of the mosquitoes captured will have had at least one blood meal , but currently available gravid traps catch few Ae . polynesiensis ( MAS unpublished data ) . For endophagic species , collection of resting mosquitoes in houses can also produce larger proportions of previously blood-fed females [45] , [24] . Gravid traps and collection of resting mosquitoes in houses have been effective for Cx . quinquefasciatus xenomonitoring in areas where that species is the major LF vector . Culex quinquefasciatus does not appear to be an important LF vector in the Samoan islands [15] , but it was the second most abundant species in our BG Sentinel traps and an estimated 0 . 11% contained W . bancrofti DNA . In villages where this species is abundant ( Figure 2 ) , use of gravid traps targeting Cx . quinquefasciatus in place of , or in addition to , BG Sentinel traps targeting Ae . polynesiensis might improve xenomonitoring efficiency by increasing both the capture rate and the proportion of the catch consisting of previously blood-fed individuals . This approach remains to be tested in American Samoa . The large proportion of traps which produced positive mosquitoes in the area of the school at which two children tested positive for CFA indicated possible ongoing transmission there . Examination of blood smears and PCR testing following the ICT failed to find evidence of microfilaremia in either child [33] , suggesting they may not have been the sources of the W . bancrofti detected in the trapped mosquitoes . The two children came from different villages , and each lived approximately 1 km from the school . Because Ae . polynesiensis feeding times overlap with times when students are at school and at home [11] , [6] , transmission by this vector could occur in either setting . According to the 2010 census [46] , approximately 21 , 196 of American Samoa's population attended school ( pre-kindergarten – college ) and 12 , 070 of the territory's 16 , 482 working population traveled more than 15 min from home to work . The mobility of the human population and the daytime feeding habits of Ae . polynesiensis suggest that W . bancrofti transmission likely occurs not only in residential areas of villages , but also at other locations , such as workplaces , bus stops , and schools . With the exception of the single school , this study did not sample these other potentially important locations . There were several similarities between the results of this study and the only other study to use MX in American Samoa [25] . Only one of the three villages sampled by Chambers et al . [25] was sampled again in the current study . Prevalence of W . bancrofti DNA in Ae . polynesiensis for Afao Village was estimated to be 0 . 82% in the 2006 study and 0 . 47% in the current one . The wide confidence intervals obtained in the two studies ( Figure 4 here and Figure 4 of Chambers et al . [25] ) indicate a much larger sample size would be required to evaluate the significance of a difference of this magnitude . The estimates for prevalence of W . bancrofti DNA in Ae . aegypti were higher than those for Ae . polynesiensis both in this study and in the 2006 study , although the 95% confidence intervals for the two species overlapped broadly in both cases . The high propensity of Ae . aegypti for feeding on human hosts is well documented ( e . g . , [47] , [48] ) and could result in a higher frequency of feeding on microfilaraemic individuals than would be the case for mosquito species with less affinity for humans . Aedes polynesiensis is known to feed on birds and mammals other than humans , but little is known about the frequency with which it feeds on the different hosts [11] , [49] , [5] . No W . bancrofti DNA was detected in the 262 Ae . upolensis collected from throughout the territory in the current study . A similar number of Ae . upolensis collected from three villages in the earlier study by Chambers et al . [25] produced one positive pool . The low incidence of W . bancrofti DNA in this species and the low numbers collected in villages support the suggestion that it is likely a minor vector of LF in American Samoa [14] . Positive PCR results for species not considered to be important LF vectors revealed evidence of W . bancrofti in some locations where results from Ae . polynesiensis collections did not . Only two of the six traps with positive pools of Ae . aegypti and only one of the five traps with positive Cx . quinquefasciatus also produced positive Ae . polynesiensis . At the village level , two villages ( Onenoa and Vailoatai ) produced positive Ae . aegypti or Cx . quinquefasciatus pools from multiple traps , but no positive Ae . polynesiensis pools . The discrepancies are likely due to behavioral differences and variation in relative abundance of the three species across trapping sites . Together they suggest that sampling multiple species—including non-vectors—with different feeding behaviors may provide a more complete assessment of W . bancrofti infections than sampling only a single important vector species . The three species exhibit important differences in feeding behavior [50] , [7] , [5] . Aedes aegypti , like Ae . polynesiensis , feeds primarily during the day , but is more endophilic than Ae . polynesiensis . Culex quinquefasciatus feeds mainly at night and feeds and rests both inside and outside houses . Differences in range of movement could also result in different exposures to W . bancrofti . Aedes aegypti and Ae . polynesiensis are believed to have limited dispersal ability [12] , [11] , [51] , but Cx . quinquefasciatus may move longer distances [52] , [53] , [54] , [55] . Finally , if multiple species are included in xenomonitoring , the reduced sensitivity resulting from a low catch rate for Ae . polynesiensis in some villages , as occurred in Vailoatai , might be partially compensated for by higher catches of other species ( Figure 2 ) . Xenomonitoring using multiple species , including non-vectors , is a departure from the approach of monitoring only a single vector species and comparing estimated prevalence in that species to model-based or empirical thresholds to assess progress in LF elimination programs [24] , [42] . The latter approach is complicated in the Samoan islands due to the presence of an important secondary vector , Ae . samoanus , the lack of an effective trap for that species , and the difficulty in distinguishing it morphologically from a closely related non-vector species . Another complication is the spatial heterogeneity of LF prevalence and transmission [56] , [57] which suggests that even when aggregate prevalence in mosquitoes captured over a large area may fall below a target threshold , some local prevalences may exceed it . In addition , earlier xenomonitoring efforts have revealed that W . bancrofti prevalence in Ae . polynesiensis collected at a single location can vary substantially over the course of a year or even between collection periods separated by as few as ten days [58] , [25] . Together , these factors , along with the difficulty of collecting large numbers of vectors and the resulting wide confidence interval estimates , suggest that xenomonitoring currently has limited usefulness for quantifying the progress of LF elimination in American Samoa . Instead its operational value may lie in helping to map areas where human infections exist without the invasiveness of human blood collection . Even such presence-absence mapping , however , requires trapping sufficient mosquitoes at each location to provide a high probability of detecting positive mosquitoes in the locations where they occur—something that may be difficult to achieve in areas where prevalence and catch rates are low . In summary , the detection of W . bancrofti DNA in mosquitoes at many locations on Tutuila and Aunu'u islands suggests widespread occurrence of human infections on these islands , while the low overall prevalence estimate suggests a similarly low overall prevalence of human infections . But caution is required in making inferences about prevalence at more local levels due to small sample sizes in many villages . Currently xenomonitoring has little value for programmatic decision-making in American Samoa beyond its ability to identify areas where human infections may exist . Increasing its relevance to MDA decision-making will require additional research to develop more efficient mosquito collection methods and to improve understanding of the relationship between prevalence of W . bancrofti DNA in mosquitoes , infection rates in humans , and resulting transmission rates relative to critical thresholds . | Lymphatic filariasis ( LF ) , a mosquito-borne parasitic disease , has been targeted for elimination in many countries since the introduction of mass drug administration ( MDA ) programs using two-drug combinations along with improved diagnostic methods . Sensitive molecular methods detecting parasite DNA in pools of mosquitoes , along with efficient mosquito collection methods , can help identify sites of continuing LF transmission that may require further treatment after MDA has eliminated transmission in most areas . We tested mosquitoes from villages throughout American Samoa after the conclusion of a series of annual MDAs . Widespread but low prevalence of parasite DNA in mosquitoes from two of the five islands suggested continued occurrence of LF . In this study , parasite DNA detection in mosquitoes helped identify areas where human infections exist and additional treatment may be needed . In the future , development of more efficient mosquito collection methods for local species would facilitate larger sample sizes and more precise estimates of prevalence . In addition , developing a better understanding of the epidemiological significance of parasite DNA prevalence in the local mosquitoes will increase the operational value of those estimates for LF elimination programs . | [
"Abstract",
"Introduction",
"Methods",
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"infectious",
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"insect"... | 2014 | Molecular Xenomonitoring Using Mosquitoes to Map Lymphatic Filariasis after Mass Drug Administration in American Samoa |
Dogs are the main source of human cystic echinococcosis . An oral vaccine would be an important contribution to control programs in endemic countries . We conducted two parallel experimental trials in Morocco and Tunisia of a new oral vaccine candidate against Echinococcus granulosus in 28 dogs . The vaccine was prepared using two recombinant proteins from adult worms , a tropomyosin ( EgTrp ) and a fibrillar protein similar to paramyosin ( EgA31 ) , cloned and expressed in a live attenuated strain of Salmonella enterica serovar typhimurium . In each country , five dogs were vaccinated with the associated EgA31 and EgTrp; three dogs received only the vector Salmonella; and six dogs were used as different controls . The vaccinated dogs received two oral doses of the vaccine 21 d apart , and were challenged 20 d later with 75 , 000 living protoscoleces . The controls were challenged under the same conditions . All dogs were sacrificed 26–29 d postchallenge , before the appearance of eggs , for safety reasons . We studied the histological responses to both the vaccine and control at the level of the duodenum , the natural localization of the cestode . Here we show a significant decrease of parasite burden in vaccinated dogs ( 70% to 80% ) and a slower development rate in all remaining worms . The Salmonella vaccine EgA31-EgTrp demonstrated a high efficacy against E . granulosus promoting its potential role in reducing transmission to humans and animals .
Cystic echinococcosis , also called hydatidosis , represents a severe public health and livestock problem , particularly in developing countries [1]–[3] . The causative agent is the cestode Echinococcus granulosus . The adult stage may be found in the small intestine of canine carnivores . Growth of the larval stage throughout the internal organs , especially the liver and lungs , causes clinical signs in the intermediate hosts , such as sheep , cattle , and camels . Humans may also become host to this metacestode . Usually , however , intermediate hosts become infected by grazing on vegetation contaminated by eggs shed by adult worms via canine feces [4] . In various endemic areas , prevention and control programs have been established [5] . These programs usually involve the repeat treatment of dogs with praziquantel alongside the establishment of health education programs . However , such programs represent a significant financial burden for developing countries . A vaccine against hydatidosis ( the disease caused by the larval stage of the parasite ) , or echinococcosis ( the disease caused by the adult stage ) , is thus highly desirable in order to provide long-term prevention of the disease and to complement control programs . An effective vaccine against ovine hydatidosis , based on a recombinant protein from parasite oncospheres ( first larval stage of the parasite ) , has been developed that targets the larval stage of the parasite [6] . If used in the field , this vaccine would need to be administered to all animals in a herd , which may be very costly to control programs . In contrast , a vaccine directed at protecting dogs against the adult worm would have to be given to only a few animals to protect the environment , because dogs are less numerous than other animals in the herd . Mathematical modeling has recently confirmed the possible utility of this strategy [7] , and several authors have demonstrated its feasibility by showing that dogs can develop protective immunity against E . granulosus [8] , [9] . Based on these reports , subcutaneously injected anti-echinococcosis vaccines have been prepared from soluble native protoscolex ( scoleces situated in brood capsules ) proteins as well as from recombinant proteins normally expressed by mature adult worms [10] . This vaccine aims at decreasing worm growth and egg development . Here we describe a vaccine that uses a live attenuated Salmonella mutant strain as a vector to deliver two recombinant proteins expressed by the adult stage of E . granulosus: tropomyosin ( EgTrp ) and ( EgA31 ) , which share some sequence elements with paramyosins . The vaccine is intended to protect domestic and stray dogs as well as wild canids from infection with E . granulosus . We studied the histological and immunological responses to both the vaccine and to a challenge at the level of the upper duodenum in dogs .
EgA31 and EgTrp are recombinant proteins . As described by Saboulard et al . [11] , a PstI/DraI cDNA fragment of the EgA31 cDNA ( GenBank [http://www . ncbi . nlm . nih . gov/] accession no . AF067807 ) was cloned into the PQE80L expression vector ( Qiagen ) . Plasmid pQE11-EgTrp encoding the C terminus of E . granulosus antigen EgTrp and plasmid pTECH2 1994 have been described elsewhere [12] , [13] . S . enterica serovar typhimurium ( S . typhimurium ) aroC strain LVR01 suitable for oral immunization of dogs is described elsewhere [14] . An immunogenic fragment encoding aa 168–246 [11] from EgA31 was amplified by PCR from pQE80[egA31] using the primers EgA3 ( forward primer: 5′-CATGGATCCGCTGAAAAACAAGCCATGGAT-3′ ) , and EgA4 ( reverse primer: 5′-ATGAAGCTTAATTTCAGCTTTCTGCTC-3′ ) . Forward and reverse primers contained BamHI and SpeI restriction sites ( underlined ) to facilitate directional cloning into pTECH as previously described [14] . The EgTrp ( GenBank accession no . AAB65799 ) was amplified by PCR from pQE11-EgTrp using primers TrpF ( forward primer: 5′-CGCGGATCCGAAACATCTACTAAGCTTGACG-3′ ) , and TrpR ( reverse primer: 5′-CCCAAGCTTTCAGAAGGAAGTGAGCTCCGCG-3′ ) . Forward and reverse primers contained BamHI and SpeI restriction sites ( underlined ) to facilitate directional cloning into pTECH . Each of the PCR products was ligated into the pTECH plasmid , which had been previously digested with the same enzymes , and the ligation product was transformed into E . coli strain TG2 . Transformant colonies were evaluated by DNA restriction analysis of the plasmid . Expression of the TetC fusions was tested by Western blotting on lysates of bacteria harboring the construct , using anti-TetC serum and either anti-EgA31 or anti-EgTrp sera as probes , as previously described [15] . The constructs were then transferred to Salmonella LVR01 and tested again for expression of the fusion protein . All work with dogs was conducted following international guidelines on the use of animals for experimentation ( recommendation of the European Commission No L 358 , ISSN 0378-6978 ) . Dogs of common breeds , between 1 and 6 mo of age , were purchased locally in Tunisia and Morocco and kept in approved facilities for 2 mo before use . A total of 28 dogs were used in this study , 14 in each country . Dogs were divided into four groups , with the number , sex , and age detailed in Table 1 . Group 1: Ten animals . All were vaccinated with EgA31 and EgTrp , before being challenged with protoscoleces . Group 2: Six animals . All received the vector Salmonella not expressing any E . granulosus antigen , before being challenged with protoscoleces . Group 3: 12 animals . All were controls: Five dogs received a mock vaccination with 0 . 1 mM PBS before being infected with protoscoleces; five dogs were only infected with protoscoleces; and two dogs were the noninfected ( negative ) controls . For oral immunization , dogs were starved 12 h before being allowed to ingest 5×1010 recombinant bacteria in 2 ml of PBS , or PBS alone as previously described [15] . Animals received two doses 21 d apart . Bacterial cultures were prepared just before each vaccination dose . Weekly blood samples were taken after immunization , and sera were stored at −20°C until testing . Twenty days after the last dose of Salmonella , all animals were orally challenged with 7 . 5×104 live protoscoleces obtained from liver cysts recovered from sheep . The viability of protoscoleces was verified before challenge . Dogs were euthanized by intravenous injection of pentobarbital 26–29 d post-challenge . Immediately following euthanasia , full-thickness sections of the experimental and control dogs' proximal duodenum ( always within 10–15 cm from the pylorus ) were collected for immunostaining and histological examination . Worms were recovered by scraping the intestinal mucosa followed by several washings with 0 . 9 N NaCl solution and a series of sedimentation steps . Tissues were fixed in 10% neutral-buffered formalin , embedded in paraffin wax , sectioned at 6 µm , and either stained with haematoxylin for routine histological evaluation or transferred onto poly-l-lysine–pretreated slides for immunohistochemical studies . To identify T cells and plasma cells in sections , we used a panel of primary antibodies to: CD3 , lambda ( λ ) , kappa ( κ ) , IgA , IgM , as well as a standard avidin-biotin ABC immunoperoxidase ( Autoprobe II Biomeda ) . Briefly , fixed sections were passed through graded alcohol to PBS ( 0 . 01 M [pH 7 . 2] ) , then lightly digested in stabilized enzyme mixture ( Auto/Zyme Reagent Set; Biomeda ) for 10 min at 37°C to break the disulphide bridges and enhance antigen retrieval . After one wash in PBS , sections were heated in 10 mM citrate buffer ( pH 6 . 0 ) for 40 min at 90°C in a double boiler . Endogenous peroxidase activity was blocked by incubation with hydrogen peroxide ( 3% v/v ) in PBS for 10 min , and slides were then incubated for 10 min with a blocking solution ( tissue conditioner , Biomeda ) to reduce nonspecific background activity . Sections were incubated with primary antibody for 1 h and sequentially incubated with biotinylated secondary antibody ( Autoprobe II , Biomeda ) for 30 min . Prior to use , the secondary antibody was incubated for 30 min with 10% ( v/v ) dog serum . Slides were then incubated with streptavidin-biotin horseradish peroxidase complex ( Autoprobe II , Biomeda ) for 30 min . All incubations were performed at room temperature . We used PBS to wash sections three times between each incubation step , to perform all dilutions , and to replace primary antibodies for control purposes . Binding of the streptavidin-biotin conjugate was visualized by addition of 3 , 3′-diaminobenzidine terahydrochloride and hydrogen peroxide ( Autoprobe II , Biomeda ) ; sections were counterstained with haematoxylin . To ensure homogeneity of the analyses , all sections were coded and analyzed in a blinded fashion by the same investigator . Samples were examined with an Olympus BX 50 microscope . For each section , we counted positively stained cells in the lamina propria of villi . Ten areas of the duodenal mucosa including one or more villi ( except those just above Peyer's patches ) , were chosen at 20× objective , digitized with a Nikon Coolpix 4500 digital camera , and then transferred to a computer by means of Nikon software ( Eclipse net ) . Lamina propria from each villus , from base to tip , was delineated on the computer screen ( excluding epithelium and large vessels ) , and positively stained cells within each region were manually counted . Fragments ( less than 1 mm thick ) of the different intestinal segments were fixed in 2% glutaraldehyde–sodium cacodylate/HCl 0 . 1 M ( pH 7 . 4 ) for 2 h at 4°C . After washing in socium cacodylate/HCl 0 . 2 M ( pH 7 . 4 ) , the samples were post-fixed in 1% OsO4–sodium cacodylate/HCl 0 . 15 M ( pH 7 . 4 ) for 1 h at 4°C , and dehydrated in graded ( from 30% to 100% ) ethanol . For transmission electron microscopy , samples were impregnated in Epon and flat-embedded in order to define the orientation of the tissue sample ( epithelio-connective interface ) for sectioning . After polymerization at 60°C for 3 d , thin sections were obtained on an RMC MTX ultramicrotome: semi-thin sections ( 1 µm thick ) deposited on glass slides were stained according to Richardson et al . [16] with methylene blue and Azur II; and ultra-thin sections ( 60–80 nm ) adhered to copper grids were contrasted according to Reynolds [17] with uranyl acetate and lead citrate . Sections were observed using a Jeol 1200 CX transmission electron microscope equipped with numerical camera SIS Megaview II . Iconography was treated with AnalySIS software . 96-well micro plates ( Nunc-Maxisorb ) were coated with 1 µg/ml of each recombinant protein in 30 mM carbonate-bicarbonate buffer ( pH 9 . 6 ) . After overnight incubation at 4°C , the plates were washed with 0 . 05% Tween 20 in PBS ( PBS-Tween ) , and blocked with PBS-Tween containing 1% bovine serum albumin for 2 h at 37°C . Pools of dog serum made up in PBS-Tween were added to triplicate wells and the plates incubated at 37°C for 2 h . After three washes with PBS-Tween , 100 µl/well of each of the diluted specific antibodies ( 1∶100 ) , the monoclonal goat anti-dog IgG ( Sigma-Aldrich ) , IgA , and IgE ( Interchim ) were added . After incubation for 2 h at 37°C , the plates were washed , and 100 µl/well of peroxidase-labeled rabbit anti-goat IgG ( Sigma-Aldrich ) diluted 1∶5 , 000 in PBS-Tween was added . After incubation ( 1 h at 37°C ) , the plates were washed again , and the enzyme reaction developed with the substrate 3 , 3′ , 5 , 5′-tetramethylbenzene-dihydrochloride ( Sigma-Aldrich ) . Optical densities were read at 450 nm with an ELISA plate reader ( Microplate Reader , Bio-Rad Laboratories ) . Sera from the control dogs ( nonvaccinated and noninfected ) were pooled and used as a negative control to measure the background activity in all experiments . INSTAT software ( GraphPad ) was used for the statistical studies . One-way analysis of variance ( ANOVA ) , and the Dunnett multiple comparisons post-test were employed . Statistical differences with P<0 . 05 were considered significant .
We counted the number of worms in all groups of animals . Total and average numbers are presented in Table 2 . Results of the trial in Morocco showed a 79% reduction in the number of cestodes in vaccinated dogs compared with nonvaccinated , noninfected controls . Results did , however , vary among animals: one dog ( number 4 ) from the vaccinated group had the largest worm load ( unpublished data ) , suggesting that the vaccine did not exert any protective effect in this animal . Similar to those seen in Morocco , the results from the trial carried out in Tunisia showed a 74% reduction in the number of worms in vaccinated compared with control dogs ( Table 2 ) . We measured the sizes of 50 randomly chosen worms per experimental group and recorded the percentage of developed ( ≥5 mm ) versus underdeveloped ( <5 mm ) worms . In the vaccinated dogs , 40% of the worms were small , whereas in infected control dogs , small worms represented only 15% of the total population . We observed a similar distribution in the Tunisian experiments . It is worth noting here that a decrease in the number of parasites and a delay in their growth rate are considered to be two criteria for the effectiveness of a vaccine against the adult worm . Haematoxylin-stained sections were examined prior to immunostaining . Table 3 shows the number of cells/mm2 that stained positively with the antibodies studied in each group of dogs . For each antibody , we assessed a mean area of 0 . 58 mm2 . The results from the vaccinated group exclude dog 4 , which had the large parasite load . We observed CD3 labeling characterized by a strong staining of the cytoplasm of positive cells with reinforcement just beneath the cell membrane . CD3+ cells had small lymphocyte morphology . These cells were distributed predominantly in the upper half of the villus with decreasing density towards the base ( Figure 1C and 1D ) In the lamina propria , the κ- , λ- , IgA- , and IgM-positive cells exhibited typical plasma cell morphology . The markers strongly and uniformly stained the cytoplasm of cells . We found considerably more λ-positive plasma cells than -positive cells ( ratio λ/κ of 5 ) , and IgA-containing plasma cells predominated with fewer IgM-positive cells ( ratio IgA/IgM of 6 . 6 ) . We observed the majority of IgA-containing cells located in the lower half of the villi ( Figure 1A and 1B ) and IgM-containing cells at the base of the villi . Electron microscope ( Figure 1E and 1F ) and immunostaining results confirmed that immunized dogs mounted a strong inflammatory response compared to infected controls upon challenge . The interstitium was invaded by an immunocompetent cell infiltrate with numerous plasma cells and lymphocytes . We also identified many activated lymphocytes , characterized by their nucleus-cytoplasm ratio ( <1 ) , in Peyer's patches . The quality of the immune response , and the pattern of the antibody subclass ( IgG , IgA , IgE ) were determined by ELISA . The IgA and IgE responses induced by the recombinant proteins were very low in all groups , and we observed no differences between the vaccinated group and controls .
To our knowledge , this is the first report of protection induced in dogs against the adult stage of E . granulosus after oral vaccination with a recombinant parasite protein . We have demonstrated the efficacy of the vaccine in two separate trials , each in a different country . Vaccination caused a decrease of more than 70% in the number of cestodes , and of a slower development rate of those recovered from vaccinated animals . Suppression of cestode development has also been observed recently [38] following the injection of a vaccine composed of recombinant EgM proteins . Out of consideration of the risk of infection to technical staff working with the patent infection in the dogs , we chose not to estimate the number of eggs in gravid segments , notwithstanding the opportunity offered by the strong expression of EgA31 in eggs and its potential usefulness as a marker [11] . We have demonstrated that a field vaccine based on this technology could be formulated into baits to target domestic and stray dogs in endemic countries . Hydatid disease remains an important risk to human health and has a large economic impact . Several vaccine trials have thus been carried out in dogs , mainly using protoscolex membranes [8] , [9] or excretory–secretory antigens from adult cestodes [18] , [19] . These studies have demonstrated the ability of dogs to develop protective immunity against the development of adult E . granulosus . Recombinant proteins derived from a developmentally regulated gene family were recently used as a vaccine to reduce numbers of the adult cestode in dogs [10] . Among antigens evaluated as potential vaccines in sheep , conformational epitopes of recombinant Eg95 elicited the most protective immune response against the metacestode [19] , [20] . Other proteins can induce a protective effect against helminthiases , including paramyosins [21]–[24] and tropomyosins [25] , [26] . Analogs of those proteins are also present in E . granulosus . EgA31 , a fibrillar protein presenting some properties of paramyosin , and tropomyosin ( EgTrp ) are both expressed in the metacestode and adult stages of E . granulosus [12] , [27] . EgA31 is also strongly expressed in the immature strobilar stage of the parasite at the level of the tegument , parenchymal cells , and immature eggs [10] , inducing an active immune response after injection , during the infection of dogs by E . . granulosus [10] , [28] . Both EgA31 and EgTrp therefore seem to be promising antigens in the development of immunity against E . granulosus in dogs . In the present study , these two recombinant proteins were expressed in a mutant of S . typhimurium that had been made avirulent in the dog , by precisely knocking out genes encoding enzymes in the prechorismate metabolic pathway [15] . This vaccine carrier strain has previously been used to express heterologous antigens and for effective oral immunization of dogs [15] . In 2004 , Moreno al . demonstrated that the S . typhimurium carrier induces an immune response in dogs [29] . Studies in calves have shown that attenuated vaccines based on S . typhimurium or S . dublin calf isolates are capable of eliciting humoral and cellular immune responses , locally and at the systemic level , to Salmonella and heterologous antigens [30]–[33] . Using the plasmid pTECH , which has proven high stability as part of a Salmonella delivery system [13] , [34] , we expressed a short sequence of EgA31 , including the most active epitopes [10] , with the fusion partner TetC . Such Salmonella strains can confer partial protection against the pathogen from which the heterologous antigen was derived . In concordance with these previous findings , the dogs in the present study that received only the vector not expressing the antigen ( group 2 ) showed a decrease in the number of worms compared to control . Few studies have been published on the immuno-phenotypical characteristics of lymphoid cell populations in the normal canine gut . In accordance with previous studies on the topography of T cells [35] , [36] , we observed a greater number of CD3+ T cells at the top of the villi , whether the animals were infected or not . We chose not to compare the density of CD3+ T cells obtained in the present study with that previously reported , as these earlier studies focused on parasite-free animals [37]–[39] . Our immunostaining studies indicate a decrease of more than 50% in the number of T lymphocytes in the vaccinated dogs compared to all infected control groups , with the exception of dog number 4 , which showed a different response to the vaccine . Only one other study has reported a λ/κ ratio in the dog of around 10 in the lymph nodes [40] . In human lymphoid tissue , the λ/κ ratio is around 0 . 5 . In our dog experiments the ratio is around 5 in the intestinal mucosa , and higher in the vaccinated dogs ( 5 . 6 ) compared to the infected control groups ( 4 . 9 ) . Since the number of κ-positive cells is similar in all groups , the high λ/κ ratio observed in the vaccinated group could only be due to an increase in the number of λ-positive cells . In the present study we detected no IgG or IgE antibodies . German et al . [35] observed a very small number ( less than 15/mm2 ) of IgG-positive plasma cells in the lamina propria of the jejunum , and identified IgE positive cells as mast cells . . In agreement with previous studies [35] , [41] , we observed a decrease in the number of plasma cells from the base to the top of the villi and identified the main secreted immunoglobulins as IgA . Our results indicate that the ratio of IgA to IgM is 6 . 9 , whereas German et al . reported it as 9 . 7 . The vaccinated dogs in our experiments showed a large increase in IgA-positive cells compared with dogs receiving only Salmonella ( about 40% ) , though the density was similar to that in control dogs ( about 5% ) . In light of our results we can verify a clear imbalance in the ratio of B to T lymphocytes in vaccinated dogs with a large decrease in the number of T cells and a small increase in the number of B cells , corresponding to IgA λ type–secreting plasma cells . German et al . [35] hypothesized that T lymphocytes at the top of villi are of the Th1 lineage , whereas those at the base belong to the Th2 lineage . The decreased number of T lymphocytes at the top of the villi that we observed may explain the local humoral orientation of the immune response . In contrast to the local humoral immune response , the ELISA results showed that the dogs developed no significant systemic humoral immune response against the EgA31-EgTrp vaccine . The low levels of IgG , IgA , and IgE detected in the serum showed no difference from levels in the negative control . However , the effect of the vaccine on the growth rate of the worms is clearly important at the intestinal level . This contradiction between the local and systemic immune responses after infection with E . granulosus has been studied and described by other groups [29] , [42] . In conclusion , in all but one of our experimental dogs , the Salmonella vaccine EgA31-EgTrp showed efficacy against infection with and growth of E . granulosus . The vaccine has a local effect , leading to a decrease in the developing adult worm burden . Despite the fact that one dog apparently did not respond to the vaccine , we would suggest further development and testing in field trials . To decrease the number of worms remaining in vaccinated dogs , it could be interesting to integrate other recombinant proteins , such as those used by Zhang et al . [10] , into our oral vaccine . Livestock vaccination reduces the infection pressure in the environment by decreasing the number of fertile larvae in intermediate hosts; the addition of an oral vaccine targeted at the adult stage of E . granulosus in dogs could help increase the overall efficacy of control programs in endemic countries . | In many countries in the world , livestock and humans are affected with hydatid disease , which is caused by the development , in the viscera , of the larval stage of the cestode Echinococcus granulosus . They become infected by ingesting the eggs of this parasite , which are passed in the feces of the dog—the host of the adult worm . Domestic dogs are key in the transmission to livestock and humans . This disease remains a major economic and public health problem in affected countries . Because dogs are quickly reinfected , control programs in these locations include monthly anthelmintic deworming . These control measures , however , are burdensome for the owner , so they often fail . In contrast , vaccination can take place in control programs at different stages of the parasite life cycle . For example , currently an effective recombinant vaccine for sheep has been developed that should work indirectly to reduce infection in dogs , which tend to eat sheep offal . However , we propose that a recombinant oral vaccine given to the small number of dogs keeping the herd would decrease the number of Echinococcus granulosus adult worms and , consequently , the number of infective eggs . This measure would help reduce the contamination risk factors for humans and livestock , and would be cost-effective for the owners of the dogs . | [
"Abstract",
"Introduction",
"Materials",
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] | [
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] | 2008 | An Oral Recombinant Vaccine in Dogs against Echinococcus granulosus, the Causative Agent of Human Hydatid Disease: A Pilot Study |
Dengue is one of the most aggressively expanding mosquito-transmitted viruses . The human burden approaches 400 million infections annually . Complex transmission dynamics pose challenges for predicting location , timing , and magnitude of risk; thus , models are needed to guide prevention strategies and policy development locally and globally . Weather regulates transmission-potential via its effects on vector dynamics . An important gap in understanding risk and roadblock in model development is an empirical perspective clarifying how weather impacts transmission in diverse ecological settings . We sought to determine if location , timing , and potential-intensity of transmission are systematically defined by weather . We developed a high-resolution empirical profile of the local weather-disease connection across Peru , a country with considerable ecological diversity . Applying 2-dimensional weather-space that pairs temperature versus humidity , we mapped local transmission-potential in weather-space by week during 1994-2012 . A binary classification-tree was developed to test whether weather data could classify 1828 Peruvian districts as positive/negative for transmission and into ranks of transmission-potential with respect to observed disease . We show that transmission-potential is regulated by temperature-humidity coupling , enabling epidemics in a limited area of weather-space . Duration within a specific temperature range defines transmission-potential that is amplified exponentially in higher humidity . Dengue-positive districts were identified by mean temperature >22°C for 7+ weeks and minimum temperature >14°C for 33+ weeks annually with 95% sensitivity and specificity . In elevated-risk locations , seasonal peak-incidence occurred when mean temperature was 26-29°C , coincident with humidity at its local maximum; highest incidence when humidity >80% . We profile transmission-potential in weather-space for temperature-humidity ranging 0-38°C and 5-100% at 1°C x 2% resolution . Local duration in limited areas of temperature-humidity weather-space identifies potential locations , timing , and magnitude of transmission . The weather-space profile of transmission-potential provides needed data that define a systematic and highly-sensitive weather-disease connection , demonstrating separate but coupled roles of temperature and humidity . New insights regarding natural regulation of human-mosquito transmission across diverse ecological settings advance our understanding of risk locally and globally for dengue and other mosquito-borne diseases and support advances in public health policy/operations , providing an evidence-base for modeling , predicting risk , and surveillance-prevention planning .
The escalating geographic scope and disease burden associated with dengue viruses ( DENVs ) over the past 50 years are serious global health concerns . Recent estimates of the global burden are nearly 400 million infections and 500 , 000 hospitalizations annually [1 , 2] . Weather is a fundamental and complex regulator of the local potential for DENV transmission [3 , 4] , yet the significance of weather dynamics , especially in the contexts of strategic targeting of prevention resources and effects of global warming on risk , is hotly debated [4–9] . A clear understanding of the regulatory role of weather in DENV transmission has not yet evolved [3 , 4 , 7 , 10] . Weather-dengue dynamics are complex , multi-factorial , and non-linear and traditional statistical methods have produced diverse and conflicting perspectives regarding the weather-disease connection [3–5 , 7 , 8 , 11–18] . The seasonal nature of DENV dynamics in regions with hyper-endemic transmission has been linked to seasonal cycles in local weather and the mosquito Aedes aegypti [3 , 19 , 20] . Field and laboratory studies provided insights into relationships between local weather dynamics and specific aspects of mosquito ecology , development , life-cycle , survival , biting habits , extrinsic incubation period , and capacity to become infectious and transmit the virus [19–37] . Each of these individual biological processes related to the vector are sensitive to specific aspects of weather . A slight temperature change , for example , can impact life cycle dynamics , adult vector survival , or extrinsic incubation period in different ways [21–37] . Synthesis of many weather dependent dynamics into a measure of potential for virus transmission in ecological settings that continually change in space and time is a complex undertaking . Models have been used to explore the impact of weather on specific vector dynamics , however empirical data needed to support the computational link between weather , vector , and transmission dynamics across diverse ecological settings is lacking . Temperature and humidity are emerging as weather components with greatest impact on DENV transmission , yet the separate and combined effects of this coupling across a range of ecological conditions remain ill-defined [3 , 4 , 7 , 11] . Global models estimate effects of individual components such as temperature or vapor pressure [4 , 5 , 7] . Specific weather thresholds , temporal fluctuations , and seasonal duration in optimum conditions are likely to be determinants of local transmission intensity [4 , 11] . Sparse empirical data are available to test these relationships . Closing the gap between emerging theoretical models and observable naturally occurring virus transmission dynamics is central to advancing our understanding [4] . A currently missing perspective is a high-resolution , broad-coverage empirical view of DENV transmission across a diverse range of ecological conditions as a basis for informing models and speculation about the roles and importance of weather . We present a high-resolution empirical view of the interplay between weather and DENV transmission across Peru , a richly diverse ecological setting where transmission is intense and escalating [38–40] . During 1990–2012 , each of the 4 dengue serotypes was introduced into Peru , and produced large seasonal epidemics . Highest disease levels were observed since 2001 , increasing annually since 2005 in heterogeneous spatial patterns ranging from intense transmission to none ( Fig 1 ) . Local weather patterns encompassed extreme ranges; temperature: -9 to 38°C and humidity: 5–100%; shifting east-to-west across the Amazon Basin , Andes Mountains , and Pacific coast urban/semi-urban topography . Shifts in seasonal weather cycles mirrored emerging spatial disease patterns . With 30 million people in Peru , the largest population centers are mostly clustered along the western coast . Highest dengue incidence rates developed in a different spatial pattern across northeastern Peru . We developed a binary classification tree to elucidate the location , timing and intensity of DENV transmission in relation to local daily weather patterns across Peru during 1994–2012 . We explored evidence of transmission indirectly by examining patterns in reported cases and considered the concept of transmission potential by investigating the maximum levels of incidence observed in relation to specific weather conditions . We investigated three hypotheses: Weather determines ( 1 ) local potential for DENV transmission; i . e . local weather conditions must meet/exceed specific thresholds for a defined minimum duration annually in order to support vector dynamics necessary for transmission; ( 2 ) local potential for epidemic magnitude; and ( 3 ) the seasonal timing of local dengue epidemics . Weather was conceived as a 2-dimensional “weather-space” composed of temperature vs . humidity , inclusive of the full range of conditions observed locally across Peru , within which DENV dynamics are observed ( portrayed for low and high transmission areas in S1 Fig ) . This weather-space framework enabled a clear depiction of the complex coupling between temperature and humidity , how time is distributed across this space , and specific areas of the weather domain in which transmission develops and scales to elevated levels . We conclude that a coupling of temperature-humidity is an important and sensitive determinant of DENV transmission-potential regulating the location , timing and intensity of risk . We present a high resolution profile of potential across Peruvian weather-space . The concept of potential for transmission does not imply that a certain level of transmission always occurs under specified conditions , but rather that such conditions are necessary for ongoing transmission to occur . Potential provides an indicator of risk and can be used to guide targeted surveillance-prevention programs and inform model development . Simulations capable of representing space-time varying risk dynamics associated with different ecological contexts would significantly enhance the evidence-base for prevention planning and policy decision-making to reduce dengue .
Dengue cases were assessed for all Peruvian districts by week for 2005–2012 and for the 6 highest incidence areas of Peru by week for 1994–2012 . Peruvian administrative departments are subdivided into provinces and further divided into districts . Dengue cases reported by district-week for all of Peru , 1994–2012 , were provided by the Dirección General de Epidemiología of the Ministry of Health in Lima . In a passive surveillance network spanning 95% of health centers across Peru , if a patient fit the WHO dengue case definition , the case was recorded in weekly surveillance records . Not all infected people used health facilities and the most symptomatic and severe cases were more likely to be reported . We expect there were fewer reporting facilities in earlier years and more complete/consistent coverage in years 2005–2012 , with better reporting in urban compared to rural settings in general , although no data to describe this are available . Not all reported dengue cases were confirmed with laboratory tests . Individual cases were not attributed to a particular serotype . Population data by district were derived from census data to assess disease incidence rates . Census data by district for years 1993 , 2007 , and 2012 were obtained from El Instituto Nacional de Estadísticañ e Informática ( INEI ) and combined with birthrate , death rate , and migration rate data provided by INEI for the same time span . A dynamic population model was developed from these data to represent population dynamics by district-week throughout Peru for 1994–2012 . Weather , including mean , minimum ( min ) , and maximum ( max ) temperature and mean , min , and max humidity were assessed for all Peruvian districts by week for 2005–2012 and for the 6 highest incidence areas of Peru by week for 1994–2012 . Weather data per district-week for mean , min , and max temperature and humidity were developed from daily records of weather stations ( see map in S2 Fig ) throughout western South America ( latitude: -23 to +6 . 2 , longitude: -82 to -60 ) for 2005–2012 . Station data were downloaded from www . ncdc . noaa . gov . Weekly mean , min , and max values for temperature and humidity represent means of daily values . Only conservative corrections to the weekly weather data were applied in order to avoid introducing an artificial or subjective bias . Extraneous outliers , exceeding 3 standard deviations from the mean per weather component/location , were corrected by linear interpolation . 33 such weekly corrections were applied across all time series for all locations . Short gaps with missing data for up to 11 consecutive weeks were filled by linear interpolation . A spatial 1 kilometer grid of western South America was generated for each weather component by day using triangulation with cubic interpolation between weather station control points for 2005–2012 . District-week observations were derived by averaging over grid points that fell within a district polygon within a given week . Resulting values were randomly checked for consistency with public daily weather reports for cities across Peru provided by the Peruvian government . In all checks , comparison of mean temperature for the same location and time of year were within 1–2°C and were considered consistent . Map summaries of the temperature and humidity range across Peru developed from gridded data are shown in S3 Fig . For each of the 6 highest cumulative incidence areas in Peru ( Maynas and Alto Amazonas provinces of Loreto department , and Ucayali , Madre de Dios , Tumbes , and Piura departments ) a spatial 1 kilometer grid of each area was generated using Delaunay triangulation of nearest weather stations per day for 1994–2012 . Weather measurements for these areas were derived by averaging grid points that fell within each polygon per day . In a limited area of northwestern Peru surrounding Cajamarca , weather data could not be gridded in a meaningful way due to a steep nonlinear temperature gradient over a short spatial range that transitions from mountains to coast and a lack of control points to inform the spatial weather transitions in that area . Without additional control points , gridded temperature outputs in the area could easily have been off by 5°C or more and we would have no way of estimating or verifying this . Due to the lack of control points , weather data in this limited area were considered missing . This area which includes districts with and without dengue cases was thus excluded from classification tests . The excluded area represents 3 . 9% of Peruvian districts and 5 . 5% of the population . All calculations and software development were performed in Matlab . A binary classification tree was developed to accept weather data by district-week ( 2005–2012 ) as inputs and predict districts in which DENV transmission did and did not occur during this time period ( Level 1 ) and distinguish between districts with varying intensity of transmission ( Level 2 ) . In each classification step , each Peruvian district was classified into one of two groups based on weather inputs . In Level 1 , districts were classified as positive or negative for dengue transmission during 2005–2012 . Each weather component ( min , mean , and max temperature and min , mean , and max humidity ) was tested individually and in all permutations of pairs as classification inputs . Possible thresholds per weather component for predicting transmission were tested based on the full range of weather conditions observed across Peru , 2005–2012 with stepping intervals of 0 . 5°C for temperature and 2% for humidity . At each threshold of each weather component tested , the mean duration in weeks spent above that threshold annually was assessed for each district . Thus each classification test consisted of a configuration that specified ( 1 ) the components of weather to be used as inputs , ( 2 ) the test threshold for each weather component and ( 3 ) the minimum duration of time ( 1 to 52 weeks ) above each respective threshold annually . Each test configuration produced a complete classification of districts . If a district satisfied the test of minimum duration per year above the respective threshold per weather component for all weather components in the test configuration , it was classified as dengue positive , otherwise it was classified as dengue negative . Preliminary tests indicated that max temperature and max humidity were least informative for classification ( consistently lower sensitivity and specificity then other measures ) and were eliminated , thus reducing the number of test configurations to more than 2 trillion . Pattern recognition was applied to dynamically vary the stepping interval and reduce the number of tests to a tractable level computationally . Previously a suppressive effect of max temperature above 34°C was observed in Thailand [3] . In Peru there were few instances in which max temperature reached or exceeded 34°C , thus it was not possible to observe such an effect in this study . The true status of each district was assessed from cumulative dengue cases for 2005–2012 . Districts with no cases during this period were considered negative and districts with 10 or more cases were considered positive . Districts with less than 10 total cases during the 8 year period were considered uncertain with respect to true DENV transmission status and not included in the classification . We observed multiple occurrences of 1–6 total cases reported in southern Peru where temperatures were extremely cold and dengue transmission was highly unlikely . Such low case counts could easily be associated with a few travelers who became infected in a different location and are unlikely to reflect ongoing transmission dynamics in the residence location . Considering there were likely to be similar instances that were less apparent , we chose to address this issue in a conservative approach by identifying a low threshold ( <10 total cases over 8 years ) in which the true status of a district would be considered ambiguous . This choice was guided by examining the distribution of case counts and eliminating a small cluster in the lower tail of the distribution that was likely due to a combination of infections from travelers and low reporting in remote rural districts . 80% of the excluded districts had 4 or fewer total cases during 2005–2012 . The spatial distribution of excluded districts appeared random throughout all of Peru and was not focused in any particular climate zone . It included remote rural areas in the Amazon Basin where transmission is likely and southern and coastal areas where travel related infections are likely . Scaling the threshold for exclusion criteria to larger case counts in more populated areas would have been a more aggressive approach and would have excluded many true positive districts . 1557 districts were classified of which 186 were considered true-positives and 1371 were considered true-negatives for DENV transmission . Classification results from all test configurations were evaluated to identify the weather conditions that produced the lowest overall misclassification rate and the highest level of both sensitivity and specificity in classifying 1557 districts . Sensitivity represents the percent of true-positive districts that were classified as positive . Specificity represents the percent of true-negative districts that were classified as negative . Thus testing of a very low mean temperature threshold would identify most districts as positive , resulting in high sensitivity but poor specificity; and testing a high mean temperature threshold would identify most districts as negative resulting in poor sensitivity but high specificity . The best classification criteria was chosen such that both sensitivity and specificity were high . Binary classification algorithms were developed in Matlab and are summarized in S4 Fig and S5 Fig . In level 2 of the classification tree , true positive districts were categorized into 5 groups according to 2005–2012 cumulative incidence rate per 1000 population; low to high: group 1: >0 to 1 , group 2: 1–5 , group 3: 5–20 , group 4: 20–60 , group 5: 60–130 . These categories are linear on a log-scale and reflect the exponential increase in incidence associated with longer seasonal duration of transmission . Binary classification tests were run in a similar manner to the protocol described above for level 1 . In level 2 , the goal was to test the ability to differentiate between high and low transmission groups based on weather inputs . Mean and min temperature and mean humidity were used individually and in combinations as weather inputs for tests of each pairing of groups . Min humidity was dropped from test configurations because it was consistently less informative than mean humidity in classification outcomes , i . e . it followed a similar pattern to mean humidity but with consistently lower specificity and sensitivity in test results . Statistical analyses were performed to further investigate the relationship between local weather patterns and DENV transmission patterns across districts , with a focus on quantifying transmission-potential under different weather conditions . Potential magnitude of virus transmission is a complex measure since locations associated with high levels of transmission do not always experience high transmission when weather is optimal due to other regulating factors . Considering such factors fluctuate over time , two indicators of transmission-potential were assessed: ( 1 ) cumulative incidence rate by district and ( 2 ) maximum dengue impact per district-week across the temperature-humidity weather-space of Peru . Linear regression was used to examine the association between ( a ) annual temperature and humidity ranges versus cumulative incidence rate by district and ( b ) mean annual duration above specific temperature and humidity thresholds versus cumulative incidence rate by district , 2005–2012 . The natural log of cumulative incidence rate was used because the increase in incidence as temperatures rise within the optimal range and as humidity rises amidst temperatures optimal for transmission is exponential in nature . As a surrogate for transmission-potential , maximum dengue impact was measured as the mean of the top 1% of local weekly incidence rates incurred per 1°C temperature and 2% humidity weather interval across Peruvian weather-space . Profiles of maximum dengue impact across weather-space were developed graphically in Matlab and are presented as 2-dimensional weather-space grids for mean , min , and max temperature-humidity . Six areas of Peru were identified as locations of highest cumulative dengue incidence rates during 1994–2012 . These areas include Maynas and Alto Amazonas provinces of Loreto department , and Ucayali , Madre de Dios , Tumbes , and Piura departments . Weather and DENV transmission dynamics were assessed by week during 1994–2012 for the 6 areas in order to better understand the role of weather in development of large epidemics . For the 6 locations combined and for each location individually , frequency ( where time is spent ) , cumulative incidence rate , and maximum dengue impact profiles were developed in Matlab . In each of the 6 locations , each seasonal DENV transmission cycle during 1994–2012 was evaluated to identify systematic change-points in the number of weekly cases . Three change-points were identified within each seasonal transmission cycle in each location: Nadir , Onset , and Peak . Peak was defined as the first week during the seasonal cycle in which the local seasonal maximum case count occurs . Peak marks the end of accelerating case counts and the beginning of seasonal decline . Nadir was defined as the last occurrence between consecutive seasonal Peaks in which the case count is at a seasonal low . Onset , identified using a statistical algorithm , follows seasonal Nadir and marks the time-point when potential epidemic development begins . Nadir , Onset , and Peak from each seasonal cycle were used to compare the timing of transmission change-points across seasonal cycles of varying magnitude in each location and were quantified using linear regression . A more detailed description is provided in S1 Text .
Peru encompasses a diverse dengue history spatially , including 1828 districts that range from districts with no reported dengue cases to districts with repeated large epidemics . Median size of districts ( subunit of departments/provinces ) is 208 sq . km . and 4360 people . The goal of level 1 of the binary classification tree ( see flow chart , S5 Fig ) was to perform an exhaustive search of individual and combined temperature and humidity conditions across Peru by district-week 2005–2012 to determine if one set of weather criteria effectively separated Peruvian districts into 2 groups: transmission ( positive ) and no-transmission ( negative ) . Considering the full weather-space of Peru and duration of time annually each district spends within each 1°C temperature interval and 2% humidity interval , 1557 districts were tested as positive or negative for DENV transmission using weather inputs and compared with their true status ( S4 Fig ) . The best classification criteria minimized misclassification rate and achieved high sensitivity and specificity . Out of more than 2 trillion classification tests performed to fully explore Peruvian weather-space , only one set of weather inputs achieved a classification of districts as dengue-positive or negative with 95% sensitivity and specificity . The combined criteria: mean temperature >22°C for 7 or more weeks per year and minimum temperature >14°C for 33 or more weeks per year provided a classification of districts with 95% sensitivity and 95% specificity , and overall 5% misclassification rate ( Fig 2 ) . Mean temperature alone and minimum temperature alone each produced a slightly higher misclassification rate ( 6% ) . Mean humidity alone produced a 16% misclassification rate . Inclusion of humidity in combination with temperature inputs did not improve the misclassification rate ( Table 1 ) . The 5% misclassification group included districts in remote areas of the Amazon Basin where weather conditions are likely supportive of transmission but human populations are sparse and no cases were reported and locations along the southern coast of Peru where weather conditions are marginal and districts are isolated spatially , surrounded by areas with no transmission due to colder weather . Areas where cases were observed but not predicted included a few districts along the western edge of the Amazon Basin where elevation rises quickly in the transition to mountains and local temperatures captured in gridding were on the edge of prediction criteria , and a few districts along the northern coast where seasonal weather conditions peak at the edge of prediction criteria . We expect that the results identify specific temperature thresholds necessary to support critical biological processes and duration of time annually above these thresholds necessary to support transmission . The goal of level 2 of the binary classification tree was to determine the degree to which weather inputs could effectively differentiate between districts with different disease incidence magnitude . Dengue-positive districts were separated into 5 groups based on 2005–2012 cumulative incidence rate , with groups 1–5 ordered by increasing magnitude . Binary classification tests were run between pairs of groups to assess weather inputs in classifying districts into high and low incidence groups within each pairing . Strong differentiation was found between groups 1 and 5 with 100% sensitivity , 95% specificity , and 3% overall misclassification rate ( Table 2 ) . Misclassification was 7% between groups 2 and 5 , 100% sensitivity and 92% specificity . When comparing adjacent incidence groups , 4 and 5 , misclassification was 11% , with 100% sensitivity and 85% specificity , indicating differences in weather conditions between groups . Differentiation based on weather slowly declined between groups with more similar and low/moderate incidence . Group 5 , the highest transmission group , was distinguished by annual periods of concurrent high mean and minimum temperature and high mean humidity > 82% . The most important observation from level 2 classifications was that although temperature alone identified positive vs . negative districts in level 1 , mean humidity in conjunction with temperature was necessary for differentiating groups by transmission intensity , indicating a coupling of temperature-humidity in the dynamics of large epidemics . Results of the binary classification tree indicate that temperature regulates the potential for transmission to occur locally and a coupling of temperature-humidity regulates potential magnitude . Potential magnitude of virus transmission is a complex measure because locations associated with high transmission do not always experience high transmission when weather is optimal due to other regulating factors such as human susceptibility or vector control . In endemic areas , dengue is not only characterized by seasonal cycles but also multi-year cycles . We therefore quantified cumulative and maximum-impact disease patterns across weather-space as surrogates for assessing transmission-potential . The extreme sensitivity of incidence magnitude to the coupling of temperature and humidity was evident from more detailed examination of the highest incidence areas of Peru . We identified 6 areas of highest cumulative dengue incidence: Maynas and Alto Amazonas provinces and Ucayali , Madre de Dios , Tumbes , and Piura departments ( referred to as areas of “elevated-risk” ) . See S1 Fig for a map of these locations and their individual weather-space pattern . Together these areas represent 11% of the Peruvian population and 70% of cases reported during 1994–2012 . We examined weather and dengue case data for 1994–2012 by week for these locations to gain insight into dynamics of large epidemics . A maximum dengue impact profile was developed for these areas ( lower half of Fig 4 ) and is similar to that shown for all of Peru in upper Fig 4 . The primary difference between elevated-risk areas versus Peru overall is that weather-space is limited to the upper temperature-humidity range year round and frequency grids indicate more time is spent in the optimal weather-space for transmission . Weather-space profiles of cumulative incidence and maximum impact were developed for each of the elevated-risk areas individually ( Figs 5 and 6 ) . Despite different annual weather ranges , the temperature interval in which most incidence occurred was narrow and strongly aligned across locations; 80% of cases were within: 25 . 5–29°C mean temperature , 21–23 . 5°C minimum temperature and >75% mean humidity . Tumbes is in the northwest corner of Peru at the Ecuador border with a population of 228 , 000 . Human movement across the border may provide a dengue entry point into Peru . The transmission season is short due to the seasonal weather pattern , with 75% mean humidity when temperature is optimal . Piura is the neighbor of Tumbes to the south with a population of 1 . 8 million people . Dengue incidence is highly correlated between Tumbes and Piura thus Piura may amplify the reservoir of cases developed in Tumbes . Piura cycles between optimal temperature and much lower temperature conditions seasonally , thus cases were concentrated in the higher temperature range which suffered from lower humidity . Piura had the lowest incidence rate of the 6 locations . Alto Amazonas province of Loreto department in central northern Peru is mid-way between Tumbes/Piura and Maynas and sits on the western edge of the Amazon Basin . It has a population of 117 , 000 and a transmission season more than twice the duration of Tumbes and Piura , Incidence is moderately high with mean humidity near 75% when temperature is optimal , slightly warmer than Tumbes . Maynas province ( including Iquitos district ) is geographically isolated in the northeast corner of Peru within the Amazon Basin with a human population of 550 , 000 . Maynas has a limited weather-space that supports dengue transmission nearly year round in the lower end of the optimal temperature range but with very high humidity >80% most of the year . The large population , high incidence rates , and year-round transmission season , make Maynas an important reservoir of cases for the region . Ucayali , south of Maynas and separated by sparsely populated jungle , borders Brazil on the east side of Peru . Ucayali has 478 , 000 people . Most transmission occurs in the northern urban area . The transmission season is somewhat shorter than Maynas and typically follows a similar incidence pattern with mean humidity near 80% when temperature is optimal . Madre de Dios in the southeastern corner of Peru with 128 , 000 people has a transmission season that lasts more than half the year . Madre de Dios has a unique dengue transmission history compared to the rest of the region . Weather patterns suggest that this area could produce the highest incidence rates in Peru with seasonal temperatures near 28°C and concurrent 80% humidity . Prior to 2005 , dengue cases were sparse but since then this area has been one of the highest incidence locations in Peru . Madre de Dios had the highest cumulative incidence rate of the 6 locations for 2005–2012 , followed closely by Maynas . Madre de Dios , Maynas , and Ucayali each experienced higher mean humidity when temperature was in the optimal range and had higher maximum dengue impact compared to Alto Amazonas , Tumbes , and Piura . Weather patterns varied across Peru and the timing of seasonal highs in temperature and humidity varied spatially . Locally , the presence of dengue disease was associated with a minimum annual duration above 22°C mean temperature as demonstrated by the binary classification tests . , Moderate outbreaks to large epidemics were associated with local seasonal timing in which mean temperature ranged 25–29°C with highest incidence when humidity was concurrently >80% . In elevated-risk locations , week of seasonal peak incidence locally occurred when mean temperature was 26–29°C and minimum temperature was >21°C for 96% of moderate-large seasonal transmission cycles ( 96% of 3rd and 4th quartiles in ranking of seasonal transmission cycles per location; see S6 Fig ) . Complexity in transmission networks arises from geographic variation in weather timing across Peru and thus spatial variation in timing of transmission events . Not only did timing vary on a broad geographic scale , but more importantly , timing of seasonal transmission events varied locally in relation to seasonal cycle magnitude ( see S6 Fig ) . Larger epidemics began earlier where seasonal forcing from weather was present; i . e . , where weather supported high transmission for limited duration annually . No systematic change in timing of Peak was observed for larger epidemics , thus larger epidemics began building earlier in the season , when temperature was high but humidity was not yet optimal , in order to achieve highest case counts at Peak . Peak occurred when temperature and humidity were both optimal and decline began when temperature dropped . We expect higher local human susceptibility at the beginning of seasonal cycles helps to support the early start of a large epidemic , increasing the probability of transmission . See S1 Text for further details regarding the statistical assessment of timing of large epidemics . Seasonal timing of weather cycles , annual duration of optimal weather for transmission , and timing/duration of DENV transmission varied spatially across Peru . The geographic configuration of these dynamics plays an important role in broad scale transmission networks . We mapped the seasonal timing of key weather transitions , illustrating spatial differences in timing of mean temperature >24°C and min temperature >21°C and extreme spatial variation in duration of these conditions annually . The timing of seasonal peak humidity when temperature is in this range is very different spatially from the timing of peak mean temperature ( Fig 7 ) . Spatially , the timing of peak seasonal DENV transmission was most closely aligned with timing of highest humidity when temperature was optimal , and not well aligned with seasonal timing of highest local mean temperature . Consistent with weather dynamics , moderate or higher incidence for >20 weeks annually was observed only in districts of Maynas , Alto Amazonas , Madre de Dios , and Ucayali , identifying these as key locations that may have played an important role in enabling the broad scale epidemiology of dengue in Peru . Seasonal duration was nearly year-round in Maynas , indicating it may provide an important ongoing reservoir of cases for the region . West to east , Peru’s topography shifts from Pacific coast to Andes Mountains to Amazon Basin with dramatic weather transitions . Northeastern Peru sits on the edge of the Amazon Basin , a large geographic feature in South America shared by Peru , Ecuador , Columbia , Venezuela , Brazil , northern Bolivia , Suriname , and Guyana . Highest DENV transmission areas in Peru sit on the western edge of this basin . The entire Amazon Basin shares a weather pattern that is similar to northeastern Peru ( S7 Fig ) with conditions optimal for transmission nearly year-round that define a high level of risk where human and vector populations are resident . Peru is influenced by transmission in countries to the north and east that share the Amazon Basin and have a heavier dengue burden that preceded Peru’s dengue history [41 , 42] . Chronologically , serotype-specific outbreaks in Peru followed similar transmission in these neighboring countries [43] . The broader weather pattern has important implications for understanding risk in relation to potential DENV transmission throughout South America and into Peru .
DENV transmission dynamics are complex . Weather plays a fundamental regulatory role in determining location , timing , and magnitude of virus transmission-potential . Key discriminators of potential and indicators of risk were defined by annual duration in specific areas of temperature-humidity weather-space . Temperature defined necessary vs optimal conditions for transmission; higher humidity levels concurrent with optimal temperature amplified potential; magnitude rose exponentially with increased humidity and annual duration in optimal weather-space . Dengue-positive districts were identified by mean temperature >22°C for 7+ weeks and minimum temperature >14°C for 33+ weeks per year , defining environmental conditions needed to sustain biological processes that enabled transmission . These results are consistent with observations from Thailand , 1983–2001 , in which no cases were reported when mean temperature was <21°C or minimum temperature was <14 . 5°C [3] . Laboratory studies previously reported failure to obtain virus from salivary glands when Aedes aegypti were maintained at 20°C and failure for larvae to become adults below 14°C [24–26] . Temperature barriers observed in laboratory studies and now in long term observations of virus dynamics , likely represent a fundamental weather barrier for sustained DENV transmission . Epidemic magnitude was highly sensitive to duration in specific areas of temperature-humidity weather-space , increasing exponentially with longer duration in optimal conditions . Across Peruvian districts , we established an empirical relationship between duration in specific areas of weather-space and transmission-potential . These data indicate that more time spent in temperature-humidity that are optimal for transmission increases the duration of elevated probability of mosquito infection and transmission , i . e . , longer duration of increased probability of vector survival , higher biting frequency , increased vector competence and shorter extrinsic incubation period [21–35] . Large epidemics are likely dependent on a combination of these dynamics being optimized concurrently and for longer seasonal duration , which would explain why the largest epidemics in Peru occurred in a highly limited area of the weather-space where both temperature and humidity were optimal . Weather was not effective in predicting slight differences in incidence magnitude among low incidence areas . One explanation for this is that low-reporting rural areas in the Amazon Basin region with warm humid weather were present in several incidence rate groups and diluted the results of classification tests between groups of districts . Another possible explanation is that weather does not differentiate lower incidence levels at a fine scale as much as factors related to the distribution and movement of people . Weather patterns observed in districts with the lowest incidence rates , highest incidence rates , and in districts at the incidence rate median among dengue-positive districts are shown in S8 Fig , illustrating the nature of seasonal forcing from weather observed in different incidence rate categories . There are limitations in applying these data sets . Dengue case reporting improved over time and was likely the most consistent during 2005–2012 , the years used to apply the binary classification tree . We expect that more symptomatic and severe cases are typically reported and thus serve as a surrogate for capturing dengue transmission . Local case reporting may vary from district to district , especially in remote rural areas of northeastern Peru . We observed instances of neighboring districts with similar weather conditions in which the higher population urban district reported high incidence rates and the lower population rural district reported low incidence rates . In such instances there are no data to clarify whether more sparsely populated locations experienced lower incidence rates or lower reporting rates . The majority of these instances were observed in northeast Peru in the Amazon Basin region where transmission is highest , appearing as low incidence “holes” amidst higher incidence areas within similar weather conditions . As low incidence “holes” , these areas underscore our premise that specific weather conditions are necessary but not sufficient for high incidence to occur . If , instead , these areas suffer from under-reporting , then their true incidence pattern is more consistent with surrounding areas than our observations depict and thus consistent with our findings regarding the weather-dengue link . The most likely bias introduced by lower reporting in remote rural districts , is a slight dilution of the observed relationship between local weather range and cumulative disease magnitude . Our indicators of transmission-potential , using cumulative case counts over many years and maximum impact reported during discrete time intervals , were designed to minimize the effects of unavoidable variances in case reporting . Weather data registers the spatial-temporal patterns of temperature and humidity across Peru by district and week throughout the study period . This is a broad and detailed coverage of weather patterns , however transmission dynamics are also susceptible to even more detailed fluctuations on a sub-daily scale and shifts from sun-exposed to shade and indoor vs . outdoor settings . Our weather profile represents an average of local dynamics over typically a few hundred houses at a time across all of Peru . The patterns we observed in the relationship between increasing transmission and changes in weather patterns were very smooth and consistent throughout , especially in classification trees , which indicates that our approach allowed us to observe relationships innate to these complex dynamics . Our study did not include rainfall because data of sufficient quality were not available . Previously , in an analysis of the weather-dengue link in Thailand , rainfall was examined in conjunction with temperature and humidity [3] . Results of that study indicated that local seasonal onset of dengue occurred after the rainy season began , but no correlation was observed between the amount of rainfall and the timing or magnitude of dengue disease . Instead , the coupling of temperature-humidity was strongly associated with the timing of local seasonal transmission dynamics . In this study , it was not possible to examine the relationship between rainfall and dengue transmission in the context of temperature-humidity weather-space . We expect rainfall and humidity each have distinct ecological effects on vector dynamics and humidity is not interpreted in this study as a surrogate for the effects of rainfall . The principal effect of humidity is likely related to survival of the adult mosquito . Rainfall may introduce environmental changes in outdoor containers in locations where such containers play an important role in early stage life cycle dynamics of the vector . Our results were developed using reported cases across Peru . Actual number of dengue infections during the study period was much higher . Many infections are asymptomatic , mild , or not reported [39 , 44] . We expect that vector dynamics related to unreported cases are not systematically different from reported cases and thus the observed relationship with weather is ubiquitous . In early years of dengue transmission in Peru , reporting methods were evolving and a majority of cases were primary infections , likely biased toward mild symptoms , thus infections likely occurred in areas where no cases were reported . To minimize such concerns , we chose 2005–2012 as the study period for classification tests , when multiple serotypes circulated throughout Peru , incidence rates were accelerating , severe cases were increasing , and a well-defined spatial pattern of transmission had emerged . Numerous factors , in addition to weather , influence the size of epidemics locally . Spatial distribution and movement of people have been associated with the spatial distribution of disease [45 , 46] . Cycling and replacement of dominant serotypes have been associated with multi-year oscillations in epidemic magnitude [39 , 47] . Different serotype-strains and sequential infections have been associated with different disease severity profiles [48–50] and likely impact both transmission and reporting . These are complicated dynamics that also influence observed disease incidence rates in a given area at a given time . Case data for this study were not attributed to a particular serotype , thus serotype specific effects could not be isolated . Prior studies in Peru reported patterns of sequential serotype dominance and replacement with changes in serotype dominance during 2005–2012 [39 , 40] . We analyzed maximum dengue impact , as shown in Fig 4 for years 2005–2012 , using separate 3-year subsets of our study period ( see S9 , S10 and S11 Figs ) in which different serotypes were dominant . The level of intensity of dengue impact varied across the time periods but the pattern of maximum impact across weather-space remained consistent during consecutive periods in which different serotypes were dominant . We propose that DENV dynamics are dependent on support for transmission provided by vector dynamics , and thus local weather dynamics provide a necessary but not sufficient set of conditions for transmission to exist and develop to epidemic levels . Several important covariates could not be explicitly assessed in this study . Local presence of Aedes aegypti populations is an important factor in determining transmission potential . The spatial distribution has likely evolved since the onset of dengue in Peru in the early 1990’s , however no data was available at the level of districts to assess the presence or abundance of mosquitoes explicitly . An evolving spatial distribution of local serotype-specific susceptibility profiles of humans is a confounding factor in estimating transmission potential . These dynamics are complex and difficult to interpret even when local human susceptibility by serotype is known over time . Peru has evolved from a mostly susceptible population in 1990 to locally varying levels of susceptibility to each of the 4 serotypes by 2012 . A population highly susceptible to a circulating serotype might have an elevated probability of transmission however if a high percentage of resulting cases are primary infections , they may be mostly mild and go unreported . A higher percentage of secondary infections may result in more symptomatic or severe cases and motivate higher reporting . Data detailing space-time human susceptibility was not available for this study . It is likely that the number of cases reported within a district during a particular season was impacted by these dynamics in ways that are not fully understood . The weather-disease relationship we present was observed over many transmission seasons and across many locations , representing all four circulating serotypes and describes where in weather-space elevated cases occurred , amidst evolving serotype-specific human susceptibility . Seasonal and inter-annual variability is an innate complexity of dengue disease patterns . Focusing on observed maximum disease impact , we developed a profile of transmission-potential across Peruvian weather-space , accounting for incidence that varied over time due to many contributing factors . Amidst these layered dynamics , we described the naturally-occurring underlying relationship between weather and disease that are linked via complex vector dynamics . Given the accelerating global disease burden of dengue and global warming debates , an improved understanding of risk dynamics in varying ecological settings is critically important . Profiles of transmission-potential in weather-space help to define the location , timing and magnitude of risk , contributing needed information to the knowledge-base that drives surveillance-prevention planning and health policy decision-making . Country-specific mapping of the spatial distribution of timing and seasonal duration of elevated risk conditions can be used to guide targeted surveillance and prevention strategies . Pre-emptive control measures that target areas of highest or longest duration of risk may effectively reduce disease on a broad regional scale . Such strategies could be tested in simulations that consider spatially explicit dynamics of natural regulation of risk . A broad empirical perspective detailing the weather-disease connection across diverse conditions provides an ecological basis for informing/validating vector transmission , global warming , and dynamic risk models . Our machine learning approach can be extended to test disease prediction in other areas of the world . We hope that this broad-scale empirical assessment of the weather-disease connection will advance efforts to control dengue and other mosquito-borne diseases locally and globally . | Timing and spatial-extent of diseases such as dengue and malaria that result from transmission between humans and mosquitoes are regulated by weather in complicated ways . For Aedes aegypti mosquitoes , the primary vector of dengue , slight changes in different components of weather have important effects on population dynamics , lifespan , biting-frequency , virus incubation period and capacity to transmit the virus , thus inducing changes in transmission probability . These complicated dynamics produce a weather-disease connection that is not well-defined for different ecological settings . Understanding this connection is important to critical elements of policy development and operational control of dengue such as predicting risk , developing human-vector transmission models , and planning surveillance-intervention strategies locally and globally . The empirical profile of the weather-disease connection for dengue developed in this study provides a needed understanding of how temperature and humidity work together in regulating human-mosquito transmission . The observed likelihood of low to epidemic-level transmission was highly sensitive to local seasonal duration in limited areas of this two-dimensional weather-space . Data presented represent a resource for estimating where and when transmission-potential supports epidemics of varying magnitude . This high-resolution weather-disease profile for dengue reveals systematic relationships that are informative for mosquito-borne diseases in general and discussions of consequences of global warming . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Weather Regulates Location, Timing, and Intensity of Dengue Virus Transmission between Humans and Mosquitoes |
Although a defect in the DNA polymerase POLQ leads to ionizing radiation sensitivity in mammalian cells , the relevant enzymatic pathway has not been identified . Here we define the specific mechanism by which POLQ restricts harmful DNA instability . Our experiments show that Polq-null murine cells are selectively hypersensitive to DNA strand breaking agents , and that damage resistance requires the DNA polymerase activity of POLQ . Using a DNA break end joining assay in cells , we monitored repair of DNA ends with long 3′ single-stranded overhangs . End joining events retaining much of the overhang were dependent on POLQ , and independent of Ku70 . To analyze the repair function in more detail , we examined immunoglobulin class switch joining between DNA segments in antibody genes . POLQ participates in end joining of a DNA break during immunoglobulin class-switching , producing insertions of base pairs at the joins with homology to IgH switch-region sequences . Biochemical experiments with purified human POLQ protein revealed the mechanism generating the insertions during DNA end joining , relying on the unique ability of POLQ to extend DNA from minimally paired primers . DNA breaks at the IgH locus can sometimes join with breaks in Myc , creating a chromosome translocation . We found a marked increase in Myc/IgH translocations in Polq-defective mice , showing that POLQ suppresses genomic instability and genome rearrangements originating at DNA double-strand breaks . This work clearly defines a role and mechanism for mammalian POLQ in an alternative end joining pathway that suppresses the formation of chromosomal translocations . Our findings depart from the prevailing view that alternative end joining processes are generically translocation-prone .
A diverse group of at least 16 DNA polymerases carry out DNA replication , repair , and damage tolerance in the mammalian genome [1] , [2] . One of these is DNA polymerase theta ( POLQ ) . POLQ homologs are found in multicellular eukaryotes including plants , but an equivalent enzyme is absent from yeast [3] . The large 290 kDa human POLQ protein has an unusual bipartite structure with an N-terminal helicase-like domain and a C-terminal DNA polymerase domain [4] . This domain arrangement and the POLQ protein sequence is highly conserved in vertebrates [3] . Several functions have been suggested for POLQ [3] including bypass of template DNA lesions such as abasic sites and thymine glycols [5] , [6] , a backup role in DNA base excision repair [7] , [8] , and influencing the timing of DNA replication origin firing [9] . Loss of POLQ homologs in Drosophila and C . elegans causes hypersensitivity to DNA interstrand crosslink ( ICL ) -forming agents [10] , [11] such as nitrogen mustards or cisplatin . A consistent picture of hypersensitivity to DNA damage in mammalian cells lacking POLQ has not emerged from studies reported so far [3] . Mice devoid of or carrying mutant alleles of Polq display an elevated level of micronuclei ( indicating chromosome breaks ) in their peripheral erythrocytes [12]–[14] . A further increased frequency of micronuclei is observed after ionizing radiation exposure , and is much elevated in Polq mutant animals [12] , [14] . The majority ( ∼90% ) of mice with double homozygous deficiencies in Polq and Atm die during the neonatal period , with surviving double mutant mice showing severe growth retardation [13] . From this observation it was suggested that POLQ has a unique role in maintaining genomic stability that is distinct from the major homologous DNA recombination pathway regulated by ATM [13] . DNA double-strand breaks ( DSBs ) can be formed in cellular genomes by environmental agents such as ionizing radiation . DSBs also arise during normal cellular duplication cycles , when DNA replication stalls at naturally occurring structures or at sites of internally-generated DNA damage . In diversification steps of the mammalian immune system , DSBs are deliberately formed by regulated enzymatic action , to initiate rearrangement of antibody and receptor segments , and as a means to introduce local variation . Because DSBs are toxic and/or mutagenic if not repaired , organisms have multiple mechanisms for DSB repair [15] , [16] . The primary strategies are end-joining mechanisms , which process and rejoin the ends of a DSB , and homologous recombination ( HR ) pathways which employ an undamaged copy of the DNA [17] . End-joining pathways appear to be the first line of defense again DSBs . The most studied pathway is “classical” non-homologous end-joining ( cNHEJ ) , which relies on the DNA-binding Ku70 ( XRCC6 ) and Ku80 ( XRCC5 ) gene products , and the DNA protein kinase ( DNA-PK , PRKDC ) . One or more “alternative” end-joining pathways ( altEJ ) also exist , which are independent of these factors [18] , [19] . During immunoglobulin diversification , the regional end-joining process of class switch recombination ( CSR ) replaces one constant region coding exon for another . This CSR process is known to occur through both cNHEJ and alternative end joining pathways [20] . In mammalian cells , an alternative end-joining repair pathway repair of DSBs is thought to play a role in driving the formation of chromosomal translocations , although the specific enzymology is unclear [21] , [22] . Here , we report experiments that define a specific function and mechanism of action for POLQ in a pathway for alternative end-joining of DNA double-strand breaks in mammalian cells .
To clarify the cellular role of POLQ in response to DNA damage , we measured the sensitivities of Polq-null and Polq-proficient bone marrow stromal cell ( BMSC ) lines to various DNA damaging agents . Cells lacking POLQ exhibit hypersensitivity to ionizing radiation ( Figure 1 ) [12] , [23] , and to the double-strand break-inducing chemical bleomycin , as previously reported [12] . We found that Polq−/− cells were also hypersensitive to other agents which directly cause DNA breaks , including the topoisomerase II inhibitors etoposide and ICRF-193 [24] and camptothecin , a topoisomerase I inhibitor . In contrast , loss of Polq did not cause hypersensitivity to agents that largely form DNA replication-blocking adducts on one DNA strand including ultraviolet radiation and the methylating agent temozolomide . The Polq−/− cells were also not more sensitive than control cells to mitomycin C , cisplatin , and UVA-photoactivated psoralen plus UVA , all of which induce some interstrand DNA crosslinks ( ICLs ) ( Figure 1 ) . These data indicate that POLQ is most important in a process conferring resistance to direct DNA strand-breaks , particularly double-strand breaks . Cells lacking Polq were not hypersensitive to the PARP inhibitor olaparib ( Figure 1 ) while control RAD51D-defective cells were hypersensitive ( Figure S1A ) . This suggests that POLQ does not function in the BRCA/homologous recombination pathway [25] . POLQ-proficient cells treated with both olaparib and camptothecin were significantly sensitized compared to camptothecin alone . However , addition of olaparib to Polq-null cells only modestly increased the sensitivity to camptothecin ( Figure 1 ) . Consequently , PARP and POLQ may operate within the same subpathway of DNA repair . It is important to know whether the elevated level of micronuclei in Polq-defective cells extends to cell types other than peripheral erythrocytes . To answer this question , matched wild-type and Polq−/− BMSC lines were exposed to etoposide or x-rays , and the number of cells with micronuclei after 48 h were enumerated ( Figure 2A and B ) . Polq-null cells exhibited a ∼3 fold increase in frequency of spontaneous micronuclei formation ( Figure 2C ) . Upon exposure to DNA damaging agents , the percentage of cells with micronuclei increased about 1 . 5-fold more per unit of damage for Polq−/− cells in comparison to Polq+/+ cells ( Figure 2A and B ) . This shows that the susceptibility to micronucleus formation in Polq−/− cells is not confined to cells of the hematopoietic lineage , but occurs also in cultured cells , including fibroblast-like BMSCs . Cells lacking Polq were analyzed for their ability to proliferate in culture . Two independent BMSC lines devoid of Polq expression proliferated at a rate comparable to a pair of wild-type control cells , the Polq BMSCs showing only a 5% increase in population doubling times ( Figure 2D and E ) . We extended this analysis to isogenic immortalized mouse embryonic fibroblast ( MEF ) cell lines ( Figure 2F and G ) . Polq−/− cells divided at a rate comparable to Polq-proficient cells . These findings fit with our previous observations that hematopoietic cell counts in irradiated Polq-null mice recovered at rates comparable to wild-type mice [12] . We have observed no major alterations in growth or development in unchallenged Polq null or mutant mice , consistent with previous reports [13] , [14] , [26] . These observations indicate that despite some increased chromosomal instability , POLQ-defective cells originating from a variety of tissues can proliferate at near-normal rates . We sought next to investigate which catalytic activities of POLQ are necessary to confer resistance to DNA damaging agents . Lentiviral-delivered expression vectors were constructed to express wild-type or mutant versions of POLQ in immortalized MEFs , in order to test for functional complementation ( Figure 3A ) . A tandem ( D2330A , Y2331A ) mutation was introduced into the DNA polymerase domain ( POL ) ; mutation of the corresponding residues in other DNA polymerases completely inactivates polymerase activity [27] . In a separate construct , a mutation was introduced into the conserved ATP-binding site of the Walker A motif ( K121M ) in the helicase-like domain ( HLD ) . An equivalent mutation eliminates DNA helicase activity in related enzymes , including HELQ [28] . A third construct ( DM ) was made harboring mutations in both domains . These vectors expressed full-length recombinant POLQ as tested in transfected 293T cells ( Figure 3B and C ) . The mutant cDNAs were tested for their ability to genetically complement the bleomycin sensitivity of Polq-null MEFs . Stable clones with each of the constructs were generated and analyzed for expression of POLQ ( Figure 3D ) . Independent clones of knockout MEFs expressing wild-type recombinant POLQ ( WT4 and WT6 ) were able to rescue bleomycin hypersensitivity ( Figure 3E ) as an antibody that recognizes endogenous POLQ does not yet exist . Neither the polymerase domain mutant ( POL ) nor the polymerase-helicase double mutant ( DM ) restored bleomycin sensitivity ( Figure 3E , Figure S1B ) . Expression of a construct with a mutation only in the helicase-like domain ( HLD ) was , however , still able to restore resistance to bleomycin . These data indicate that POLQ polymerase activity is essential for conferring resistance to DNA damage , while the ATPase activity of the helicase-like domain is not necessary . Similarly reintroduction of polymerase activity of POLQ into Polq-deficient MEFs was able to rescue chromosomal instability ( micronuclei and DNA DSBs , as measured by 53BP1 and γH2AX colocalization ( Figure 3F and 3G , Figure S2 ) . Mice with an S1932P mutation in Polq ( the “chaos1” allele ) have an increased spontaneous frequency of micronuclei [13] . We generated a human POLQ cDNA mimicking the chaos1 mutation ( S1977P ) , but attempted expression of POLQ with this mutation in 293T cells did not yield detectable protein ( Figure S3 ) . This suggests that the chaos1-encoded mutant protein is unstable , consistent with the finding that chaos1 mice have a phenotype essentially indistinguishable from Polq knockout mice [13] . Immunoglobulin class-switch recombination ( CSR ) uses DNA end joining to exchange one constant region of an antibody gene for another constant region . CSR can occur by both Ku-dependent classical non-homologous end joining and Ku-independent altEJ [20] . The overall frequencies of CSR are similar in Polq-defective mice [29] and cultured B cells [30] . To determine whether POLQ is involved in a mechanistically distinct subset of CSR joins , we isolated and analyzed DNA sequences at such joins . Naïve B cells were isolated from the spleens of wild-type and Polq-null mice and stimulated for IgM to IgG class switching , and then the fraction of IgG1-positive B cells was measured by flow cytometry . Parallel B-cell cultures were incubated with NU7026 , a DNA-PKcs inhibitor that suppresses cNHEJ [31] . It has been shown that B cells incubated with NU7026 have an increased proportion of CSR junctions with >1 bp insertion at the junction [31] . This suggests that when a pathway of altEJ is used during CSR , it more frequently results in insertion of nucleotides . We found that B cells from Polq-proficient and deficient mice had similar overall frequencies of CSR ( Figure 4A ) , and inhibition of DNA-PKcs increased the frequency of CSR in both genotypes by 1 . 5 to 2 fold ( Figure 4B ) . The Sμ-Sγ1 junction was then sequenced from 100 clones of each group of IgG1-positive B cells . These data revealed that in wild-type B cells , insertions of >1 bp at Sμ-Sγ1 junctions , that are thought to be altEJ-dependent , comprised about 9% of total events , and that this increased to ∼21% in cells incubated with NU7026 ( Figure 4C , Table 1 ) . Strikingly , in cells lacking Polq , this class of insertions at CSR junctions was absent , even in the presence of NU7026 ( Figure 4D , only one insertion of 2 bp observed ) . Insertion of >1 bp therefore requires POLQ . This class of Polq-dependent joining events included insertions of between 2 and 35 bp . For longer insertions ( greater than ∼10 bp ) homologous sequences were unambiguously detected up to 2–5 kbp away from the junction site ( Table 1 ) , as has been reported for long insertions at Sμ-Sγ1 junctions in ATM-defective B cells [31] . This suggests that most or all of such insertions are formed in a templated manner during altEJ by POLQ . The most important factor in determining which double-strand break repair pathway is used is whether or not the 5′ termini of broken ends are resected [32] . Ends with little or no single stranded overhang are typically rejoined by Ku-dependent cNHEJ . In contrast , CtIP and MRN-dependent resection of 5′ termini generates ends with extended single stranded 3′ overhangs; resection is thought to block cNHEJ [33] and enable repair by altEJ [34] , [35] . To analyze differing requirements for end joining , with or without end resection , we generated two linear DNA substrates with 3′ single stranded overhangs; one with a short overhang ( 6 nt ) , and one a long overhang ( 45 nt , a “pre-resected end” ) ( Figure 5A ) . Both can be aligned with the same 4 nt of terminal complementary sequence . These substrates were then introduced into wild-type mouse fibroblasts or fibroblasts harboring deficiencies in Ku70 or Polq . Repaired products were recovered from cells and quantified . Repair of the short overhang substrate was , as anticipated , over 10-fold less efficient in cells without Ku70 ( Figure 5B ) when compared to Ku70-complemented controls . The absence of Polq−/− had no consequence for repair of this substrate . End joining with the 45 nt overhang substrate was assessed using qPCR primers located to ensure that at least 10 nt of overhang was included in joined products ( Figure 5A ) . Recovery of these products was no longer dependent on Ku; instead , it was increased 2 . 8-fold in Ku70-deficient cells ( Figure 5C ) . This is consistent with previous studies arguing Ku suppresses repair by altEJ . Strikingly , joining of the long overhang substrate in Polq−/− cells was reduced 10-fold , near background levels of signal observed using this assay . Complementation of the knockout cells with POLQ returned joining to wild-type levels ( Figure 5C ) . These data demonstrate that POLQ participates in some form of alEJ , but cells lacking POLQ maintain proficiency for cNHEJ . Our results demonstrate that POLQ is necessary to form the insertions found in CSR junctions in a process of altEJ . We next sought to determine the mechanism . Like other DNA polymerases , an active polymerase fragment of POLQ [36] can catalyze template-dependent DNA synthesis from an annealed primer ( Figure 6A ) . As is common for family-A DNA polymerases , only a single nucleotide is added to the end of duplex DNA [5] . Unusually , however , POLQ can catalyze extension of single-stranded oligonucleotides [37] . It was unclear whether this reflects a robust terminal deoxynucleotide transferase activity of POLQ on single-stranded DNA , or some form of template-dependent synthesis . For example , POLQ can extend a single-stranded 16-mer oligonucleotide provided without a complementary template ( products up to 35 nt long ) , while E . coli pol I Klenow fragment has no activity on this substrate ( Figure 6B ) . The major 22 nt extension product produced by POLQ on the 16-mer used in Figure 6B may be accounted for by inter- or intra-oligonucleotide pairing ( Figure S4C ) . Neither POLQ nor Klenow fragment could extend an oligonucleotide that was incapable of annealing to itself ( Figure S4 ) [37] . To identify the mechanism of 3′ single-stranded DNA extension by POLQ , we used a different single-stranded oligonucleotide designed to be unable to form self-complementary base pairs longer than a single nucleotide [37] , and sequenced the products of POLQ-mediated extension . Individual extension products of 1 to 30 nt were found ( Table S1 ) . Most of the sequenced extension products feature AAC or AAAC sequences that could arise from copying GTTT sequences in the template via inter- or intra-molecular priming and re-priming ( Figure 6C ) following minimal base pairing at the 3′-primer end . These data reveal that POLQ uniquely extends 3′ DNA tails through template-dependent DNA synthesis from a primer with minimal base pairing and that the polymerase lacks true TdT-like activity . POLQ indeed has unique biochemical properties compatible with these observations . Unlike other DNA polymerases , POLQ can efficiently extend a DNA chain with a nucleotide incorporated opposite an abasic site [5] , or from a mismatched primer-terminus [38] . Further , there is evidence that primers slip on DNA templates with an increased frequency during POLQ-mediated synthesis , as shown by the high frequency of single base pair frameshift mutations generated by purified POLQ [39] . Double-strand breaks initiated by AID activity in the immunoglobulin heavy chain ( IgH ) locus of B cells are necessary to generate immunological diversity , but breaks are sometimes generated at other chromosomal sites , providing an opportunity for dangerous chromosome translocations [21] , [22] , [40] , [41] . For instance the oncogenic Myc/IgH translocation that causes Burkitt lymphoma is AID-dependent and requires breaks at both loci , with breaks in the Myc gene rate-limiting [42] . An altEJ process is implicated in the formation of oncogenic translocations in lymphoid tissues , including the Myc/IgH translocation in murine B cells [21] , [43] , [44] . cNHEJ suppresses the formation of such chromosomal translocations [45] . To determine the role of POLQ in chromosomal translocations , Polq+/+ and Polq−/− naïve splenic B cells were stimulated in culture and assayed for the frequency of Myc/IgH translocations ( Figure 7A ) . Notably , in the absence of Polq there was a 4-fold increase in translocation frequency ( Figure 7B and C ) . This indicates that mammalian POLQ acts in a subset of altEJ events to suppress chromosomal translocations . Additionally , an increase in intramolecular IgH rearrangements was found in B cells lacking Polq ( Figure 7B ) . Therefore , although POLQ is involved in an altEJ pathway , it prevents rather than promotes chromosomal instability , rearrangements and the formation of Myc/IgH translocations .
We show that in mammalian cells , POLQ has a specific role in defense against DNA damaging agents that cause direct DNA double-strand breaks , including ionizing radiation , bleomycin , and topoisomerase inhibitors . Our findings indicate that POLQ participates in a novel pathway of alternative-end joining of DSBs , a process that can occur throughout the cell cycle in mammalian cells [17] . The minimal additional sensitization to camptothecin by olaparib in Polq-defective cells suggests that one function of PARP is to participate in a Polq-dependent altEJ pathway . Our experiments indicate that POLQ is an important factor in DNA DSB repair in all cells , not just cells of the hematopoietic lineage . Indeed , Polq is broadly expressed in murine tissues ( Figure S5 ) . Mutants of POLQ homologs in Arabidopsis ( TEBICHI ) , C . elegans ( polq-1 ) , and Drosophila ( Mus308 ) are hypersensitive to ICL-inducing agents [3] , whereas Polq-defective mammalian cells are not appreciably hypersensitive to such agents ( Figure 1 ) . This difference may arise because of differences between organisms in the priority of DNA repair pathway engagement . In proliferating mammalian cells , ICLs are usually dealt with through the Fanconi anemia pathway , which produces enzymatically induced double-strand breaks that are channeled into homologous recombination repair [46] . In Drosophila and some other organisms , an altEJ-dependent pathway may be more important for resolving ICL-associated double-strand breaks . Although Drosophila Mus308 mutants are not hypersensitive to IR , pronounced IR sensitivity occurs in a double mutant when HR is also inactivated [47] . The phenotypic consequences of POLQ-dependent altEJ of double-strand breaks may thus depend on the relative dominance of HR which varies between organisms . We show here that the DNA polymerase activity of POLQ is necessary to prevent cell death and chromosome breaks ( micronuclei ) caused by a double-strand break-inducing agent . Disruption of the ATPase activity in the helicase-like domain of POLQ did not , however , alter the correcting function of POLQ addition to knockout cells . A previous study with mouse cell lines suggested that disruption of the polymerase domain of the murine Polq gene is less severe than complete disruption of Polq [30] , but the result is difficult to evaluate in the absence of quantitative measurements of expression of the partially deleted form . No activity has yet been shown for the helicase-like domain , other than DNA-dependent ATPase function [4] . It is likely that an additional role remains to be discovered that is dependent on the ATPase function of POLQ . When double-strand breaks form in mammalian cells , a majority will be repaired through cNHEJ . However , a subset of these breaks will be handled by alternative end-joining pathways in situations where the DNA end is not compatible with processing by Ku-dependent cNHEJ , or if core components of the cNHEJ machinery are absent or unavailable ( Figure 7D ) . In general , altEJ is defined as a means for repair of chromosome breaks that is exclusive of Ku-dependent , classically defined NHEJ [48] , and dependent on factors ( CtIP , MRN ) that resect double-strand breaks to generate extended 3′ ssDNA tails [34] , [35] ( Figure 5A ) . Accordingly , we showed joining of a “pre-resected” extrachromosomal substrate ( substrate with 45 nucleotide 3′-ssDNA tails ) was stimulated in Ku-deficient cells , similar to results using chromosomal substrates [35] . Joining of this substrate was also dependent on Polq ( Figure 5C ) . Our experiments thus define an altEJ subpathway in mammalian cells that involves POLQ ( termed synthesis-dependent end joining , SD-EJ , in Figure 7D ) , Additional Polq-independent altEJ subpathways may also be operational ( Figure 7D ) . To some extent , different end-joining pathways can be been distinguished from one another by the ligase employed in the pathway , with DNA ligase IV ( LIG4 ) suggested as essential for cNHEJ , and DNA ligase III ( LIG3 ) for altEJ in mammalian cells [21] , [43] , [49] . There are caveats , however . For example , some functional redundancy is apparent between LIG1 and LIG3 in altEJ [44] , [50]–[52] . Ligase deficiencies may thus not be the best marker for distinguishing different end-joining pathways . For the altEJ subpathway under consideration here , dependence on POLQ is the best available definition . The biochemical properties of POLQ provide a mechanistic explanation for its contribution to altEJ . POLQ has a unique ability to add nucleotides to the 3′ ends of single-stranded DNA [37] , primed by minimal pairing with other available DNA molecules ( Figure 6 and Figure S6 ) . Synthesis by POLQ in this context is consistent with the unusually efficient ability of the polymerase to extend from mismatched DNA termini [5] , [38] , and its tendency towards primer-template slippage [39] . In further biochemical experiments it will be of interest to examine the action of POLQ and DNA ligases at double strand breaks with 3′-single-stranded overhangs that closely mimic the resected ends of a DNA double-strand break . In vivo studies with such substrates , including those that can form hairpins in the single-stranded region , would give insight as to the preferred structures for POLQ-catalyzed extension . Unique to the POLQ-dependent altEJ process are frequent joins displaying templated DNA insertions . Some form of altEJ has been implicated in resolution of a subset of double-strand break intermediates in CSR , producing templated insertions [20] . Our data support a role for POLQ in generating the CSR products with these templated insertions . These events are consistent with the templated insertions that occur during Mus308-dependent repair of directed double-strand breaks in Drosophila [47] , [53] and in C . elegans [54] . In the absence of POLQ , the lack of insertion-containing joins is observed , but the global CSR frequency is relatively unchanged ( Figure 4 ) . These insertions are best explained by repeated initiation of synthesis by POLQ ( Figure 6C ) on template sites , ultimately leading to a joined product . In the absence of POLQ , we found a ∼4-fold increase in the formation of the oncogenic translocation Myc/IgH in mice . This increase is comparable to that seen in B cells that have lost Tdrd3 , a regulator of R-loop formation during transcription [55] and miRNA-155 which regulates AID and suppresses oncogenic translocations [56] . In the absence of Polq there is also an apparent enhancement of rearrangement events in the IgH locus , consistent with the elevated level of chromosomal instability observed in cells lacking POLQ [57] . altEJ is typically associated with frequent annealing of the DNA ends at existing microhomologies ( 2–5 bp ) and large deletions at repair junctions [19] . Since translocations commonly feature such microhomologies at their breakpoint junctions [58] , [59] and occur more frequently in cNHEJ defective cells , altEJ is considered the primary mechanism by which translocations occur . Thus , a striking finding of the present work is that the formation of Myc/IgH translocations is suppressed when the POLQ-dependent altEJ subpathway is operational . It is possible that DNA DSBs persist for a longer time in the absence of POLQ , giving more opportunity for the formation of translocations . Alternatively , the POLQ-dependent pathway may be the most efficient at repairing a structurally distinct class of translocation-prone DNA breaks . These studies clearly define a role for POLQ in the repair of DNA strand-breaking agents and provide a mechanism of template-dependent extension of DNA ends necessary to repair breaks in a subpathway of altEJ . This distinct altEJ pathway is necessary to prevent the formation of chromosomal translocations as shown by our in vivo experiments . It has been suggested that suppression of POLQ may be useful in increasing the efficacy of DNA damaging treatments in cancer [3] , [23] , [60] . This promising prospect should be tempered with the knowledge that loss of POLQ may also lead surviving cells to be prone to potentially oncogenic chromosome translocations .
Research mice were handled according to MD Anderson Cancer Center Institutional Animal Care and Use Committee policies and protocol 08-08-08732 . Mice were euthanized by CO2 euthanasia followed by cervical dislocation . Polq+/+ and Polq−/− bone marrow stromal cells and mouse embryonic fibroblasts were plated in triplicate ( 200 , 000 cells per 10 cm dish ) with 15 mL of complete media ( Dulbecco's Modified Eagle Medium+Glutamax , 10% FBS , 1% PennStrep ) . On the indicated days , cells were trypsinized and live cells were counted using trypan blue exclusion ( Countess automated cell counter , Life Technologies ) . Experiments were repeated three times in order to generate standard deviations . Viability was consistently high for all cell lines examined ( >95% trypan blue-excluding cells ) . For X-irradiation 5×105 cells were plated on a 10 cm plastic culture dish , and exposed the following day at 2 Gy/min , 160 kV peak energy ( Rad Source 2000 irradiator , Suwanee , GA ) . Cells were then trypsinized for replating . For UVC-irradiation ( 254 nm peak germicidal lamp ) cells were irradiated in 500 µl PBSA ( 105 cells/ml ) at 5 J m−2 min−1 and then plated . For psoralen-UVA treatment , 5×105 cells were plated on a 10 cm dish and incubated in medium with the indicated concentration of HMT-psoralen for 1 h , the dish was irradiated for with 0 . 9 kJ m−2 UVA ( 365 nm peak , 30 min , 0 . 5 mJ m−2 sec−1 ) , the psoralen-containing medium was removed , and the dish UVA-irradiated in fresh medium for a further 30 min before replating . Chemicals were added at the indicated concentrations to dishes at the beginning of the experiment . Drugs were solubilized in ethanol ( mitomycin c ) , DMSO ( ICRF-193 , etoposide , camptothecin , HMT-psoralen , temozolomide , olaparib ) , or 150 mM NaCl ( cisplatin ) . All chemicals were from Sigma ( St . Louis , MO ) except ICRF-193 ( Enzo LifeScience , Farmingdale , NY ) , olaparib ( AZD2281 , Selleck Chemicals , Houston , TX ) , and mitomycin c ( Calbiochem , Darmstadt , Germany ) . Cells were plated in triplicate in 10 cm dishes and grown for 7–10 days before being fixed and stained with crystal violet . Colonies of 50 or more cells were quantified and experiments were repeated three times to generate standard deviations . A clonogenic assay was performed with Rad51D+/+ and Rad51D−/− Chinese hamster ovary ( CHO ) cell lines exposed to varying concentrations of olaparib . BMSCs were plated at 1 . 5×104 cells per well in chambered slides and treated with the indicated amount of x-rays or etoposide the following day . 48 hr later , cells were fixed with 2% para-formaldehyde , stained with DAPI and coverslipped . Micronuclei were scored by immunofluorescence for 300 cells per group . Experiments were repeated three times to generate standard deviations . 293T cells ( kindly provided by Dr . Christopher Bakkenist , University of Pittsburgh Medical School ) were plated at 150 , 000 cells in six-well plates and transfected the following day with 2 . 5 µg of either pCDH ( System Biosciences , Mountain View , CA ) containing empty control , POLQ , POLQ-K121M , POLQ-D2330A , Y2331A , POLQ-S1977P , or POLQ-DM cDNA using Lipofectamine 2000 ( Life Technologies ) according to manufacturer's specifications . 48 hr after transfection , cells were harvested for RNA isolation ( RNeasy , Valencia , CA ) or immunoblotting . For immunoblots , cells transfected in six-well dishes were resuspended in 200 µL of 2× SDS loading buffer ( 4% SDS , 0 . 2% bromophenol blue , 20% glycerol , 100 mM Tris HCl pH 6 . 8 , 12% 2-mercaptoethanol ) and heated at 95°C for 5 min . 20 µL of extract was separated on a 4–20% polyacrylamide gel , transferred to PVDF membrane , blocked , and blotted with anti-alpha-Tubulin ( Abcam , Cambridge , UK ) ab4074 , 1∶10 , 000 ) , anti-FLAG ( Sigma F7425 , 1∶5 , 000 ) , anti-PCNA ( Santa Cruz , Santa Cruz , CA , sc-56 , 1∶1 , 000 ) , anti-HA ( RW , 1∶10 , 000 ) , or anti-POLQ ( MDACC POLQ20 , 1∶250 ) antibodies and corresponding secondary antibodies ( Sigma A0168 , A0545; 1∶10 , 000 ) and visualized with ECL reagent ( Pierce , Rockford , IL ) . Polq-null ( Polq−/− ) mice [13] were obtained from Jackson Laboratories and maintained on a C57BL/6J background . Isogenic primary MEFs were generated from 13 . 5 day pregnant females and cultured in a 2% O2 atmosphere . MEFs were then transfected with 1 µg of pSV-Tag [61] , [62] and grown in atmospheric oxygen for six population doublings to allow for immortalization . To generate lentivirus used for transduction , 293T cells were cotransfected with psPAX2 ( 6 µg ) , pMD2G ( 6 µg ) , and pCDH ( 12 µg ) expression vector ( See Text S1 for construction of expression vectors ) using Lipofectamine 2000 . One day prior to transduction Polq−/− MEFs were seeded into a 10 cm dish at 1 . 5×105 cells with 12 mL complete media . 48 hr post-transfection virus-containing media was harvested , filtered through a 0 . 45 µm syringe filter and used to replace the media on the plated MEFs . MEFs were incubated in the virus-containing media for 24 hr before being split into T-75 flasks and allowed to grow to 80% confluence before undergoing three weeks of puromycin selection ( 2 . 5 µg/mL ) . Following selection , pure clones were isolated and cultured with complete media containing puromycin ( 1 µg/mL ) . RNA isolated from the complemented MEF lines were analyzed for quality and purity using RNA 6000 Nano kit ( Agilent Technologies , Santa Clara , CA ) . 1 µg of total RNA was used to generate cDNA using the High Capacity cDNA RT kit ( Life Technologies ) . qPCR analysis was performed in triplicate using the ABI Prism 7900 HT thermocycler and the following Taqman Probe set or primer set with iTAQ SYBR Green Supermix with ROX ( Bio-Rad , Hercules , CA ) : MmPolQ_FWD 5′-GGCTCTGAAGAACTCTTTGCCTTT-3′ , MmPolQ_REV 5′-GCTGCTTCCTCTTCTTCATCCA-3′ , probe 5′-TCCGGGCACTTTTG-3′; HsPOLD1_FWD 5′-CGACCTTCCGTACCTCATCTCT-3′ HsPOLD1_R 5′-ACACGGCCCAGGAAAGG-3′ , probe 5′-CCCTCAAGGTACAAACAT-3′; Qexon FWD 5′-TGCCTTTCAAAAGTGCCCGGAAGGC3′ , Qexon REV 5′-TGCCAGTCACCCANATAGTTCNCAT-3′ . Data were analyzed using the ΔΔCt method . For absolute quantification , titration of pCR-XL-TOPO/MmPolQ and pET/MmPold1 plasmids were used to generate standard curves for expression . Transcript abundance was determined by extrapolation from linear regression analysis of best fit lines from titration experiments . GAPDH was used as an internal control in all experiments . Complemented MEF lines were plated in triplicate into white 96 well plates at 1250 cells per well and grown overnight using complete media containing puromycin ( 1 µg/mL ) . The following day , cells are cultured with complete media containing the indicated amounts of bleomycin ( dissolved in 150 mM NaCl ) for 24 hr before the media was replaced . Cells were allowed to recover for 72 hr before cellular viability was measured using the ATPlite 1Step kit ( Perkin Elmer , Waltham , MA ) using a Biotek plate reader . Experiments were repeated three times . Complemented MEF lines were plated at a density of 1 . 5×104 cells per well in 4-well chamber slides and the following day were irradiated with either 0 or 6 Gy of x-rays . Media was changed and cells were allowed to recover for 48 hr after damage before fixation with 2% para-formaldehyde and permeabilized with Triton X-100 . Samples were blocked with donkey serum for 30 minutes before being incubated overnight with primary antibodies against 53BP1 ( Bethyl , Montgomery , TX , A300-272A , 1∶500 ) and γH2AX ( EMD Millipore 05-636 , 1∶400 ) . Cells were later incubated with AlexaFluor-488 goat-anti-mouse or AlexaFluor-594 goat-anti-rabbit secondary ( Life Technologies , 1∶1000 ) and then stained with DAPI before being coverslipped . Cells were scored for DSBs by enumerating the percentage of cells with >2 53BP1 foci and >2 γH2AX foci [61] , [63] . The majority of cells that contained >2 foci for each of the DSB markers , exhibited colocalization of the foci . Cells with pan-staining of γH2AX were not included in the analysis as they are proposed to represent pre-apoptotic cells [64] . Many of the cells with 53BP1 foci , exhibited enlarged foci that are associated with nuclear OPT ( Oct-1 , PTF , transcription ) domains that sequester damaged DNA in G1 [65] , [66] . Thus , most of the MEFs that were foci positive contained DSBs [65] . DAPI-stained micronuclei were also scored . For each experiment 250 cells were scored for three independent experiments for a total of 750 cells . POLQ was purified as described [36] . Klenow Fragment ( 3′→5′ exo- ) was purchased from NEB . POLQ was diluted in buffer containing 30 mM Tris-HCl pH 8 . 0 , 50 mM NaCl , 2 mM DTT , 10% glycerol , 0 . 01% Triton X-100 , and 0 . 1% BSA . Klenow Fragment ( 3′→5′ exo- ) was diluted in buffer containing 25 mM Tris-HCl pH 7 . 4 , 1 mM DTT , and 0 . 1 mM EDTA . POLQ reaction mixtures ( 10 µl ) contained 20 mM Tris-HCl pH 8 . 8 , 4% glycerol , 2 mM dithiothreitol ( DTT ) , 80 µg/ml bovine serum albumin ( BSA ) , 8 mM MgCl2 , 0 . 1 mM EDTA , 100 µM of each dNTP , 30 nM of the primer-template or primer ( see Text S1 ) . Klenow Fragment ( 3′→5′ exo- ) reaction mixtures ( 10 µl ) contained 10 mM Tris-HCl pH 7 . 9 , 50 mM NaCl , 1 mM DTT , 10 mM MgCl2 , 100 µM of each dNTP , and 30 nM of the primer-template or primer . After incubation at 37°C for 10 min for a 16-1+PA42 substrate or 20 min for 16-1 , C20 , C19THF substrates , reactions were terminated by adding 10 µl of formamide stop buffer ( 98% formamide , 10 mM EDTA pH 8 . 0 , 0 . 025% xylene cyanol FF , 0 . 025% bromophenol blue ) and boiling at 95°C for 3 min . Products were electrophoresed on a denaturing 20% polyacrylamide-7 M urea gel , exposed to BioMax MS film , and analyzed with a STORM 860 Phosphor Imager ( Molecular Dynamics ) . A dermal fibroblast line from Ku70 and p53 deficient mice ( the gift of Dr . P . Hasty , University of Texas Health Sciences Center ) was transduced with empty vector ( pBABE-puro ) retrovirus or a retrovirus expressing mouse Ku70 . Substrates were generated by ligating short linkers to the head and tail of a 556 bp linear double-stranded DNA fragment . Linkers possessed 16–17 bp of double-stranded DNA and either 6 or 45 nt 3′ single-stranded overhangs . The linkers with 6 nt overhangs were made by annealing 5′- AGTCTGAGATGGGTGTGAGATCTGC-3′ to 5′-CACTCTCTCACACCCATCTTA-3′ ( “head” linker ) , and 5′-TGACTATACAGCTAAGCGATGATGCAG-3′ to 5′-CATCGCTTAGCTGTATA-3′ ( “tail” linker ) . The linkers with 45 nucleotide 3′ overhangs were generated by annealing 5′-AGTCTGAGATGGGTGTGAGAGTGAAGATCCTCACCTTCGGAGTACTCCTTCTTTTGAGATCTGC-3′ to 5-CTCACACCCATCTCA-3′ ( “head” linker ) and 5′-TGACTATACAGCTAAGCGATGCTCTCACCGAGCGTATCTGCTGTGTTGTGGATGAATTAGATGCAG-3′ to 5′-CATCGCTTAGCTGTATA-3′ ( “tail” linker ) . Excess linker was removed by QiaQuick purification and substrate purity validated by polyacrylamide gel electrophoresis . 75 ng of substrate was mixed with 1 . 1 µg of supercoiled pMAX-GFP ( Lonza ) plasmid carrier and introduced into 2×105 cells in a 10 µl volume by electroporation with one 30 ms 1350 V pulse ( Neon , Invitrogen ) . Cells were harvested after incubation for 1 hour at 37°C , washed , resuspended in Hank's buffered saline solution supplemented with 5 mM MgCl2 , and extracellular DNA digested by incubation with 6 . 25 U Benzonase ( Novagen ) for 15 min at 37°C . Cells were pelleted and DNA purified with the Qiamp kit ( Qiagen ) . Joining efficiency was determined by quantification of head-to-tail junctions by qPCR using primers that either anneal within double-stranded flanks ( 5′- CTTACGTTTGATTTCCCTGACTATACAG-3′ and 5′- GCAGGGTAGCCAGTCTGAGATG-3′; 6 nt overhang , Figure 5B ) or , for the 45 nt overhang substrate only , which anneal to overhang sequence ( 5′- TAAGCGATGCTCTCACCGAG and 5′- GATGGGTGTGAGAGTGAAGATC; 45 nt overhang , Figure 5C ) . Results from electroporated samples were further corrected for differences in transfection and sample processing efficiency using a qPCR specific for substrate ( 5′- GGCACTCTCCAAGGCAAAGA and 5′- ACATGTCTAGCCTATTCCCGGCTT ) . B cells were isolated from mouse spleens ( n = 6 per genotype ) and stimulated for class-switching in culture for 72 hr . Where indicated , cultures were incubated with DNA-PKcs inhibitor 20 µM NU7026 ( Tocris , Bristol , UK ) dissolved in DMSO , or mock-treated . The stimulation procedure and flow-sorting for CSR analysis was as described [31] , [67] . Prior to this analysis , cells were counted; numbers and viability were similar for all groups . Sμ-Sγ1 CSR junctions were amplified by PCR using the following conditions for 25 cycles at 95°C ( 30 s ) , 55°C ( 30 s ) , 68°C ( 180 s ) using the primers ( FWD 5′-AATGGATACCTCAGTGGTTTTTAATGGTGGGTTTA-3′; REV 5′ CAATTAGCTCCTGCTCTTCTGTGG-3′ ) and Pfu Turbo ( Stratagene , La Jolla , CA ) . To the PCR reaction , 5 U of Taq polymerase ( Promega , Madison , WI ) was added and incubated at 72°C for 10 min . The resulting product was TOPO TA cloned and transformed into Top10 E . coli cells ( Life Technologies , Carlsbad , CA ) and plasmids were purified and sent for sequencing using M13 FWD and REV primers in addition to the amplification primers for sequencing . 100 clones for each group were analyzed for mutations , deletions , insertions , and sequence overlaps at the junction and both 30 nt upstream and downstream of the junction . p-values were determined by using two-tailed Fisher's exact test . Naïve B cells from three pairs of Polq+/+ and Polq−/− mice were harvested as above , cultured for 72 hr , and DNA was isolated . 32 separate PCR reactions , each containing the genome from 1×105 cells , was performed with primers to amplify Myc/IgH translocations and amplified translocations were verified by Southern blotting using internal probes to the Myc and IgH loci as described [68] , [69] . Three independent experiments were performed and the p-value was determined using two-tailed Fisher's exact test . %IgG1 was also measured as an internal control to ensure the B cells from each genotype were switching at a comparable level . | The reason for the hypersensitivity of POLQ-defective mammalian cells to ionizing radiation has been elusive . Here we show that POLQ-defective mammalian cells are selectively susceptible to double-strand breaks in DNA . We present experiments in mammalian cells showing that a specific double-strand break repair pathway is POLQ-dependent . To analyze the repair function in more detail , we examined class switch joining between DNA segments in antibody genes . Insertions of DNA bases are sometimes found at the joins between such segments , but the origin of these insertions has been mysterious . We show that this class of insertion joins during immunoglobulin class-switching is entirely POLQ-dependent . In experiments with purified human POLQ protein , we found a novel biochemical mechanism explaining the formation of the insertions . POLQ has a unique biochemical ability to extend DNA with minimal base pairing . Finally , we examined the biological consequences for chromosome stability . Unexpectedly , the Burkitt lymphoma translocation ( a major cancer-associated genome instability ) is enhanced in the absence of POLQ . This alters the current view about the action of DNA end joining in mammalian cells , revealing that a POLQ-dependent DNA repair pathway combats potentially damaging chromosome translocations . | [
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"immun... | 2014 | Mechanism of Suppression of Chromosomal Instability by DNA Polymerase POLQ |
Treatment of Chagas disease , caused by Trypanosoma cruzi , relies on nifurtimox and benznidazole ( BZL ) , which present side effects in adult patients , and natural resistance in some parasite strains . Hydroxymethylnitrofurazone ( NFOH ) is a new drug candidate with demonstrated trypanocidal activity; however , its safety is not known . HepG2 cells dose response to NFOH and BZL ( 5–100 µM ) was assessed by measurement of ROS , DNA damage and survival . Swiss mice were treated with NFOH or BZL for short-term ( ST , 21 d ) or long-term ( LT , 60 d ) periods . Sera levels of cellular injury markers , liver inflammatory and oxidative stress , and fibrotic remodeling were monitored . HepG2 cells exhibited mild stress , evidenced by increased ROS and DNA damage , in response to NFOH , while BZL at 100 µM concentration induced >33% cell death in 24 h . In mice , NFOH ST treatment resulted in mild-to-no increase in the liver injury biomarkers ( GOT , GPT ) , and liver levels of inflammatory ( myeloperoxidase , TNF-α ) , oxidative ( lipid peroxides ) and nitrosative ( 3-nitrotyrosine ) stress . These stress responses in NFOH LT treated mice were normalized to control levels . BZL-treated mice exhibited a >5-fold increase in GOT , GPT and TNF-α ( LT ) and a 20–40% increase in liver levels of MPO activity ( ST and LT ) in comparison with NFOH-treated mice . The liver inflammatory infiltrate was noted in the order of BZL>vehicle≥NFOH and BZL>NFOH≥vehicle , respectively , after ST and LT treatments . Liver fibrotic remodeling , identified after ST treatment , was in the order of BZL>vehicle>NFOH; lipid deposits , indicative of mitochondrial dysfunction and in the order of NFOH>vehicle>BZL were evidenced after LT treatment . NFOH induces mild ST hepatotoxicity that is normalized during LT treatment in mice . Our results suggest that additional studies to determine the efficacy and toxicity of NFOH are warranted .
Chagas disease is endemic in 21 countries , and the World Health Organization estimates that approximately 9-million people are infected by Trypanosoma cruzi [1] . The acute phase lasts for ∼2-months , and is characterized by high parasitemia and fever . Chronic chagasic cardiomyopathy is the most severe clinical consequence of T . cruzi infection , which is detectable in 30% of the patients several years after the primary infection [2] . A considerable advancement in the knowledge about the biology of the T . cruzi parasite has been made in the last few decades ( reviewed in [3] ) . Yet , treatment of Chagas disease still relies on two drugs , namely nifurtimox ( NFX , Bayer ) and benznidazole ( BZL ) , that were developed during the 1960s and are currently manufactured and distributed by Roche ( Rochagan , in Brazil ) , and Maprimed and ELEA laboratories ( Abarax ) in Argentina [4] . Since the T . cruzi life cycle involves intracellular division and subsequent parasite release to peripheral blood , a long-term treatment period ( usually an oral dose for 60 days ) is required . Both drugs are well tolerated by infants and used as standard treatment to control acute infection by T . cruzi in children [5] . However , chronic chagasic cardiomyopathy is a complex malady that involves immune mechanisms as well as parasite persistence . Several studies have noted that treatment with BZL or NFX is not always effective in controlling the chronic disease in chagasic patients . Moreover , these drugs trigger multiple side effects in adults , resulting in noncompliance with long-term treatment [6] . Further , BZL and NFX are not effective against all naturally occurring T . cruzi strains , and some strains have been documented to acquire resistance to these drugs [7]–[9] . Thus , alternative therapeutic drugs that are well-tolerated , safe , and effective against T . cruzi are urgently needed . Due to their limited commercial potential , development of new drugs for Chagas disease is perhaps not a viable option . Instead , drug repurposing–finding a new indication for an existing drug—has enormous potential in developing a new therapy against parasitic diseases . Nitrofurazone ( NF ) , commercialized as a topical medicine for bactericidal activity against gram-positive and gram-negative bacteria , has recently been shown to inhibit trypanothione reductase , the main enzyme responsible for xenobiotic metabolism in T . cruzi [10] . Subsequently , NF was found to have significant anti-T . cruzi activity [11] . Unfortunately , NF showed toxicity against mammalian cells [12] and long-term treatment with NF induced ovarian cancer in mice and rats [13] , [14] , spurring the identification of alternative chemicals with specific activity against T . cruzi only . Among the latter , hydroxymethylnitrofurazone ( NFOH ) was identified as a derivative of NF . The reduction of nitrofurazone is pH-dependent and in acidic medium the hydroxylamine derivative , involving four electrons , is the principal product formed . In aqueous-alkaline medium , the reduction of nitrofurazone occurs in two steps , the first involving one electron to form the nitro-radical anion and the second corresponding to the hydroxylamine derivative formation . NFOH presented the same voltammetric behavior and electroactivity , indicating that the molecular modification performed in NF did not change its capacity to be reduced [15] . We and others have shown NFOH has 2-fold more cytotoxic activity than the parental compound NF against T . cruzi [15] , [16] . The mechanism of action of NFOH against T . cruzi is not completely clear; however , like all nitroheterocyclic compounds , it is enzymatically reduced at the nitro group resulting in the generation of nitroanion ( RNO2•− ) and hydronitroxide ( RNHO•− ) free radicals [17] . NFOH has also been shown to at least partially interfere with mRNA trans-splicing [16] and cruzipain activity [18] that are essential for parasite invasion as well as differentiation to replicative form . Before NFOH can be tested and promoted further as an anti-T . cruzi drug for human use , it is essential that we evaluate its safety profile . In a therapeutic regimen administered to T . cruzi infected mice , NFOH and BZL provided comparable control of T . cruzi and survival from infection ( 84% and 67% , respectively ) [11] , while NF caused 75% mortality in infected mice . Accordingly , in this study , we have evaluated the liver toxicity of NFOH in comparison with BZL by using in vitro and in vivo models . We treated HepG2 liver cells and mice with the two drugs and assessed inflammation , oxidative stress , and cell survival or tissue remodeling . We chose to treat mice with NFOH for short-term ( ST ) and long-term ( LT ) periods that were similar to the recommended regimen for the BZL treatment of children and adults exposed to T . cruzi infection .
All animal experiments were performed according to the National Institutes of Health Guide for Care and Use of Experimental Animals and approved by the Ethical Committee of the National University of Salta and the Animal Care and Use Committee at the UTMB ( protocol # 08-05-029 ) . A hepG2 human hepatocyte cell line was obtained from the American Tissue Culture Collection ( Maryland , USA ) . The cells were cultured in complete DMEM high glucose media ( Gibco ) supplemented with 10% FBS at 37°C , 5% CO2 . Female Swiss mice were bred at the Instituto de Patologia Experimental mouse facility . Swiss mice have been widely employed as animal models for experimental chemotherapy in Chagas disease [19] . NFOH was kindly provided by Dr . Man Chin Chung ( Faculdade de Ciencias Farmaceuticas , Universidade Estadual Paulista , Brazil ) . BZL ( Roche Pharmaceuticals , Brazil ) was obtained from the Ministry of Public Health , Province of Salta , Argentina . Mice ( 30-day old ) received NFOH ( 150 mg/kg/day ) or BZL ( 150 mg/kg/day ) , suspended in 9% NaCl/5% Tween-80 ( vehicle solution ) , as an oral dose of 100 µl , once a day ( six days per week ) . The selected doses of the BZL and NFOH mimicked the dose per kg and dose regime ( two months of daily treatment ) of humans . All experiments were carried out in female mice because they develop disease symptoms similar to those seen in human infection . Additionally , we have noted that the female mice exhibited a higher tolerance to infection than did male mice [11] . One set of mice ( n = 9/group ) received the treatment for 21 days to allow us to determine the acute ( short-term , ST ) liver toxicity of the drugs . Another set of mice ( n = 9/group ) received the treatment for 60 days to facilitate our knowledge of the chronic ( long-term , LT ) liver toxicity of the drugs . Mice given the vehicle solution were used as controls . After treatment , mice were sacrificed , and sera samples and liver tissues stored at −80°C until use . HepG2 cells were cultured as above , seeded at 7 . 5×104 cells/well in 96-well microplate , and incubated overnight in complete media at 37°C , 5% CO2 . Cells were treated in serum-free medium with NFOH or BZL ( 5 , 50 and 100 µM ) for 24–48 h . To examine the drug-induced changes in cell viability and proliferation , after the drug treatment , we incubated the cells for 30 min in the presence of AlamarBlue reagent ( Life Technologies/Invitrogen , 10% final concentration ) . Resazurin , the active ingredient of alamarBlue reagent , is a non-toxic , cell-permeable compound that is blue in color and virtually non-fluorescent . Upon entering cells , resazurin is reduced to resorufin , a compound that is red in color and highly fluorescent . Viable cells continuously convert resazurin to resorufin , increasing the overall fluorescence and color of the media surrounding cells . Fluorescence was measured at Ex540/Em590 nm on a SpectraMax Microplate Reader . Results were analyzed as per the manufacturer's instructions . HepG2 cells were incubated in the presence or absence of NFOH or BZL for 24 h , as above . For the quantitation of reactive oxygen species ( ROS ) , 5 µM CellROX Green reagent was added to each well in complete media , and cells were incubated at 37°C for 30 minutes . CellROX Green is cell-permeant and non-fluorescent , or very weakly fluorescent , in the reduced state . Upon oxidation , the reagents exhibit strong fluorescence and remain localized within the cell . Cells were washed with PBS and analyzed by flow cytometry . For assessing the effect of NFOH and BZL in inducing DNA damage , HepG2 cells were treated with NFOH or BZL for 24 h , as above . Cells were harvested , washed with PBS , fixed with 3 . 7% paraformaldehyde for 15 min at 4°C , and permeabilized with 90% methanol . Cells were then incubated at room temperature for 2 h with mouse anti-8-oxo-dG antibody ( 250-fold dilution , EMD Milipore , Billerica , MA ) and for 30 min with PE-conjugated , anti-mouse IgG ( eBioscience , San Diego , CA ) . Cells stained with isotype-matched IgGs were used as controls . Samples were visualized on an LSRII Fortessa Cell Analyzer , acquiring 30–50 , 000 events in a live cell gate , and further analysis performed by using FlowJo software ( version 7 . 6 . 5 , Tree-Star , San Carlo , CA ) . Frozen liver tissues ( 25 mg ) were homogenized in 0 . 5 ml of ice-cold lysis buffer ( 25 mM Tris pH 7 . 6 , 150 mM NaCl , 1% sodium deoxycholate , 1% Igepal CA-630 , 0 . 1% SDS , 10 µl/ml sodium orthovanadate , 10 mM PMSF and 10 µl/ml Sigma protease inhibitor cocktail ) , and centrifuged at 3000 g for 10 min at 4°C . Supernatants were stored at −80°C , and protein concentration determined by the Bradford method . The activities of glutamate oxaloacetate transaminase ( GOT ) and glutamate pyruvate transaminase ( GPT ) , alternatively called aspartate transaminase ( AST ) and alanine transaminase ( ALT ) , respectively , were determined by using commercially available assay kits ( Wiener Lab , Rosario , Argentina ) . Briefly , for the GOT/AST assay , 50 µl of sera sample or liver homogenate ( ∼100-µg protein ) was added to reagent A containing 12 mM 2-oxoglutarate , 0 . 18 mM NADH , 420 U/l malate dehydrogenase ( MDH ) , and 600 U/l lactate dehydrogenase ( LDH ) . The reaction was started by adding 80 mM Tris HCl buffer , pH 7 . 8 , containing 240 mM L-aspartate , and the resultant oxaloacetate formation coupled with NADH oxidation by MDH monitored at 340 nm . For the GPT/ALT assay , 50 µl of sample was added to reagent A containing NADH , 2-oxoglutarate ( as above ) and 1200 U/l LDH . The reaction was started by adding 80 mM Tris HCl buffer , pH 7 . 8 , containing 500 mM L-alanine , and resultant reduction of pyruvate coupled with NADH oxidation by LDH monitored at 340 nm ( ε = 6 , 220 M−1cm−1 ) . We measured lipid peroxides , a biomarker of oxidative stress [20] , by using a LPO Assay Kit ( Cayman ) . Briefly , liver homogenate LPOs were extracted into chloroform , mixed with methanol ( 1∶1 , v/v ) , and added in triplicate ( 55 µl/well ) to 96-well plates . The reaction was started with addition of 50 µl/well of 4 . 5 mM FeSO4/0 . 2 M HCl , 3% ammonium thiocyanate ( chromogen ) solution . The redox reaction with ferrous ions was stopped after 5 min , and absorbance monitored at 500 nm ( standard curve: 0–500 µM 13-hydroperoxy octadecadienoic acid ) . The level of protein nitrosylation , an indicator of nitrosative stress , was determined by Western blotting [21] . Tissue homogenates ( 10-µg protein ) were resolved on 10% SDS polyacrylamide gels , and transferred to PVDF membranes by using a vertical Criterion Blotter ( Bio-Rad ) . Membranes were washed in TBS ( 20 . 4 mM Tris , 150 mM NaCl , pH 7 . 6 ) , blocked for 1 h in 5% nonfat milk ( NFM ) , and incubated overnight at 4°C with anti-3-nitrotirosine antibody ( clone 2A8 . 2 , 1∶2000 , Millipore ) . After washing with TBS-T ( TBS/0 . 1% Tween-80 ) , membranes were incubated for 1 h at room temperature with HRP-conjugated secondary antibody ( 1∶10 , 000 , Southern Biotech ) , and signal was developed with an enhanced chemiluminiscence detection system ( GE-Healthcare ) . Membranes were incubated in stripping buffer ( Thermo Scientific ) and probed with anti-β-actin antibody ( 1∶10 , 000 , Sigma ) to confirm an equal loading of samples . All antibody dilutions were made in NFM . Spot densitometry for protein bands was carried out using a FluorChem HD2 Image Analyzer ( Alpha Innotech ) . MPO activity was determined as a biomarker of macrophage/neutrophil activation [20] . Liver homogenates ( 10 µg protein ) were added in triplicate to 0 . 53 mM o-dianisidine dihydrochloride and 0 . 15 mM H2O2 in 50 mM KH2PO4/K2HPO4 buffer ( pH 6 . 0 ) . After incubation for 5 min at room temperature , the reaction was stopped with 30% sodium azide , and the change in absorbance was measured at 460 nm . Sample protein content was measured by the Bradford Method , and 1 unit MPO was defined as that degrading 1 n mol H2O2/min at 25°C ( ε = 11300 M−1 . cm−1 ) . TNF-α levels were measured as a molecular marker of inflammation . Total RNA was isolated from frozen tissue sections by using the RNeasy plus Kit ( Qiagen ) , and analyzed for quality and quantity on a SpectraMax UV microplate reader . After reverse transcription of 2 µg RNA with poly ( dT ) 18 , first-strand cDNA was used as a template in a real-time PCR on an iCycler Thermal Cycler with SYBR-Green Supermix ( Bio-Rad ) and specific oligonucleotides for TNF-α ( 5′-GTT CTA TGG CCC AGA CCC TCA CA-3′ and 5′-TAC CAG GGT TTG ACC TCA GC-3′ ) and GAPDH ( 5′-TGG CAA AGT GGA GAT TGT TG-3′ and 5′-TTC AGC TCT GGG ATG ACC TT-3′ ) . The PCR Base Line Subtracted Curve Fit mode was applied for Threshold Cycle ( Ct ) and mRNA level measured by iCycler iQ Real-Time Detection Software ( Bio-Rad ) . The threshold cycle ( Ct ) values for target mRNA were normalized to GAPDH mRNA , and the relative expression level of TNF-α gene was calculated with the formula n-fold change = 2−ΔCt , where ΔCt represents Ct ( TNF-α ) −Ct ( GAPDH ) [22] . Tissue homogenates were also subjected to measurement of TNF-α cytokine by using an optEIA ELISA kit ( Pharmingen , San Diego , CA ) . Liver tissues were fixed in formalin , embedded in paraffin , and 5-µm sections were stained with hematoxylin and eosin ( H&E ) and Masson's Trichrome to examine inflammatory infiltrates and collagen deposition , respectively . Cryostat tissue-sections ( fixed in OCT cryostat-embedding medium , TissueTek ) were stained with Oil red O to examine lipid/fat deposition . In general , we analyzed each tissue section for >10-microscopic fields ( 100× magnification ) , and examined three different tissue sections/mouse ( n = 3–4 mice/group ) to obtain a semi-quantitative score . Presence of inflammatory cells was scored as I ( absent ) , II ( focal or mild , 0–1 foci ) , III ( moderate , ≥2 foci ) , IV ( extensive inflammatory foci , minimal necrosis , and retention of tissue integrity ) , and V ( diffused inflammation with severe tissue necrosis , interstitial edema , and loss of integrity ) . Inflammatory infiltrates were characterized as diffused or focal depending upon how closely the inflammatory cells were associated . Fibrosis and lipid deposition were assessed by measuring the Masson's Trichrome-stained collagen area ( blue ) and Oil Red O-stained intrahepatocyte lipid area ( red ) , respectively , as a percentage of the total area by using Simple PCI software ( version 6 . 0; Compix , Sewickley , PA ) connected to an Olympus polarizing microscope system ( Center Valley , PA ) . All pixels with blue stain in Masson's trichrome-stained sections and red stain in Oil Red O were selected to build a binary image , and utilized for calculating the percentage of the area occupied by collagen and lipid droplets , respectively . The fibrotic area was further scored as I ( <10% of total area ) , II–III ( 10–30% of total area ) , III–IV ( 30–60% of total area ) and V ( >60% of total area ) . Oil red O ( intrahepatocyte lipid deposition ) was scored as I ( absent ) , II ( <10% of total area or patchy distribution of tiny red droplets ) , III ( 10–30% of total area or scattered tiny red droplets ) , and IV ( >30% of total area or intense red staining of variable size droplets ) [23] . Data ( mean ± SD ) were derived from at least triplicate observations per sample ( n = 9–12 animals/group ) , confirmed to be normally distributed by a Q-Q test and histogram plot , and analyzed by Student's t test ( comparison of two-groups ) and 1-way analysis of variance ( ANOVA ) with a Holm-Sidak test ( comparison of multiple groups ) . Non-parametric Kruskal-Wallis Dunn's test was used to analyze the statistical significance for each cytokine's gene expression . The level of significance is presented by * ( normal versus treated; *p<0 . 05 , **p<0 . 01 ) .
We first evaluated the dose response of the HepG2 cell line to NFOH and BZL . Hepatocytes express cytochrome P450 isoforms , including Cyp2E1 that elicit ROS generation under stress conditions . Additionally , impairment of mitochondrial permeability transition , fatty acid β-oxidation , and inhibition of mitochondrial respiration are all potential mechanisms of ROS generation under stress conditions . We noted a 40–75% increase in CellRox fluorescence ( detects intracellular ROS , ( Fig . 1A . a ) in 27–72% ( Fig . 1A . b ) of the HepG2 cells treated with 5–100 µM NFOH . The maximal increase in ROS generation was observed when HepG2 cells were treated with 50 µM NFOH treatment . The 8-oxo-2′-deoxyguanosine ( 8-oxo-dG ) is the major product of DNA damage and concentrations of 8-oxo-dG within a cell are used as a measurement of oxidative stress . We noted an up to 33% increase in 8-oxo-dG levels ( Fig . 1B . a ) in 23% ( Fig . 1B . b ) of the HepG2 cells treated with increasing concentrations of NFOH . Despite the increase in ROS and DNA damage biomarkers , cell viability , measured by AlamarBlue assay , was not significantly altered by NFOH treatment for 24 h ( Fig . 1C . a ) or 48 h ( Fig . 1C . b ) . In comparison , HepG2 cells treated with increasing concentrations of BZL showed no statistically significant increase in ROS generation and DNA damage ( Fig . 1A&B ) ; however , a cytotoxic response to increasing concentrations of BZL was noted ( Fig . 1C ) . The maximal cytotoxicity ( 33% cell death ) was observed when cells were treated with 100 µM BZL for 24 h ( Fig . 1C . a ) or 48 h ( Fig . 1C . b ) . Together , these data suggest that NFOH ( 5–100 µM ) induces a mild stress response in hepatopcytes , while BZL at 100 µM concentrations is cytotoxic and causes cell death . We used a well-established experimental model of Swiss mice for assessing the in vivo cytotoxicity properties of NFOH . We have previously demonstrated NFOH activity against T . cruzi in Swiss mice [11] . These outbred mice display a broader response to drugs than is observed in in-bred C3H/HeN , C57BL/6 and Balb/c mice [19] . We measured elevation in GOT and GPT activities as a general biochemical marker of liver injury after 21 d ( ST ) and 60 d ( LT ) treatment with NFOH ( controls: vehicle solution ) . We observed no significant difference in GOT and GPT activities in the sera of mice treated with NFOH or vehicle for the ST and LT period ( Fig . 2B&C ) . Likewise , liver levels of GOT and GPT activities in mice treated with NFOH or vehicle for ST and LT were not statistically different , and comparable to those noted in normal controls ( Fig . 2D&E ) . In comparison , mice treated with BZL for LT exhibited a >5-fold ( p<0 . 05 ) increase in the sera levels of GPT activity and liver levels of GOT activity ( Fig . 2C&D ) . These data suggest that NFOH is not hepatotoxic , and its treatment for ST or LT is safe . In comparison , LT treatment with BZL was hepatotoxic and caused chronic cellular injury . The host defense response to NFOH or BZL can result in activation of macrophages and neutrophils that produce oxidative burst , nitric oxide ( •NO ) , and HOCl supported by induction of NADPH oxidase , inducible nitric oxide synthase ( iNOS ) [23]–[25] , and MPO [26] , respectively . The cytotoxicity of reactive oxygen species ( ROS ) results in oxidation of cell constituents , including proteins , lipids , and DNA , which lead to deterioration of cellular structure and function . Additionally , •NO reacts with O2•− and forms peroxynitrite ( ONOO− ) and peroxynitrous acid ( ONOOH ) that cause increased protein 3-nirotyrosine ( 3NT ) formation [20] , [23] , [27] . We investigated host defense responses to NFOH and BZL by measurement of MPO activity and oxidative/nitrosative stress . The level of MPO activity in liver homogenates of mice treated with NFOH for ST or LT was not statistically different when compared to that noted in mice given vehicle only , and was within the basal-level range ( 100–150 milli-units/mg protein ) . The BZL-treated mice exhibited a 20–40% increase in liver level of MPO activity when compared to that noted in NFOH-treated mice ( Fig . 3A ) . LPO refers to highly reactive hydroperoxides of saturated and unsaturated lipids , formed by oxidation [28] . NFOH and BZL treatment for ST or LT resulted in no significant increase in the liver levels of LPO formation in comparison to those in control mice treated with vehicle solution ( Fig . 3B ) . The basal level of LPO ( <0 . 25 n mol/mg protein ) in all mice , irrespective of ST or LT treatment with NFOH , BZL or vehicle was within the lowest detection range . The polypeptide-bound 3-NT residues , formed by peroxynitrite attack , were monitored by Western blotting . These data showed that the 3-NT level in liver homogenates of mice given ST NFOH or BZL treatment was increased by ∼2 . 5-fold when compared to those in normal controls , and were similar to those noted in mice given vehicle only ( Fig . 3C&D ) . The 3-NT contents normalized to β-actin were unchanged after LT exposure to NFOH , BZL , or vehicle in treated mice ( Fig . 3C&D ) . Overall , the data presented in Fig . 3 suggested that treatment of mice with NFOH or BZL caused a short-term increase in nitrosative stress that was likely a placebo effect , and , in general , both anti-parasitic drugs did not elicit long-term liver injury by phagocyte activation and oxidative damage in mice . Next , we determined the effects of NFOH and BZL treatment on liver inflammation . Mice treated with NFOH , BZL or vehicle for ST exhibited a 13–15-fold increase in TNF-α mRNA expression when compared to that noted in normal ( untreated ) controls ( Fig . 4A ) . The increase in TNF-α expression was also reflected by increased levels of TNF-α protein in liver homogenates of mice treated with NFOH , BZL or vehicle only for ST ( range: 58–74-pg/mg protein , Fig . 4B ) . When given for LT , the NFOH-induced increase in TNF-α mRNA and protein level was decreased by >4-fold , when compared to that noted after ST NFOH treatment and similar to that noted in controls ( Fig . 4A&B ) . Mice given BZL treatment for LT exhibited a 2-fold decline in TNF-α mRNA and a 30% increase in TNF-α protein level when compared to that noted after ST BZL treatment ( Fig . 4A&B ) . Histological studies showed that ST treatment with NFOH and BZL resulted in a mild increase in liver inflammatory infiltrate , extensive inflammatory lesions ( histological score: II–III ) being detected in BZL-treated mice followed by vehicle- and NFOH-treated mice ( Fig . 5A . a , c , e & 4B ) . Upon LT treatment , liver inflammatory lesions were noted to be in the order of BZL>NFOH ≥ vehicle . Focal lesions with 0–2 inflammatory foci per microscopic field ( histological score: II–IV ) were primarily noted in the livers of mice after LT treatment with NFOH or vehicle solution ( Fig . 5A . b , d & 5B ) . The LT treatment with BZL resulted in widespread inflammation in liver , evidenced by finding of >4-inflammatory foci/mf , extensive and diffused inflammation associated with severe tissue necrosis , interstitial edema , and loss of integrity ( histological score: III–V , Fig . 5A . f & 4B ) . Overall , the data presented in Figs . 4&5 suggested to us that NFOH , BZL and vehicle caused a short-term increase in liver levels of TNF-α and inflammatory infiltrate that was likely a placebo effect . When used for LT , NFOH was well-tolerated , while LT treatment with BZL resulted in extensive tissue inflammation in mice . ROS and inflammatory mediators have been suggested to promote tissue remodeling and dysfunction through diverse mechanisms [23] , [29] . We performed histological staining of the liver tissue sections with Masson's Trichrome and oil red O , respectively , for the detection of collagen ( Fig . 6 ) and lipid droplets ( Fig . 7 ) . Our data showed the ST with NFOH resulted in a mild degree of collagen deposition in <10% of the tissue area ( histological score: II , Fig . 6A . b & 6B , p<0 . 01 ) that was significantly lower than that noted in mice given vehicle or BZL treatment . The BZL-treated mice exhibited an up to 30% fibrotic area ( histological score: II–III ) in liver tissue ( Fig . 6A . c & 6B ) . Very few collagen lesions ( <10% fibrosis , histological score: 0–I ) indicative of liver remodeling were noted after LT treatment with NFOH , BZL or vehicle solution ( Fig . 6A . d–f & 6B ) . The extent of lipid deposition , an indicator of mitochondrial dysfunction , was not significantly different after ST treatment with NFOH , BZL or vehicle ( Fig . 7A . a–c & 7B ) . LT treatment with NFOH and BZL resulted in extensive and uniformly scattered lipid droplets of variable size in the liver tissue of mice ( 20–35% of total area; histological score III–IV ) than was noted in vehicle-treated mice ( Fig . 7A . d–f & 7B , p<0 . 01 ) . The extent of lipid deposition appeared to be high in NFOH-treated mice . Together , the results presented in Figs . 6&7 suggest that BZL resulted in mild-moderate acute remodeling of the liver that was replaced by extensive lipid deposition after LT treatment . In comparison , ST treatment with NFOH was not pro-fibrotic , and long-term treatment with NFOH resulted in minimal remodeling and a degree of metabolic dysfunction in the liver of treated mice .
BZL and NFX , drugs that provide the only line of therapy against acute T . cruzi infection , were released to the market without extensive testing for possible adverse effects , which have been reported over the four decades that these drugs have been in use . It was confirmed that NFX has more severe secondary effects than does BZL; these range from alterations of the cellular immune responses [30] , peripheral nervous system toxicity , and testicular and ovarian damage to mutagenic effects [17] , [31]–[34] . In clinical practice , it is recommended to interrupt anti-T . cruzi treatment when the adverse effects of BZL are detected in adult patients [35] . In contrast , children and newborns show a better tolerance to BZL [36] , [37] . Different strategies with the overall aim of finding a cure for Chagas disease are currently under investigation . The major challenges to testing and implementation of therapeutic use of the currently available drugs ( BZL and NFX ) include the resistance of many of the naturally occurring parasite isolates ( e . g . Colombiana ) [9] and low efficacy of treatment during indeterminate and chronic phase of disease [6]–[9] . Moreover , additional challenges are faced in immuno-suppressed chagasic patients that become recipients of transplanted organs or are HIV co-infected . The immuno-suppressed patients present a short window of time when they should be treated with anti-parasite drugs . Otherwise , parasite recurrence results in severe acute infection and organ failure . Due to high toxicity concerns , BZL and NFX are not always recommended for treatment of immuno-suppressed patients [38] . In this scenario , NFOH has emerged as a promising compound for its anti-T . cruzi activity , both in vitro and in vivo , and its favorable pharmacological properties [39] , [40] . NFOH , derived from hydroxymethyl substitution at the primary amide of nitrofurazone [15] , also has a higher solubility in water than does NF and BZL , which likely would facilitate its oral administration . In a murine model of acute T . cruzi infection , NFOH was highly effective in controlling parasitemia evidenced by the observation that infected mice , after NFOH treatment , exhibited no signal for parasite DNA by a highly sensitive PCR approach , and sero-converted with depletion of anti-parasite antibodies [11] . NFOH is a derivative of nitrofurazone ( NF ) . NF is highly toxic and shown to result in single strand DNA breaks [41] and oxidative DNA damage [42] , and is considered to be potentially carcinogenic [13] . Considering the high toxicity of NF , it is important to evaluate the toxicity of NFOH in vitro and in vivo before it is recommended for treatment of T . cruzi infection in humans . Accordingly , the present study was designed to examine the adverse effects of NFOH treatment in HepG2 cells and ST and LT treatment of NFOH in mice . We focused on examining the effects of NFOH on hepatocytes and liver because the liver is the main detoxifying organ in mammals . Metabolism of xenobiotics in the liver involves phase I and phase II reactions that add hydroxyl and methyl groups , respectively , to a given compound . NFOH is a nitrofurazone with an N-hydroxymethylation at the primary amide and was anticipated to cause significantly reduced toxicity [11] . Our in vitro studies evaluating the dose response of HepG2 cells clearly demonstrated that NFOH at higher concentrations ( 50–100 µM ) induced mild stress as was evidenced by the observation of ROS production and DNA damage ( Fig . 1A&B ) . However , the NFOH-induced stress was controlled as we observed no cell death in NFOH-treated HepG2 cells . Under similar conditions , cytotoxicity of BZL was evidenced by induction of cell death in 33% of the cells ( Fig . 1C ) . Others have shown the nifurtimox and BZL inhibited DNA and protein synthesis in hepatocytes [43] . In murine studies , the selected doses for toxicity evaluation were the same as those that we have previously tested in mouse models of acute and chronic T . cruzi infection . We included mice treated with BZL and vehicle ( NaCl/Tween-80 ) as controls . Our data showed a moderate increase in sera levels of GOT and GPT ( Fig . 1 ) after ST and LT treatment with NFOH that was similar to that noted in mice treated with vehicle only . Further , MPO activity and LPO production , measured as markers of neutrophil activation and macrophage oxidative burst , were present at the lowest limits of detection in all mice given NFOH or vehicle throughout the treatment schedule ( Fig . 3A&B ) . These data , along with the observation of no significant change in TNFα mRNA and protein level ( Fig . 4 ) suggested to us that NFOH did not induce hepatic stress associated with cellular injury , oxidative stress , and innate immune cell activation in vitro or in vivo after ST or LT treatment . Our observation of a >5-fold increase in hepatic levels of MPO activity and TNF-α expression in the livers of mice within 3 d after a single dose treatment with 2 , 3 , 7 , 8-tetrachlorodibenzo-p-dioxin ( TCDD , 20-µg/Kg/100 µl peanut oil ) in other studies suggest that when compared with TCDD effects , NFOH has no acute liver toxicity . The notion of NFOH being liver-safe is also supported by the observation of a higher degree of GOT release ( 5-fold ) , MPO activity ( 20–40% ) , and TNF-α protein levels ( up to 20% ) in BZL-treated mice in this study ( Figs . 2–4 ) . Others have also shown the BZL toxicity by alterations in mitochondrial function in liver of treated rats [44] . We corroborated the biochemical findings ( Figs . 1–4 ) of NFOH safety by histological observations of tissue inflammatory infiltrate , fibrosis , and lipid deposition in the livers of mice treated for ST or LT with NFOH , and compared the findings with BZL-treated mice ( Figs . 5–7 ) . NFOH-treated mice consistently exhibited none-to-low levels of inflammatory infiltrate and fibrotic lesions in liver tissue sections that were similar to or lower than was noted in vehicle-treated mice ( Figs . 5–7 ) . The extent of liver inflammatory infiltrate was significantly higher in BZL-treated mice , especially after LT treatment ( Figs . 5 ) . Likewise , BZL-treated mice exhibited an acute liver fibrosis ( Fig . 6 ) similar to what we have noted in TCDD-treated mice ( unpublished data ) . The extent of lipid deposition in liver tissue after LT treatment with BZL and NFOH was comparable to lipid deposits provoked within three days after treatment with a single dose of TCDD . Though not withdrawn from market , BZL has been demonstrated to be more toxic than NFOH with regard to elicitation of liver inflammatory responses , fibrosis , and a cellular dysfunction at mitochondrial level , both in our data presented in this study and other published reports [17] , [33] . We surmise that NFOH only causes a mild transient hepatic injury similar to that caused by vehicle treatment only in mice . Our results encourage further research on carcinogenicity , and mechanism of action of NFOH to address its potential safety for human use as an anti-parasite therapy . In conclusion , our results showed that ST and LT treatment with NFOH elicited similar or lower levels of liver oxidative stress , inflammation and tissue remodeling responses , when compared to that noted by a similar regimen of treatment with vehicle only . In comparison , BZL that has been used for the treatment of human chagasic patients was more toxic and induced chronic inflammation and liver injury . Our data provide the impetus for future studies focusing on further characterization of anti-parasite efficacy , toxicity , and carcinogenicity of NFOH , aiming to determine its potential safety to be considered as a drug candidate for the treatment of Chagas disease . | Hydroxymethylnitrofurazone ( NFOH ) is a promising drug candidate with demonstrated trypanocidal activity in experimental models of Trypanosoma cruzi infection and chronic disease development . In this study , we monitored the safety of NFOH in established in vitro and in vivo models . Our data show that NFOH did not induce hepatocyte cell death . Short-term or long-term treatment of mice with NFOH did not induce hepatic stress measured by cellular injury , inflammation or fibrosis . Benznidazole , the currently used treatment against acute infection in humans , was more toxic and induced chronic inflammation and liver injury in mice . We conclude that NFOH should be studied further to determine its potential safety for human use as an anti-parasite therapy . | [
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"par... | 2014 | Hepatotoxicity in Mice of a Novel Anti-parasite Drug Candidate Hydroxymethylnitrofurazone: A Comparison with Benznidazole |
After extra-cellular stimulation of G-Protein Coupled Receptors ( GPCRs ) , GDP/GTP exchange appears as the key , rate limiting step of the intracellular activation cycle of heterotrimeric G-proteins . Despite the availability of a large number of X-ray structures , the mechanism of GDP release out of heterotrimeric G-proteins still remains unknown at the molecular level . Starting from the available X-ray structure , extensive unconstrained/constrained molecular dynamics simulations were performed on the complete membrane-anchored Gi heterotrimer complexed to GDP , for a total simulation time overcoming 500 ns . By combining Targeted Molecular Dynamics ( TMD ) and free energy profiles reconstruction by umbrella sampling , our data suggest that the release of GDP was much more favored on its phosphate side . Interestingly , upon the forced extraction of GDP on this side , the whole protein encountered large , collective motions in perfect agreement with those we described previously including a domain to domain motion between the two ras-like and helical sub-domains of Gα .
In the intracellular compartment , activation of membrane-anchored heterotrimeric G-proteins involves an exchange between GDP and GTP molecules in the Gα subunit . This rapid exchange promotes the dissociation of Gα from Gβγ [1] . GDP release out of Gα is “catalyzed” by the direct interaction of the whole heterotrimer with an activated G-protein Coupled Receptor ( GPCR ) and appears as the rate limiting step [2] . This interaction mainly involves the C-terminal helix of Gα as shown by biochemical and structural data [3] , [4] . Although many X-ray structures are now available in the Protein Data Bank that describe either G-proteins [5] , [6] , GPCRs [7] , [8] , or more recently , their putative interactions [9] , the possible mechanism of GDP release still remains unknown at the molecular level . Among other unsolved questions , the exit side of GDP is still debated . Indeed , the GDP ligand lies at the interface between the two ras-like and helical sub-domains of Gα ( see Figure 1A ) ; addition of hydrogen atoms lacking in the X-ray structure results in a GDP solvent accessible surface of only 2 . 60 Å2 or18 . 32 Å2 on the base or phosphate sides , respectively ( representing only ∼4% of the maximum SASA of the GDP , which is 550 Å2 ) . A simple visual inspection of the conserved fold of heterotrimeric G-proteins thus suggests two possible exit pathways , either on the base or on the phosphate sides . Recently , the X-Ray structure of the complex between Gsαβγ and the beta-2 adrenergic receptor brought some elements on the GDP-free state of an heterotrimeric G-protein in complex with a GPCR [9] . In this structure , the inter-domain interface was surprisingly completely open , due to a large rigid-body rotation of ∼130° of the helical sub-domain of Gsα . As reported recently , this high flexibility appears only in the absence of nucleotide whereas the presence of GDP or GTP favors the stabilization of the α-helical domain on the ras-like domain of Gsα [10] . In this study we were interested in the unbinding process of GDP from the Giαβγ complex by using extensive molecular dynamics ( MD ) simulations and reconstruction of free energy profiles along the different putative exit pathways , for a total simulation time overcoming 500 ns ( Figure 2 ) . The inactive Giαβγ , GDP bound complex ( PDB:1GP2 ) [5] was equilibrated through a first unconstrained MD trajectory of 40 ns . The ending point of this first simulation was then used to extract the GDP out from its initial position , toward four different directions , by using Targeted Molecular Dynamics ( TMD ) simulations [11] . It was concluded that this method was successful in generating highly diverse exit pathways for the ligand , as reported in Figure 1B . For each extraction pathway , about 25 intermediate positions of the GDP were further selected and used as starting points for 0 . 5 ns constrained MD , allowing the reconstruction of free energy profiles using the WHAM algorithm [12] . The sampling of the system was good as proven by a quasi-harmonic analysis of all concatenated data which reproduced the intrinsic , large collective motions we described previously for the same complex [13] . Interestingly , our calculations supported a much easier extraction of the GDP on the phosphate side . Moreover , the forced extraction of GDP on this side promoted large amplitude motions of the protein that were in close agreement with those we described previously as putatively involved in GDP release [13] .
The PDB:1GP2 structure was anchored to the membrane as described previously [13] and was subjected to a first all-atoms 40 ns unconstrained Molecular Dynamics ( MD ) trajectory . At the end of this simulation , we concluded to a significant increase of the GDP total Solvent Accessible Surface Area from 4% to 10% ( ∼50 Å2 ) . This increase was particularly due to some water molecules entering into the binding pocket . This was not surprising as many water molecules were observed in this pocket among available X-ray structures . The GDP position itself was subjected to significant rearrangements . Among these modifications , the ligand lied down into the pocket , but still conserved its main interactions with surrounding residues . In particular , the Phosphate loop ( P-loop ) was slightly translated , Glu43 establishing a closer interaction with Arg178 . In the same time , Asn149 and Asp150 lying at the C-terminus of helix E were also re-oriented , the former inducing a direct interaction with the sugar 3′ hydroxyl of GDP . This was in agreement with the high B-factors observed for the side-chains of these two residues in the PDB:1GP2 X-ray structure . We also observed the formation of an additional helix-turn for helix E , as it can be guessed after a structural alignment of all available Gα X-ray structures . Compared to its starting position , Ser47 was reoriented toward the α-phosphate of GDP , so forming an additional interaction that was not seen in the initial X-ray structure . The last significant change concerned the purine base of GDP , which was significantly rotated ( ∼45° ) compared to the starting model , breaking its interaction with Asp272 , replaced by water molecules . Quantitatively , the Root Mean Square Deviation ( RMSD ) computed on GDP atoms between its initial and final positions was 1 . 8 Å , whereas the RMSD computed on surrounding residues in a sphere of 5 Å was 2 . 5 Å . This significant change of orientation of the GDP ligand could also have been expected from its high B-factors in many available X-ray structures . Finally , the computed energy of interaction between the GDP and its surrounding residues was more important at the end of the trajectory than in the starting X-ray structure ( −873 kcal . mol−1 versus −424 kcal . mol−1 ) indicating that the observed rearrangements were energetically favorable . Importantly , following of the RMSDs computed either for the protein , the GDP or the GDP binding pocket also argued for a properly equilibrated model at the end of this trajectory . To extract the GDP ligand out from its binding pocket , we first used Targeted Molecular Dynamics ( TMD ) simulations [11] . These simulations helped us to generate a large set of intermediate positions of the GDP along different putative exit pathways . Because experimental data were lacking concerning these putative exit pathways of GDP , many possible directions were tested ( See Figure 1B ) . Initially , we expected to perform a clustering analysis on protein atoms to select different possible starting points . However , after equilibration , variations of the RMSD of the protein was only 1 Å all along the trajectory , thus preventing the selection of significantly different starting conformations . Accordingly , the ending conformation of the 40 ns MD was used as a starting point in each case . The final positions of GDP that were arbitrary chosen to drive the ligand from its bound to unbound states are reported in Figure 1B ( blue , red , green and yellow spheres ) . In TMD simulations , the movement of the ligand was directly driven by the RMSD difference between its successively observed positions and its final targeted one . Thus , because an identical value of RMSD should correspond to different positions of the ligand , the explored pathways could be significantly different among TMDs , especially when a low force constant of 0 . 5 kcal . mol−1 . Å−2 . atom−1 was used as it is the case in the present study . This permitted to generate highly diverse pathways for the GDP . At this step , three groups of pathways were clearly identified . The red group of pathways corresponded to an exit of GDP along its base side . The blue group of pathways corresponded to an exit of GDP along the phosphate side . Yellow and green pathways were together forming a subgroup also directing towards the phosphate side ( Figure 1B ) . At the beginning of this study , it was expected that variations of the force applied to the ligand might be sufficient to reflect the most significant features along the different explored pathways . Unfortunately , because the ligand was strongly bound to its pocket , this force was observed , in all cases , as continuously increasing in the first stage of each simulation until reaching a quite high value of 300 kcal . mol−1 , necessary to dislocate the GDP ( see Figure 2 ) . This force was particularly high for yellow and green pathways that required more important conformational changes of the protein . This unexpected behavior both led unfortunately to meaningless force profiles and to a discontinuity in the time spend in each successive region along the explored pathways . In agreement , no detailed analysis was possible on these simulations . Nevertheless , to qualitatively determine the most favorable exit pathways for the GDP , we then used umbrella sampling . A set of ∼25 intermediate positions of the GDP was selected along each TMD trajectory . This was achieved by measuring the distance of GDP to the center of mass of its binding pocket as described in the methods section . Before going further in the study , we carefully verified that these positions were properly distributed along each trajectory . The Weighted Histogram Analysis Method ( WHAM ) [12] was used to compute free energy profiles along each of the TMD simulations . In this case , the method was used to describe the easiness/difficulty for the GDP to stay in each of its selected intermediate positions along each of the putative extraction pathways . A WHAM is based on the respect or not of an applied harmonic constraint . In our case , this constraint was a distance between the center of mass of the GDP binding pocket and either the N2 ( base side: red pathways in Figure 1B ) or P1 ( phosphate side: blue , yellow and green pathways in Figure 1B ) atoms of the ligand . After different trials , a low constant value of 10 kcal . mol−1 . Å−2 was selected for the applied force . Because of the upper explicated discontinuities along the different extraction pathways , some other intermediate points were added artificially by applying a slightly different constraint to the closest available points . After this step , we verified that two successive constrained positions of the GDP were at a maximal distance difference of 0 . 5 Å , so recreating a continuity of the data . All the so-built initial conformations were used for constrained molecular dynamics simulations , each lasting 0 . 5 ns . It was then verified that the chosen points led to a proper covering of the final histograms for the subsequent WHAM ( see Figure S1 , Figure S2 and Figure S3 ) . The free energy profiles resulting from the WHAM were reported in Figure 3 . As a first point , and in agreement with our observations described previously , it was concluded that the obtained conformation at 40 ns was located in a potential depth as compared to the X-ray crystallographic structure . More significantly , the results clearly indicated a preferred exit on the phosphate side ( blue and yellow+green profiles ) whereas an exit on the base side was qualitatively more difficult ( red profiles ) . Interestingly , the three best profiles ( blue 1+3 and green 3 ) corresponded to the phosphate side and to a predicted free energy cost of about +15 kcal . mol−1 in good agreement with the dissociation constant of 0 . 2 µM measured on the Gi protein at ambient temperature ( ∼10 kcal . mol−1 ) [14] . To better visualize the interactions established between the GDP and its environment , a list of residues was further built that included all the protein residues lying at a maximum distance of 4 Å of the GDP , as observed along all the explored exit pathways . Non-bonded ( Van Der Waals and electrostatic ) energies were then re-computed for each GDP:protein residue pair with NAMD [15] . Strong negative energy of interaction was reported in green , whereas a positive energy ( including charge:charge repulsions ) was reported in red . Results of this analysis can be seen in Figure 4 . For more clarity , only three representative interaction energy profiles were reported in this figure that corresponded to the best profiles obtained for each of the blue , green+yellow , or red groups of pathways , respectively . Importantly , it was verified that these profiles of interactions were well representative of all the other computed energy patterns from other trajectories , with correlation coefficients of ∼0 . 9+−0 . 05 ( blue pathways ) , ∼0 . 9+−0 . 05 ( red pathways ) and ∼0 . 75+−0 . 14 ( green+yellow pathways ) . These high correlation values nicely confirmed that the chosen exit pathways for GDP corresponded to highly conserved interactions , even if the final free energy profiles shared some discrepancies . Subsequent analyses of these profiles further allowed to extract the most significant events leading to the GDP exit out from its initial binding pocket along these three groups of pathways . In agreement with the corresponding free energy profiles , an increased number of strong “red” , non favorable interactions was observed on the base side ( red group of pathways , Figure 4C ) . The exit of GDP was first characterized by the loss of some favorable interactions with residues Gly42 , Ser44 , Gly45 , Lys46 , Ser47 and Lys51 , at a distance of ∼15 Å from the center of mass . These residues all belong to the helix 1 or P-loop of Gα . In this protein region , a strong repulsion between Glu43 and the Phosphate moiety of GDP was also noticed . Strong interactions were also established with Asn149 and Ser151 , involving successively the sugar and then the phosphate moieties of GDP . The change from “good” to “bad” interaction involving Asp150 corresponded to a shift of its interaction with the base to the phosphate moiety of GDP . Interactions with Arg178 and Lys180 were maintained a long time , especially for Arg178 which interacted successively with the α- and then with the β-phosphate of the ligand . At the end of the trajectories , another “good” interaction was created with Arg176 . Nevertheless , three additional non favorable interactions were evidenced , first with Asp200 in the first stage of the trajectories that reflected a repulsion with the phosphates , and also with the two Asp229 and Asp231 residues at the end of the simulations . Ser143 , Arg242 , Lys270 , Ile278 and Lys280 played a more favorable role , all along the trajectories . Finally , it was observed that the Glu276 was also a main contributor to the “bad” free energy profiles observed on the base side . On the phosphate side , including blue and green+yellow pathways , interactions were quite conserved as depicted in Figure 4 . Indeed , the mean correlation computed between blue and green+yellow profiles of interactions was 0 . 58+−0 . 2 whereas the same computed between blue and red or red and green+yellow was 0 . 47+−0 . 05 or 0 . 47+−0 . 1 , respectively . On the phosphate side , strong repulsions were only observed with Glu43 and Asp200 , at the beginning of simulations . Another repulsion was noted for Ala226 which turned to a “good” interaction at the end of blue profiles . No other strong repulsion was noted on the exit route of GDP . As observed on the base side , strong favorable interactions with Gly42 , Ser44 , Gly45 , Lys46 , Ser47 and Lys51 were quickly lost . Repulsion with Glu43 was less significant and even converted into a favorable interaction in the middle of the simulations when the sugar and the base moieties came in close contact . The most favorable interactions included residues Arg86 , Arg178 , Lys180 and Arg205 driving the exit of the ligand . Other significant contributions were noticed for residues 147 to 150 located at the N-terminus of Helix αE . The role of the two Glu43 and Arg178 residues was particularly interesting as they were strongly interacting during the entire trajectory of 40-ns MD . We observed that extraction of GDP along the phosphate side irremediably led to the breaking of this interaction , thus promoting the separation of the Switch I from the P-loop segment . This separation that could have been expected from a direct visualization of available X-ray data , was not observed during the extraction of GDP along the base side . Interestingly , some site-directed mutagenesis studies have already shown the importance of some of the upper mentioned residues in GDP release . Among them , an R178M mutant was shown to increase GDP release by 10-fold [16] . S43N mutation in Gtα ( Ser47 ) also increased GDP release [17] . Other candidates could be easily proposed from our calculations , including residues that are not in contact with GDP in the known X-ray structures , but located on its putative exit route . Using normal modes calculations in vacuo performed on the whole heterotrimeric protein , we previously proposed that the unbinding of GDP might require ( or promote ) large , collective motions of the protein [13] . The involved mode ( mode 17 ) described an inter-domain motion intrinsic to the Gα subunit , leading to the partial opening of the GDP binding pocket . To confirm or not the importance of such a motion in the release of GDP , we performed Essential Dynamics Analyses ( EDA ) on our data from TMD simulations . Such type of analysis is usually used to capture the large , collective motions of the system from a single or concatenated MD trajectory [18] . Four ensembles of trajectories were first built by concatenation of the data , namely TMDB ( TMD data , blue group of pathways , ∼60 ns ) , TMDYG ( TMD data , yellow+green groups of pathways , ∼70 ns ) , and TMDR ( TMD data , red group of pathways , ∼60 ns ) . After a Principal Component Analysis ( PCA ) of the backbone coordinates , only the fifty lowest frequency quasi-modes were retained for further analysis . All the motions described by the obtained quasi-modes were then individually compared to the 20 lowest frequencies “true” normal modes we described previously for the 1GP2 crystallographic structure [13] , including the expected mode 17 . Importantly , the same forces field was used in both studies . These comparisons were performed through the computation of displacement matrices and correlation coefficients as previously described [13] , [19] , [20] . We remember here that using this criterion , a correlation of 0 . 6 corresponds to two highly closely related motions in the cartesian space . First , it was concluded that pulling the GDP on the base side ( TMDR ) led to no significant correlation coefficient ( <0 . 5 ) between the deduced quasi-modes and any of the previously described NMs . Similar results were concluded after analysis of the TMDYG data . On the contrary , the mode 17 was retrieved with a high correlation coefficient of 0 . 7 when using the data from TMDB ( Quasi-mode number 11 ) thus confirming its putative role in the Gi heterotrimer activation and GDP release [13] . This motion depicted in Figure 5 corresponded to a concerted motion involving especially the C-terminus , the α4 and the αG helices , as well as the whole helical domain of Gα . Interestingly , these intrinsic motions also described a separation of the two ras-like and helical sub-domains of Gα , as strongly suggested by recent experimental observations [10] , [21] . Importantly , this motion was not retrieved when performing an EDA of the initial unconstrained MD trajectory of 40 ns , so strongly suggesting that it was induced by the exit of GDP from its initial position .
Obviously G-proteins X-ray structures are available for a long time , the fine mechanism of GDP release at the molecular level still remains unknown . Because it constitutes the rate limiting step for G-proteins activation , it is of crucial interest . In this study , we argue for a GDP release on the phosphate side of the ligand . Key residues involved in this release have been identified , some of them having been already delineated by previously published experimental studies . Other mutants , able to either promote or block the GDP release , should easily be proposed based on our computational results , including Arg205 and other residues located on the N-terminus region of Helix αE . To extend these analyses , closely related calculations on other G-proteins should be performed , notably to explain discrepancies in their GDP release rates . Our results also argue for a steric effect of the Go-Loco peptide of RGS14 ( PDB:1KJY ) , which inhibits GDP release through its binding to the Gα subunit [22] . In precedent studies , it was rather suggested that this peptide might block the GDP release by affecting the inter-domain dynamics of Gα . Interestingly , and despite the fact that it is located on the putative phosphate exit route , the positioning of the Go-Loco peptide was completed by a reorientation of the Switch I region , an outward movement of the Lys180 residue , and by a direct interaction with Asn149 , two residues we pointed out as very significant for the GDP release . The importance of Switch I and of its conformational change during the G-protein activation cycle shown is this study was also previously suggested by experiments [23] . Here , we were especially interested by GDP release . Another question logically raised concerning the GTP binding . The accomplishment of such a study related to GTP binding to the G-protein heterotrimer would be more problematic because of the choice of the starting structure . Indeed , as demonstrated recently , the inter-domain dynamics of Gα is more significant in the absence of the nucleotide [10] , [21] . In agreement , some parts of the X-ray structure that describes an unbound Gα subunit ( PDB:3SN6 ) would require to be rebuilt , because of some putative crystallization artifacts [9] . It would also be of interest to understand the exact role of the GDP in the stabilization of the Gα ( ras ) :Gα ( helical ) sub-domains interactions . This study showed how the GDP release on its phosphate side could induce large , collective motions of the whole heterotrimer as shown by Essential Dynamics analyses . Furthermore , this motion was supported by our previous findings [13] and guessed to be involved in G-protein heterotrimeric activation . These motions are also thought to be promoted by the molecular interactions between the G-protein itself and the GPCR , in part through the α4 helix of the Gα sub-unit and the ICL3 region of the receptor . Some calculations are actually under progress to understand how the activation of GPCRs could promote G-proteins conformational changes and the subsequent GDP release .
The 40-ns unconstrained molecular dynamics ( MD ) simulation was performed on the whole Gi heterotrimer starting with the PDB:1GP2 crystallographic structure [5] . The protein was first inserted into a membrane model composed of 406 1-palmitoyl-2-oleyl-phosphatidyl-choline ( POPC ) lipids . This insertion was performed through the construction of three lipid-modified anchoring residues on Gα-Gly2 ( myristoyl ) , Gα-Cys3 ( palmitoyl ) , and Gγ-Cys68 ( geranylgeranyl ) . Anchoring residues were preliminary built with the Antechamber and the General Amber Forces field [24] , [25] . For the rest of the system , the CHARMM forces field was used including CMAP corrections [26] . The system was solvated in each z-direction with TIP3P water molecules [27] . 17 sodium ions were finally added to fully neutralize the system , allowing the use of the particle mesh Ewald method [28] for the computation of electrostatic interactions . A switching function was applied to Van Der Waals interactions in the range 10–12 Å . The NPT ensemble was used ( 1 . 013 bars and 298 K ) with Langevin dynamics and a Nosé–Hoover–Langevin piston pressure control . The integration step was set to 1 fs . Interactive molecular dynamics , using NAMD [15] and VMD [29] , was used to slowly insert the anchoring residues into the membrane . After a minimization through 5 . 000 steps of conjugate gradient , the whole system was then equilibrated during a first stage of 2ns MD simulation during which the protein atoms were kept fixed . In a second stage , all constraints were removed , and the simulation was pursued until reaching 40 ns . In TMD simulations [11] , the force applied to each of the N atoms of the pulled ligand is of the form: ( 1 ) where k is a force constant given in kcal . mol−1 . Å−2 and RMSDobs ( t ) and RMSDtarg ( t ) the observed and targeted values of RMSD at a given time step t , respectively . RMSDs were computed on the entire set of the N ligand atoms . TMD simulations were performed starting from the ending conformation of the 40 ns MD simulation . The used parameters were strictly identical , except for the random seed number generator that was changed to generate different initial velocities . The final targeted positions of the ligand were built by translating and/or rotating the GDP out of its binding pocket , in arbitrary chosen positions , either on the phosphate or on the base sides ( see Figure 1B ) . To avoid translation of the whole protein during TMD simulations , the Cα of the four Glu43 , Gly45 , Thr48 and Arg178 residues of Gα were also subjected to the TMD force . Except these residues and GDP , all the remaining atoms of the system were kept free of any constraint . It was concluded that behaviors of residues with constrained Cα were not disturbed as their Root Mean Square Fluctuations ( RMSFs ) were similar to those observed for the 40 ns MD simulation . The applied force constant k was set to 20 kcal . mol−1 . Å−2 ( 0 . 5 kcal . mol−1 . Å−2 per atom ) , a low value in the range of that described in other recent published TMD studies [30] , [31] . Each TMD simulation lasted for about 8 to 10 ns , corresponding to a linear decrease in the targeted RMSD of ∼2 . 5 . 10−3 Å . ps−1 . It was checked that the GDP ligand was out of the protein at the end of each TMD simulation . TMD simulations were used to generate intermediate positions of the GDP along different possible exit pathways . These positions were further used to perform umbrella sampling simulations and Weighted Histogram Analyses [12] of the obtained data . The harmonic constraint necessary for the umbrella sampling simulations was defined as a distance between the center of mass of the GDP binding pocket , and either the N2 or P1 atoms of GDP ( base or phosphate side ) . The center of mass ( COM ) of the GDP pocket was computed from the Cα coordinates of residues lying at a maximal distance of 4 Å to the GDP in the initial X-ray crystallographic structure . The harmonic potential used to constrain the GDP at successive distances deq from the COM was reported in Eq . 2: ( 2 ) where the force constant k was set to 10 kcal . mol−1 . Å−2 . Along each TMD , intermediate positions were extracted assuming a minimal increment of 0 . 5 Å for two successive GDP:COM distances . However , because of the TMD method , this criterion was not always sufficient to get a good coverage of the whole trajectory . To supplement these data , we performed additional simulations by starting from the closest position of GDP and setting up the distance constraint to an intermediate value . To ensure that the GDP fully explored the desired intermediary positions , the force constant k was increased to 20 kcal . mol−1 . Å−2 . All the obtained starting points were further subjected to a constrained MD simulation of 0 . 5 ns , once more using the same simulation parameters than for the 40 ns or TMD simulations . For analyses of the obtained data , the WHAM algorithm [12] was used as implemented in the code distributed by Grossfield ( http://membrane . urmc . rochester . edu/wham/ ) . Quasi-modes were derived from the molecular dynamics simulations after computation of the covariance matrices of the mass weighted Cartesian coordinates . In each performed analysis , only the backbone atoms were considered and a total of 50 quasi-modes were extracted with CHARMM . Quasi-modes were then compared to the “true” all-atoms normal modes we described previously [13] and computed with the same forces field as that used in the present study . The direct comparisons of motions described by either modes or quasi-modes were performed by using displacement matrices as previously explained [13] . Briefly , the method consisted to compute 2D matrices that reflected the relative displacements of all pairs of Cα atoms between two structures . For each considered mode , these matrices were computed from the two structures obtained at a displacement amplitude of +1 Å in each direction . Correlation coefficients between two matrices were then computed with the Mantel test [32] . With this method , it was assumed that two 2D maps sharing a correlation coefficient greater than 0 . 5 described highly related motions in the Cartesian Space . | Despite the availability of many structural and biochemical data , the activation of G-proteins remains to be understood at the molecular level . We used a computation tool to decipher the first limiting step of this activation: GDP release . Combining different methods of analysis , we propose that the GDP exit occurs on its phosphate side . This study helped to rationalize some experimental observations from the literature and opens many perspectives concerning the study of G-proteins activation and their putative inhibition . | [
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Dengue fever is endemic in Malaysia , with frequent major outbreaks in urban areas . The major control strategy relies on health promotional campaigns aimed at encouraging people to reduce mosquito breeding sites close to people's homes . However , such campaigns have not always been 100% effective . The concept of self-efficacy is an area of increasing research interest in understanding how health promotion can be most effective . This paper reports on a study of the impact of self-efficacy on dengue knowledge and dengue preventive behaviour . We recruited 280 adults from 27 post-outbreak villages in the state of Terengganu , east coast of Malaysia . Measures of health promotion and educational intervention activities and types of communication during outbreak , level of dengue knowledge , level and strength of self-efficacy and dengue preventive behaviour were obtained via face-to-face interviews and questionnaires . A structural equation model was tested and fitted the data well ( χ2 = 71 . 659 , df = 40 , p = 0 . 002 , RMSEA = 0 . 053 , CFI = 0 . 973 , TLI = 0 . 963 ) . Mass media , local contact and direct information-giving sessions significantly predicted level of knowledge of dengue . Level and strength of self-efficacy fully mediated the relationship between knowledge of dengue and dengue preventive behaviours . Strength of self-efficacy acted as partial mediator in the relationship between knowledge of dengue and dengue preventive behaviours . To control and prevent dengue outbreaks by behavioural measures , health promotion and educational interventions during outbreaks should now focus on those approaches that are most likely to increase the level and strength of self-efficacy .
Dengue fever is transmitted by the bite of an Aedes mosquito infected with any one of the four dengue viruses . Although most infections are self-limiting a proportion of cases develop severe complications such as dengue haemorrhagic fever which can carry a significant risk of death . The incidence of dengue has risen dramatically around the world in recent decades . Since no vaccine is currently available , primary prevention is regarded as the most effective measure in controlling dengue . Each time an outbreak occurs , the local health authority will plan and carry out various types of promotional and educational activities that aim to increase knowledge of dengue and change dengue preventive behaviour among communities at the centre of the outbreak . These promotional activities can be carried out through various methods such as individual home visits , or at the population level through the mass media . Health promotion and educational intervention like , ‘search and destroy’ activities , advice on the need to seek immediate medical attention in patients with fever , and proper disposal of rubbish are usually the focus of behavioural-change promotion activities . The promotional and educational messages are usually delivered using small group discussion , public lecture , live public announcement , demonstration , distributing printed materials , putting up posters , bunting and billboards , community source reduction and community dengue-cleanliness program ( in Malay: Gotong-Royong ) and health exhibition [1] . There have been a number of systematic reviews of public health interventions aimed at reducing the risk of dengue fever in recent years [2]–[6] . However , as pointed out by Bouzid et . al . authors have often reached different conclusions regarding the effectiveness of interventions , even when reviewing the same primary studies [7] . Health promotion campaigns that appear to have some benefit are those aimed at encouraging local people to engage in activities that reduce the number of mosquitoe breeding sites close to home [2] . However , such campaigns are not totally effective and the impact on vector presence may only be short-live . Achieving sustainable change in dengue preventive behaviours remains difficult [8] , and may not necessarily lead to dengue prevention [9] , [10] , [11] . Dengue fever is endemic in Malaysia with frequent major outbreaks in the urban areas . Since dengue was first documented in Malaysia in 1902 and was made notifiable in 1973 , the disease pattern has changed from major outbreaks every four years to one of increasing trend yearly . The largest outbreak was seen in 1996 with 14 , 255 dengue cases reported and 32 deaths . The fever is the number one disease in the top 10 listed communicable diseases in Malaysia as compared to other diseases like Tuberculosis , Malaria and HIV/AIDS in 2010 and 2011 [12] . The number of dengue cases reported also increased from 27 , 381 cases in 1998 to 46 , 171 cases in 2010 . Estimate of an economic burden of dengue in Malaysia is USD102 . 25 ( 95%CI: 77 . 94–310 . 66 ) million per year which is approximately USD3 . 72 ( 95%CI: 2 . 83–11 . 30 ) per capita [12] . There is evidence that despite the fact that Malaysians generally have good knowledge of dengue fever and its prevention [13] , dengue incidence rate has substantially increased from 31 . 6/100 , 000 population in year 2000 to 163/100 , 000 in year 2010 [12] . There is a growing body of literature concerning the concept of self-efficacy , which is considered to be people's belief or confidence in their capabilities to achieve different levels of performance attainment [14] . Self-efficacy perceptions are viewed as important determinants of behaviour and affect , and the potency of these perceptions in predicting behaviours in many domains has been shown [15] . The concept of self-efficacy is commonly used in studies of health behaviours [16] , [17] . including area such as smoking cessation [18] , [19] , weight loss and body weight control [20]–[25] , exercise [26] , [27] , [28] , nutrition intake [29] , [30] , alcohol use [31]–[34] , and AIDS prevention [35] , [36] , [37] . Self-efficacy may also function as a mediator between cognitions , feelings and behaviours and the adoption of lifestyle behaviours such as healthy diet [38]–[41] . Although the effects of health promotion and educational interventions to control dengue fever have been investigated in previous studies , none of the studies have investigated the impact of self-efficacy dimensions ( level and strength of self-efficacy ) as mediators between level of dengue knowledge and effective behavioural actions to control dengue outbreak and transmission . Strength of self-efficacy refers to a person's perceived assurance that they ‘can do’ or ‘cannot do’ something reflected in their affirmative answers to questions about whether they can perform particular dengue preventive behaviours . Level of self-efficacy is a person's judgement about whether or not they can accomplish a given performance which reflects their perceived capability as measured against task demands ( dengue preventive behaviour ) at various levels of challenge ( scenarios ) to successful control of dengue fever during outbreaks [42] . We argue that understanding the relationship between knowledge , self-efficacy and behavioural change may be a route towards improved and sustainable dengue control . This paper reports on work that was conducted to study the impact of self-efficacy on dengue preventive behaviours . We conducted a survey in villages that was subsequently examined with analyses based on predictions from Bandura's Social Cognitive Theory and Maibach's path model [42] , [43] . We specifically examined the potential mediating effects that level and strength of self-efficacy may have on the relationship between knowledge of dengue and dengue preventive behaviour after being exposed to health promotion and educational interventions during the outbreaks .
We recruited heads of families or their spouses aged above 18 years old from 27 villages that had recently experienced an outbreak of dengue fever . These villages were located in the state of Terengganu , on the east coast of peninsular Malaysia . Using the method by Woodward , we calculated that we needed a sample size of about 280 respondents [44] . This was based on a requirement to detect a Pearson correlation coefficient of 0 . 4 with a power of 80% and alpha 5% ( 200 samples ) . This sample size was then inflated by 20% to account for possible non-parametric tests and 20% for potential impact of clustering within village . The population of the study included all the villages of the outbreak localities from July to December 2010 in the state of Terengganu . The list of outbreak sites was obtained from Terengganu Vector Borne Disease Division and the Terengganu Crisis Preparedness Resource Centre ( CPRC ) database . In total there were 32 outbreak locations for that 6-months period , but only 27 locations were included in the study as five others were not actually villages but higher education institutions and schools . Figure 1 shows a simplified illustration of sampling procedures used in this study . The households that were interviewed in those selected villages or sites were randomly selected based on the current outbreak list obtained from the Terengganu Crisis Preparedness Resource Centre ( CPRC ) database . The households were selected randomly from 9 , 959 houses or premises included in the study using SPSS . A total of 149 premises were excluded because they were non-owner premises or abandoned houses . The selected households were not changed or replaced with other households even if the first and second visits resulted in failure to meet some the participants for interview . Research ethics approval for this study was granted by the National Medical Research Register , Ministry of Health Malaysia ( NMRR-10-206-5412 ) and the Faculty of Health Research Ethics Committee of the University of East Anglia ( 2010/2011–13 ) , the author's institution , prior to data collection . Written informed consent was obtained from all participants prior to completion of the survey . The data collection was carried out from January to March 2011 . The recruitment and training of the 32 interviewers from the local State Health Department staff was undertaken in December 2010 . The interviewers were dedicated staff from the State Health Department whose usual tasks involved running health promotion and education activities during the outbreak . The training was conducted by the lead researcher assisted by the Head of the Health Promotion Unit , Terengganu Health State Department . All respondents were recruited after they had given informed consent . The interviewers read the questionnaires and the respondents gave their answers to those questions . The interviewers then ticked the answers in the column provided and recorded any subjective answers not listed in the answer scripts . A copy of the questionnaire is given in supplementary Text file S1 . The questionnaire used in the field was in Malay . Correct translation was checked through a parallel back-translation by members of Malaysia Institute of Translation . The questionnaire was also tested in a pilot study in both languages . Pearson or Spearman correlation coefficients used in analyses between the three main outcome measures: exposure to health promotion and education , knowledge of dengue and self-efficacy dimensions . Regression modeling was carried out using Generalized Estimating Equations of SPSS 18 to account for cluster sampling at the village level . Based on consideration of Bandura's theory , the research model by Maibach et al . [42] , and previous empirical findings on related public health issues and significant correlations from the investigation , an initial proposed model was constructed [38]–[41] . A structural equation model ( SEM ) was developed using AMOS version 18 [50] . The model reflects the relationships between variables obtained in the study in order to predict the dengue preventive behaviour change resulting from an increased dengue knowledge level and self-efficacy dimensions after being exposed to health promotion and educational intervention during the outbreak . SEM was used to test the proposed model against the observed dataset . SEM is a combination of factor analysis and path analysis and it is a confirmatory rather than an exploratory technique , because it compares a hypothesized model's covariance matrix with that of the observed data . Since the proposed model of this study involved observed variables , SEM allows us to determine significant paths between those variables in deriving a better explanation of their significant relationship findings based on the research hypotheses and proposed model . There are several steps in analysing SEM using AMOS: 1 ) to develop a model based on research theory; 2 ) identify unique values that can be used for the parameters to be estimated in the proposed model; 3 ) apply various estimation techniques , for example in this study , maximum likelihood; and 4 ) test the fit of the model against the data . According to the results , the researcher might 5 ) modify the measurement model based on theoretical justifications; revise the model by adding , deleting , or modifying relationships between variables; or use measures indicating lack of fit for specific parts of the model when theoretically justified in the Modification Indices table [51] . Goodness of fit indices were used as indicators of model fit . Chi-square tests were used as an index of the significance of the discrepancy between the original ( sample ) correlation matrix and the ( population ) correlation matrix estimated from the model . Because the significance of chi-square tests is dependent on the number of subjects , the comparative fit index ( CFI ) and the root mean square error approximation ( RMSEA ) were further considered . CFI values are derived from the comparison of the hypothesized model with the independence model . RMSEA values help to answer the question of how well the model with unknown but optimally chosen parameter values would fit the population covariance matrix if it were available [52] . The lower the discrepancy measured by the RMSEA the better , with an RMSEA of 0 . 0 indicating a perfect fit . Acceptable values are CFI> . 90 and RMSEA< . 08 . Once the model fitted the data well , the next step was to test the mediation effect of self-efficacy dimensions on the relationship between knowledge of dengue and dengue preventive behaviour by comparing a Full Mediation Model , Direct Model and Indirect Model as recommended by Baron and Kenny [53] , and Hayes [54] . The post-hoc probing test for mediation effect significance was performed to determine if the drop in the total effect ( i . e . level of dengue knowledge to dengue preventive behaviour ) was significant upon inclusion of mediator ( level or strength of self-efficacy ) in the model [55] . We aimed to test two primary hypotheses , namely ( i ) knowledge of dengue is directly associated with dengue preventive behaviours and ( ii ) both strength and level of self-efficacy are associated with dengue preventive behaviour .
We recruited 280 participants as per the sample size calculation . More than half of the respondents were female ( 58 . 9% ) . Their mean age was 42 . 7 years , and the majority ( 57 . 5% ) were aged between 36 to 55 years old , and were married ( 96 . 1% ) . The ethnic background of the respondents was 98 . 6% Malay with the remainder being Chinese . Nearly half of the respondents were housewives ( 45% ) . Table 1 presents the means , standard deviations , percentiles and ranges for all the principle scores . The level of health promotion and educational intervention exposure was low , with only 20% of the respondents receiving a moderate to high level of exposure . The respondents seemed to have moderate ( 40 . 7% ) to good ( 38 . 6% ) knowledge of dengue . In general , the self-efficacy of the respondents was at the moderate level . Although it was also found that 62 . 2% of respondents perceived they were relatively confident in performing dengue preventive behaviours , only 1 . 1% of them reported having excellent strength of self-efficacy . About half of respondents ( 45 . 4% ) showed moderate levels of self-efficacy , while 36 . 4% had little confidence and felt uncertain how to perform these kinds of dengue preventive behaviours . 2 . 5% of them reported below the average confidence ( mean = 2 . 99 ) . Most of respondents said they had received health information on dengue fever from Public Announcements ( 57 . 5% ) , Television ( 57 . 9% ) and the Newspaper ( 44 . 6% ) . In term of respondents' participation in the health promotion and educational interventions , most of them tended to be involved in the Community Source Reduction Program or Gotong-Royong ( 60% ) as compared to the Public Lecture ( 24 . 3% ) . Only 4 . 6% were involved in Demonstration activities . Regarding respondents' recent behaviour to control dengue outbreak and transmission , 73 . 2% of them failed to perform a 10-minute search and destroy exercise to eradicate Aedes mosquitoes breeding sites within the last 14 days . With regards to dengue preventive behaviour , about half ( 45 . 5% ) of the respondents did not comply with correct behaviours to control dengue fever transmission . Moreover , 30 . 4% of them had carried out only 5 minutes of cleanliness activity within the past 14 days . Only 23 . 9% of the respondents were found to comply with the correct behaviours to prevent dengue fever transmission as promoted in the educational interventions during the outbreaks . Four factors were extracted from the data on information sources and together these four factors represented 60 . 1% of the variance in the original variables . Table 2 shows the rotated component . Factor 1 was associated with obtaining information through Television , Radio and Newspaper ( regression score >0 . 5 ) . We named this factor Mass Media . Factor 2 was associated with participation in Gotong-royong and obtaining information from public announcements and outdoor media . This factor was named Local Contact . Factor 3 was named Small Group Contact ( Small Group Discussion and Demonstration ) and Factor 4 was named Direct Information-Giving Session ( Public Lecture and Individual Advice ) . A correlation matrix was generated that included each of the variables in the study ( see Table 3 ) . Overall , the bivariate relationships between the majority of independent and dependent variables were weak . The relationship between Factor 1 from the health promotion and educational intervention ( Mass Media ) and mean dengue knowledge scores was the strongest ( r = 0 . 326 , p<0 . 01 ) as compared to other factors . Significant bivariate relationships were evident between dengue knowledge and level ( r = 0 . 262 ) and strength of self-efficacy ( r = 0 . 363 ) at p<0 . 01 . Level of self-efficacy was significantly correlated with strength of self-efficacy ( r = 0 . 383 ) at p<0 . 01 and dengue preventive behaviour ( r = 0 . 212 ) at p<0 . 05 . There was a significant correlation between dengue knowledge and dengue preventive behaviour . There was no significant difference in level of dengue knowledge between those respondents who were exposed to different levels of health promotion and educational intervention . However , there were different degrees of strength in self-efficacy among those who were exposed to different levels of health promotion and educational interventions ( p = 0 . 022 ) . The level of self-efficacy however was no different among them . Although not included in the SEM , we found a significant correlation between proportions of villages who didn't undertake at least 10-minutes-cleanliness behaviour per week with the duration of the outbreak in the village ( p = 0 . 044 ) . Examination of our proposed model using SEM of AMOS 18 indicated that adjustment could be made to improve the match between the data and model ( χ2 = 75 . 622 , df = 41 , p = 0 . 189 , CFI = 0 . 870 , TLI = 0 . 895 , RMSEA = 0 . 098 ) . To identify the sources of error in the proposed model as indicated in Modification Indices , we eliminated paths that were not significant one at a time in order to find the most parsimonious model . First we eliminated the path between Factor 3 from the health promotion and educational intervention ( small group contact ) and knowledge of dengue . Second , we dropped the path between knowledge of dengue and dengue preventive behaviours . Table 4 contains the models' goodness of fit indices . Our final model fitted the data well ( χ2 = 71 . 659 , df = 40 , p = 0 . 002 , CFI = 0 . 973 , TLI = 0 . 963 , RMSEA = 0 . 053 ) . Bentler [56] , and Chou [57] both recommend CFI and TLI scores of greater than 0 . 90 as indicators of good fitting models . Browne & Cudeck [58] , ( 1993 ) and Byrne [52] recommend that models with an RMSEA of 0 . 08 or less and preferably 0 . 05 or less are good fitting models . Figure 2 shows the entire final model with accompanying path coefficients . Overall , the structural model contains relatively weak influences on the dengue preventive behaviours , with path coefficients ranging from 0 . 092 to 0 . 271 . Our main objective was to investigate the self-efficacy dimensions as mediators of the relationship between dengue knowledge and dengue preventive behaviours in relation to control of dengue outbreaks . Assessment of the mediation effects was done by comparing the full mediation model ( which includes a direct model ) and indirect model from the final structural model that we created earlier . Table 5 shows the mediation effect findings of this models comparison . From the initial analysis , knowledge of dengue did not have a direct effect on dengue preventive behaviour ( standardized β weight = 0 . 092 , p = 0 . 082 ) . However , knowledge significantly predicted the level of self-efficacy as expected ( standardized β weight = 0 . 172 , p<0 . 001 ) , and this level of self-efficacy also significantly predicted dengue preventive behaviour ( standardized β weight = 0 . 179 , p = 0 . 036 ) . Knowledge had a direct effect on strength of self-efficacy ( standardized β weight = 0 . 291 , p<0 . 001 ) and this strength of self-efficacy also significantly predicted dengue preventive behaviours ( standardized β weight = 0 . 149 , p<0 . 001 ) . Analysis for model comparison as recommended by Baron and Kenny [53] , and Hayes [54] found that the Beta for the Indirect Model was reduced from 0 . 092 to 0 . 090 in the Full Mediation Model ( in both for level of self-efficacy and strength of self-efficacy as mediators ) . Therefore , knowledge on dengue was found to have significant indirect effect on dengue preventive behaviour with a mediation effect of level of self-efficacy or strength of self-efficacy on the relationship . Post-hoc probing of significant mediation effects was performed using the Sobel Equation of computing to determine if the drop in the total effect ( i . e . , knowledge on dengue ) is significantly dependent upon inclusion of the mediator ( level of self-efficacy and strength of self-efficacy ) in the model [53] , [55] , [59]–[60] . This strategy indicated that level of self-efficacy ( z = 4 . 77 , p<0 . 05 ) and strength of self-efficacy ( z = 2 . 38 , p<0 . 05 ) did function as mediators . According to Holmbeck [55] , p<0 . 05 is the absolute value of z>1 . 96 . In addition since the Beta for the total effect of the relationship between knowledge on dengue and dengue preventive behaviours was 0 . 37 , thus roughly 65% of the path was accounted for by level of self-efficacy as a mediator ( Beta for indirect effect was 0 . 2405 ) . Likewise , the path between knowledge of dengue and dengue preventive behaviours was 97% accounted for by strength of self-efficacy as a mediator in the relationship ( Beta for indirect effect was 0 . 3575 ) [61] . Therefore , since the direct path between knowledge on dengue and dengue preventive behaviours was not significant , level and strength of self-efficacy did function as full mediators of that relationship . This result showed that self-efficacy has a complete mediation effect on the relationship between knowledge on dengue and dengue preventive behaviour . As we hypothesized , strength of self-efficacy and level of self-efficacy significantly predicted dengue preventive behaviours ( p<0 . 001 ) . In addition , strength of self-efficacy significantly predicted level of self-efficacy ( standardized β weight = 0 . 282 , p<0 . 001 ) . Later analysis for models comparison found that , the Beta for the Indirect Model was reduced from 0 . 179 to 0 . 168 in the Full Mediation Model . Therefore , strength of self-efficacy was also found to have a significant indirect effect on dengue preventive behaviour with a mediation effect of level of self-efficacy on the relationship . Once again , post-hoc probing of significant mediation effects was performed using the Sobel equation of computing as a follow-up to the findings for the structural equation model . The post-hoc strategy was conducted to determine if the drop in the total effect ( i . e . , strength of self-efficacy ) is still significant upon inclusion of the mediator ( level of self-efficacy ) in the model . This strategy indicated that level of self-efficacy did function as mediator ( z = 2 . 020 , p<0 . 05 ) . ( Note that p<0 . 05 if the absolute value of z>1 . 96 ) . In addition since the Beta for the total effect of the relationship between strength of self-efficacy and dengue preventive behaviours was 0 . 3695 , thus roughly 43% of the path was accounted for by level of self-efficacy as a mediator ( Beta for indirect effect was 0 . 1596 ) [61] . In this case , level of self-efficacy partially mediated the association between strength of self-efficacy and dengue preventive behaviours . This result showed that level of self-efficacy has a partial mediation effect on the relationship between strength of self-efficacy and dengue preventive behaviour .
This investigation is one of the first within the public health and health psychology research literature to concentrate on health promotion and educational interventions designed to reduce the risk of dengue fever . With regards to our first primary hypothesis , knowledge was found not to be independently associated with dengue preventive behaviour other than through the impact of knowledge on self-efficacy . For the second hypothesis , both level and strength of self-efficacy were predictive of dengue preventive behaviours . Our hypothesis regarding the mediational effect of self-efficacy on the relationship between knowledge on dengue and dengue preventive behaviours was supported through post-hoc probing , as recommended by Holmbeck [55] . Thus , our work would suggest that increasing the public's knowledge about dengue fever is an essential first step towards encouraging people to engage in dengue preventive behaviours . However , increasing knowledge alone would not be sufficient unless it results in increasing the level and/or strength of people's confidence in performing these behaviours . These findings are consistent with previous work on self-efficacy and healthy lifestyle behaviours [15] , [38]–[41] . Our findings hold a number of important implications for health promotion authorities and planners . This is because , since both self-efficacy level and strength are modifiable and reliable mediators of health behaviours [62] , health promoters should design dengue educational interventions and campaigns that promote self-efficacy as well as knowledge . With regards to knowledge generation , we have shown that mass media campaigns such as TV , radio and newspapers and local contact ( Gotong-royong , public announcements , and outdoor media ) and direct face-to-face communication session ( public lecture and individual advice ) are the most effective . For self-efficacy , various authors have suggested that one of the most effective means of promoting self-efficacy is through modelling socially relevant enactments of the behaviours in the mass media [15] , [42] , [62]–[65] . For example , in order to empower people to perform dengue preventive behaviours , the health authorities could produce video-taped material with trained role-players/actors performing the desired behaviours . The specially made videos could be shown to people who do not feel confident performing the specific preventive behaviours . People could then be asked to perform the desired behaviours until a level of competence and confidence was achieved . These activities could be promoted in groups or at individual level . We would also add that since the self-efficacy dimensions are a cognitive response to direct and vicarious experiences with the behaviours , health promotion and educational interventions should use persuasive messages on dengue prevention . This is to enable the community to translate the messages into the anticipated or actual dengue prevention behaviour ( example of a persuasive message: “If it breeds , we bleed , take action ! Only 10 minutes to destroy Aedes breeding sites” ) . This accords with Bandura's idea on health campaign messages that successfully encourage the target audience to engage in simply enacted interim behaviour which will serve to enhance self-efficacy through direct experience . In relation to dengue prevention , such interim behaviour might include both trial performances of the behaviour such as not purchasing flower pots that accumulate water , as well as low-level versions of the target dengue preventive behaviour such as putting garbage in a closed bin instead of accumulating it in a group for incineration . Tailored messages tend to be more personally relevant and thus attract more attention [66] . When recipients received messages tailored to their personal information processing style , they were later more likely to engage in the desired behaviour advocated in the message [67]–[69] . Therefore , in health promotion and educational interventions during dengue outbreaks , guidelines and protocols should outline vividly the specific persuasive messages to be conveyed to specific target audiences during the outbreaks in order to increase self-efficacy dimensions . The health promotion and educational interventions should advocate dengue preventive behaviour that takes account of the psychological characteristics of the desired behaviour and of the information-processing style of the target population . This simple strategy may lead to better crafting of persuasive messages , which in turn , increase adoption of dengue preventive behaviour so that outbreaks and transmission are reduced . In considering the generalizability of our study it should be noted that our respondents were predominantly based in rural villages that had recently experienced a dengue outbreak . Our findings may not be applicable in areas where there had been no prior experience of dengue fever . However , given the fact that the vast majority of the world's currently at risk population will have had prior experience , our findings ought to remain applicable . Clearly our study was conducted in a rural Malaysian population and so there are issues about whether the results can be generalised to urban populations or to rural populations in other countries with different cultures . It should also be noted that because the structural equation model was based on cross-sectional data , there should be some degree of caution on the interpretation of causal inferences . Nevertheless , it should be noted that these causal paths were hypothesized based on available research concerning predictors of dengue preventive behaviours during the outbreaks [12] , [70] , [71] . There remains an issue regarding possible reporting bias in the data collection , especially as the interviewers were also involved in the health promotion activities . The importance of not leading the respondents in any questions was stressed during the interviewers training sessions in order to minimise this source of bias . Although we cannot say definitively that there was no bias in our data collection , we would argue that the complexity of the model and the relationships between self-efficacy and knowledge independent of any particular health promotion activity would suggest that interviewer bias would have been unlikely to have played a major role in the main findings of this study . Our findings address the important need for studies that generate empirically sound and theoretically relevant data to identify variables likely to be effective for designing interventions . Further research should aim to describe other aspects of psychological variables related to behaviour changes and maintenance in relation to control of dengue fever , such as the complex role of motivation as well as perceived barriers and perceived benefits to engaging in the target behaviours [72]–[76] . Furthermore , we should also consider future intervention studies that evaluate different mediation effects of level and strength of self-efficacy as separate psychological components in predicting other health behaviours to control or prevent public health diseases . In conclusion , our research indicates that the impact of public health campaigns designed to increase the adoption of behaviours by the public to control dengue fever by increasing knowledge is mediated by the impact on self-efficacy . We argue that to be most effective public health campaigns should be designed to maximise the impact on self-efficacy . There is a strong need for further research on how to design public health campaigns for the control of vector-borne disease that maximise self-efficacy and not just knowledge . | Dengue fever is one of the most rapidly increasing vector-borne diseases of humans in the tropics . There is currently no treatment and no vaccine , so control of the disease depends on controlling the mosquito vector . Unfortunately health promotional campaigns aimed at encouraging people to reduce mosquito breeding sites have not always been 100% effective . Self-efficacy is an area of increasing research interest and can be thought of as people's confidence in their ability to engage in health behaviours . We report a study of the impact of self-efficacy on dengue preventive behaviour . We conducted face to face interviews in villages in the state of Terengganu , Malaysia that had been affected by dengue outbreaks . A structural equation model was tested and fitted the data well . Mass media , local contact and direct information-giving sessions significantly predicted level of knowledge of dengue . However , self-efficacy fully mediated the relationship between knowledge of dengue and engagement in dengue preventive behaviours . We conclude that educational components of community dengue control programmes should focus on interventions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Mediational Effects of Self-Efficacy Dimensions in the Relationship between Knowledge of Dengue and Dengue Preventive Behaviour with Respect to Control of Dengue Outbreaks: A Structural Equation Model of a Cross-Sectional Survey |
The control of helminth infections and prevention of anemia in developing countries are of considerable public health importance . The purpose of this study was to determine patterns and risk factors of helminth infections and anemia in a rural and a peri-urban community of Zanzibar , Tanzania , in the context of national helminth control programs . We carried out a community-based cross-sectional study in 454 individuals by examining at least two stool samples with different methods for soil-transmitted helminths ( Ascaris lumbricoides , hookworm , Strongyloides stercoralis , and Trichuris trichiura ) and one urine sample for Schistosoma haematobium . Finger-prick blood was taken to estimate anemia levels and to detect antibody reactions against ascariasis , strongyloidiasis and schistosomiasis , using an enzyme-linked immunosorbent assay ( ELISA ) approach . Parasitological methods determined a helminth prevalence of 73 . 7% in the rural , and 48 . 9% in the peri-urban setting . Most helminth infections were of light intensity with school-aged children showing the highest intensities . Multiple helminth species infections were pervasive in rural dwellers regardless of age . More than half of the participants were anemic , with a particularly high prevalence in the peri-urban setting ( 64 . 7% ) . Risk factors for helminth infections were age , sex , consumption of raw vegetables or salad , recent travel history , and socio-economic status . After several years of chemotherapy-based morbidity control efforts in Zanzibar , helminth prevalences are still high and anemia is common , but helminth infection intensities are low . Hence , chemotherapy should be continued , and complemented with improved access to clean water , adequate sanitation , and health education , along with poverty alleviation measures for a more enduring impact .
Soil-transmitted helminthiasis and schistosomiasis affect hundreds of millions of people and the global burden due to these parasitic worms might exceed 40 million disability-adjusted life years ( DALYs ) lost annually [1]–[3] . Chronic infections can result in negative birth outcomes , delayed physical and cognitive development during childhood , and reduced agricultural productivity among adults [4] , [5] . There is growing evidence that , besides malaria and nutritional deficiencies , heavy helminth infections as well as multiple helminth species infections of light intensities , are associated with anemia [6]–[8] . The global strategy for the control of soil-transmitted helminthiasis and schistosomiasis is morbidity control using single-dose orally-active anthelmintic drugs . In 2001 , at the 54th World Health Assembly ( WHA ) , member states were urged to achieve a minimum target of regular deworming of at least 75% and up to 100% of school-aged children and other groups at risk of morbidity by 2010 [9] . In Zanzibar , infections with soil-transmitted helminths ( Ascaris lumbricoides , hookworm , and Trichuris trichiura ) are highly endemic [10]–[12] . Strongyloides stercoralis infections also occur , but few epidemiological investigations have focused on this helminth [11] , [13] . With regard to schistosomiasis , Schistosoma haematobium is the only species endemic in Zanzibar . Its distribution is focal , linked to the distribution of the intermediate host snail [12] , [14] . Due to excessively high helminth prevalences ( >90% ) in school-aged children in Zanzibar , large-scale school-based administration of anthelmintic drugs ( mebendazole or albendazole ) was initiated in the mid-1990s [15] , and contingent upon drug donations deworming has been carried out annually . Of note , in 2006 , Zanzibar achieved the minimum target of regular administration of anthelmintic drugs to at least 75% of all school-aged children [16] . In addition , within the global program to eliminate lymphatic filariasis ( GPELF ) , ivermectin plus albendazole were distributed to the entire at-risk population ( excluding children younger than 5 years and pregnant women ) in Zanzibar from 2001 to 2006 , and a mean annual coverage rate of 80% was reached [17]–[19] . Importantly , ivermectin is efficacious against S . stercoralis [13] and albendazole against common soil-transmitted helminths [20] . Large-scale deworming programs in Zanzibar have reduced helminth-related morbidity and are likely to have lowered overall transmission [17] , [21] . Hence , as original programmatic targets are being met , it is interesting to study the patterns and risk factors of helminth infections in contemporary times . The aim of our cross-sectional study was to assess the current prevalence and intensity of helminth infections and to determine anemia levels in different age groups in a rural and a peri-urban community in Zanzibar , in the context of helminth control programs . We used standardized , quality-controlled parasitological and serological tests , administered a questionnaire to investigate behavioral , demographic , and socio-economic risk factors for helminth infection and anemia , and determined self-reported morbidity signs that might be associated with helminth infections . Combined , this information will provide an important insight into the impact of ongoing interventions and hopefully evidence-based realignment of disease-control priorities .
The study protocol was approved by the institutional research commission of the Swiss Tropical and Public Health Institute ( Basel , Switzerland ) . Ethical clearance was obtained from the Ethics Committee of the Ministry of Health and Social Welfare ( MoHSW ) in Zanzibar ( application number 16 ) . The shehas ( community leaders/heads ) and sub-shehas of each shehia ( administrative area ) were informed about the purpose and procedures of the study . The inhabitants of the shehia were subsequently informed by the shehas . All adult participants and the parents/legal guardians on behalf of their children ( aged 5–16 years ) signed a written informed consent sheet . All participants were free to withdraw from the study at any time without further obligation . At the end of the study all participants were invited to learn about their parasitological results and were treated with albendazole ( single 400 mg oral dose ) if infected with A . lumbricoides , T . trichiura and/or hookworm , with ivermectin ( single 200 µg/kg oral dose ) if infected with S . stercoralis , and with praziquantel ( single 40 mg/kg oral dose ) if infected with S . haematobium . The Zanzibar archipelago consists of the two main islands of Unguja and Pemba with ∼1 million inhabitants according to the population census in 2002 . Fishing and farming are the most important economic activities . Islam is the predominant religion . Our community-based cross-sectional study was carried out in two shehias of Unguja , in collaboration with the Helminth Control Laboratory Unguja ( HCLU ) of the MoHSW in June and July 2008 . Bandamaji is a rural shehia , located in District North A , ∼40 km from Zanzibar Town . According to the 2002 census its population consisted of 993 inhabitants and the annual growth rate in District North A is 2 . 4% [22] . Dole is a peri-urban shehia in District West , located ∼10 km north-west from Zanzibar Town . There were 2 , 496 inhabitants in 2002 . The annual growth rate of District West is 9 . 2% [23] . Both sites received single annual treatments of albendazole and ivermectin in the frame of the GPELF from 2001–2006 , and albendazole or mebendazole plus praziquantel via the school-based deworming program till 2006 . Of note , the school in Bandamaji was only opened in 2005 . According to guidelines put forth by the World Health Organization ( WHO ) , a sample size of 250 complying individuals in a geographically distinct community is needed to assess the prevalence and intensity of soil-transmitted helminth infections [24] , [25] . Allowing for drop-out and non-compliance of 20–30% , we aimed at enrolling approximately 330 individuals per study setting , with a quarter being school-aged children ( 5–15 years ) and the remaining three-quarter being adolescents and adults ( >15 years ) . All individuals from Bandamaji and Dole aged 5 years and above were eligible for the study . The shehas were asked to invite ∼300 adult community members to attend an information meeting and to bring along their children . After the meeting , adolescents and adults who participated in the meeting were invited to sign an informed consent form . Additionally , children on the spot and their parents/legal guardians were asked for the children's age and whether they were interested to participate in the study . Subsequently , the first ∼50 girls and the first ∼50 boys aged 5–15 years , who lined up to receive a consent form and were accompanied by their parents or legal guardians who signed the form were enrolled in the study . The shehas of both communities were informed about the purpose of the study . After obtaining their oral consent the shehas participated actively in deciding how , when and where the specific parts of the study ( e . g . , information of participants , collection of stool , urine , and blood , and day for questionnaire survey ) should take place . After a further meeting where the aims of the study were explained in lay terms in the local language Kiswahili to ∼300 invited adult community members and participating children , written informed consent was obtained and three stool containers labeled with unique identifiers and the intended collection day were distributed . Our goal was to obtain three stool samples over consecutive days . Stool samples were collected between 08:00 and 10:00 hours by fieldworkers at a public spot and transferred to the HCLU in Zanzibar Town . On Friday , when people tend to stay near their village for prayer , additional field procedures were carried out . Participants were interviewed with a pre-tested questionnaire about risk factors and morbidity signs that might be related to helminth infections and anemia , and about household characteristics and asset ownership [26] . Subsequently , finger-prick blood was collected from each participant and the hemoglobin ( Hb ) level was determined with a portable Hemocue device ( HemoCue Hb 201+; Sheffield , United Kingdom ) . Finger-prick blood samples were collected in small tubes ( BD Microtainer , Ref . : 365967 ) and put on ice after clotting . Finally , participants were invited to submit a urine sample , which was collected between 10:00 and 14:00 hours . Participants were interviewed in Kiswahili with a pre-tested questionnaire by trained field enumerators of HCLU . The questionnaire included demographic and a series of housing characteristics ( i . e . , building type of walls , floor , and roof ) and asset ownership ( i . e . , mobile phone , radio , black and white television ( TV ) , color TV , satellite dish , video compact disc player , fan , refrigerator , bicycle , motorbike , car , stove type ( electric , coal , wood ) , soap , access to the power grid , and animals ( cattle , cow , goat , sheep , and donkey ) ) . Risk factors for a helminth infection were determined via source of drinking water used ( i . e . , tap , shallow well , deep well , spring , and river ) , presence and type of toilet at home ( no toilet–using “the bush” , latrine , or flush toilet ) , hand washing behavior ( whether or not hands are washed with or without soap before eating and after defecation ) , consumption of raw vegetables or salad , consumption of unpeeled fruits , consumption of soil , always wearing shoes , sleeping under a bed net , traveling within the last two weeks , and ownership of a dog or a cat . Finally , using a recall period of two weeks , the questionnaire included 12 morbidity signs ( i . e . , fever , fatigue , stomach ache , diarrhea , blood in stool , blood in urine , pain when urinating , vomiting , cough , blood in sputum , itching , and headache ) and six diseases ( i . e . , malaria , soil-transmitted helminthiasis , schistosomiasis , skin disease , eye disease , and cold ) . Parasitological and serological data were entered twice in Microsoft Excel version 10 . 0 ( 2002 Microsoft Corporation ) . Questionnaire data were entered twice in EpiInfo version 3 . 5 . 1 ( Centers for Disease Control and Prevention; Atlanta , GA , United States of America ) . Double-entered datasets were compared using EpiInfo and discrepancies removed against the original records . Data were analyzed using STATA version 9 . 2 ( StataCorp . ; College Station , TX , United States of America ) and R version 2 . 10 . 1 ( R Development Core Team; Vienna , Austria ) . Only individuals who submitted at least two stool samples were included in the final analyses . Age was stratified into four groups , ( i ) 5–11 years , ( ii ) 12–14 years , ( iii ) 15–59 years , and ( iv ) ≥60 years , as suggested by WHO [33] , [34] . For each individual , the arithmetic mean of the helminth species-specific egg counts from the K-K thick smears was calculated and multiplied by a factor 24 to obtain a standardized measure of infection intensity , expressed as eggs per gram of stool ( EPG ) . Infection intensities were classified into light , moderate , and heavy , according to thresholds put forward by WHO [9] . The lower limits of moderate and heavy infections were 5 , 000 and 50 , 000 EPG for A . lumbricoides , 1 , 000 and 10 , 000 EPG for T . trichiura , and 2 , 000 and 4 , 000 EPG for hookworm , respectively . S . haematobium egg counts were classified into light ( 1–49 eggs/10 ml of urine ) and heavy ( ≥50 eggs/10 ml of urine ) [9] . Differences in the median EPG of the four age groups were determined using the Kruskal-Wallis test . Pair-wise comparisons between the median EPG of two age groups were adjusted for multiple testing as suggested by Siegel and Castellan [35] . Statistical significance was given at a p-value of 0 . 05 . Hb thresholds used to define anemia were 115 g/l for children of both sexes aged 5–11 years , 120 g/l for children of both sexes aged 12–14 years , 120 g/l for women aged ≥15 years ( non-pregnant ) and 130 g/l for men aged ≥15 years , following WHO thresholds [33] . Anemia was classified into ‘moderate to severe anemia’ and ‘heavy anemia’ when Hb values were <90 g/l and <70 g/l , respectively [36] . Antibody reactions were regarded as positive when the absorbance readings were >0 . 1 OD units for A . lumbricoides , >0 . 2 OD units for S . stercoralis , and ≥0 . 2 OD units for S . haematobium , following the manufacturer's handbook . Sensitivity ( i . e . , proportion of true-positives identified as positive ) and specificity ( i . e . , proportion of true-negatives identified as negative ) of the ELISA tests were determined considering the pooled results of the respective ELISA and at least two K-K this smear readings ( for A . lumbricoides ) , one urine filtration ( for S . haematobium ) , or at least two KAP and/or BM ( for S . stercoralis ) as diagnostic ‘gold’ standard . Hence , a person was considered positive for a particular parasite if at least one diagnostic test revealed a positive result . The socio-economic status was determined according to a wealth index , calculated on the basis of housing characteristics and asset ownership . Using principal component analysis ( PCA ) , based on the sum of household and asset characteristic scores , all interviewed participants were grouped into wealth quintiles: ( i ) most poor , ( ii ) very poor , ( iii ) poor , ( iv ) less poor , and ( v ) least poor [37] , [38] . Multivariable logistic regression was used for estimating odds ratios ( ORs ) , including 95% confidence intervals ( CIs ) , to determine risk factors for helminth infections and anemia , and to determine associations between helminth infections and anemia and self-reported morbidity signs and diseases , as assessed by the questionnaire . Outcomes were defined as specific helminth infection , determined by any parasitological method in at least one stool or urine sample , or the presence of anemia , or any self-reported morbidity sign or disease . Candidate explanatory variables for the multivariable logistic regression were those which were reasonable and present in at least 5% of the interviewed participants in each community . A backward stepwise multivariable logistic regression , allowing for possible clustering within houses and stratified by community , and removing non-predicting covariates up to a significance level of 0 . 2 was performed using the sandwich estimator robust cluster option in STATA . The remaining variables were included into the final models and the Wald test was used to determine whether each independent variable was significantly related to the outcome variable .
From 658 individuals who registered for the study and signed a written informed consent sheet , 137 ( 20 . 8% ) never submitted any stool sample . A single stool sample was provided by 67 ( 10 . 2% ) participants . The remaining 454 individuals ( 69 . 0% ) submitted two or three stool samples , and hence were included for further analyses ( Figure 1 ) . Among them , 294 were female ( 64 . 8% ) and 160 were male ( 35 . 2% ) . There were 270 people from Bandamaji ( 59 . 5% ) and 184 from Dole ( 40 . 5% ) . The age groups of 5–11 , 12–14 , 15–59 and ≥60 years consisted of 106 , 74 , 231 and 43 individuals , respectively . The median age was 19 . 5 years . Due to insufficient quantities of feces and the priority for the sequence of tests employed , 446 ( 98 . 2% ) , 437 ( 96 . 3% ) and 411 ( 90 . 5% ) individuals had at least two stool samples examined with the K-K , KAP , and BM method , respectively . Since for hookworm diagnosis the results of both K-K and KAP , and for S . stercoralis diagnosis the results of both KAP and BM were combined , the respective analyses included 450 ( 99 . 1% ) and 443 ( 97 . 6% ) individuals . Urine samples for S . haematobium examination were available from 351 individuals ( 77 . 3% ) . Finger-prick blood for estimating Hb levels was available from 352 participants ( 77 . 5% ) . Antibody reactions against S . haematobium , S . stercoralis , and A . lumbricoides , were tested using ELISA from 339 ( 74 . 7% ) , 337 ( 74 . 2% ) , and 227 ( 50 . 0% ) participants , respectively The questionnaire was completed by 375 out of the 454 individuals who submitted at least two stool samples ( 82 . 6% ) . Key population characteristics as determined by the questionnaire survey , stratified by study settings ( rural Bandamaji and peri-urban Dole ) , are presented in Table 1 . Both populations differed significantly as for place of birth , religion , profession , educational attainment , and socio-economic status . In summary , most of the rural dwellers were born in Unguja and most adolescents/adults ( ≥16 years ) resided in Bandamaji for more than 10 years . They were all Muslim and farming was their primary occupation . Only 9 . 8% of the participants belonged to the least poor wealth quintile . In contrast , more than a third of the peri-urban dwellers were born outside Unguja and almost a quarter of the interviewed adolescents/adults had lived in Dole for less than 10 years . Islam is the predominant religion , but there were also Christians ( 15 . 1% ) . The range of occupations in Dole was broader than in Bandamaji , including a higher percentage of traders , teachers , and civil servants . More than a third of the peri-urban inhabitants belonged to the least poor wealth quintile . The overall prevalence of infection with any helminth species , according to different tests examined under a microscope was 73 . 7% in Bandamaji and 48 . 9% in Dole . In Bandamaji , the prevalence of A . lumbricoides , T . trichiura , hookworm , S . stercoralis , and S . haematobium was 49 . 4% , 48 . 7% , 31 . 1% , 10 . 3% , and 5 . 4% , respectively . In Dole , hookworm was the predominant species ( 32 . 2% ) . Infections with A . lumbricoides were rare ( 3 . 4% ) , whereas prevalences of 16 . 8% , 12 . 7% , and 11 . 7% were found for T . trichiura , S . stercoralis , and S . haematobium . Among the infected individuals , 87 . 0% ( 120/138 ) and 13 . 0% ( 18/138 ) had a light and moderate infection with A . lumbricoides , 98 . 7% ( 158/160 ) and 1 . 3% ( 2/160 ) had a light and moderate infection with T . trichiura and , with the exception of one moderate hookworm infection , all others 99 . 1% ( 105/106 ) were of light intensity . No heavy infections were found for any soil-transmitted helminth . All moderate infection intensities with both A . lumbricoides and T . trichiura were observed in Bandamaji , whereas the moderate hookworm infection was found in Dole . Among the 27 S . haematobium infections , 22 . 2% ( four individuals from Dole and two from Bandamaji ) were heavily infected . The prevalence of anemia was 64 . 7% in Dole and 50 . 9% in Bandamaji , with 8 . 1% ( 16/198 ) individuals showing moderate-to-severe anemia , and 2 . 5% ( 5/198 ) individuals being severely anemic . The species-specific prevalence of helminth infections in each of the four age groups in Bandamaji and Dole is presented in Figure 2 . In both settings , the prevalence of A . lumbricoides and T . trichiura decreased with age . Whilst a decrease with age was also observed for hookworm and S . stercoralis in Bandamaji , the age-prevalence curve for these two helminths was relatively stable in Dole . Regardless of the study setting , S . haematobium infections were most prevalent in the 12–14-year-old age group ( 17 . 1% in Bandamaji and 26 . 3% in Dole ) . No S . haematobium infections were found in the elderly ( ≥60 years ) . Finally , anemia peaked in the age group 12–14 years ( 60 . 6% in Bandamaji , 73 . 7% in Dole ) . Figure 3 shows that patterns of polyparasitism differed according to setting and age . In rural Bandamaji , polyparasitism was highest in the youngest age group ( 5–11 years ) , with 36 . 4% , 10 . 9% , and 1 . 8% of the children concurrently infected with 3 , 4 , or even 5 helminth species , respectively . Polyparasitism decreased with age: 47 . 6% of the elderly ( ≥60 years ) were free of infection . In peri-urban Dole , concomitant multiple helminth species infections were less common . Approximately half of the participants were free of any helminth infection , and concurrent infections with 3 or 4 helminths occurred in <5% of the participants of any age group . No individual was found to be parasitized with all 5 helminths concurrently . Considering only arithmetic mean EPGs from infected individuals , the box plots in Figure 4 indicate that EPGs for A . lumbricoides were significantly higher in 5–11-year-old children than in participants aged 15–59 years ( Figure 4A ) . EPGs for T . trichiura were significantly higher in the 5–11-year-old children than in the three older age groups ( Figure 4B ) . In contrast , EPGs for hookworm showed no significant difference between age groups ( Figure 4C ) . Positive antibody reactions against A . lumbricoides antigen were found in all but one tested individual ( 99 . 6% ) . The seroprevalence of anti-S . haematobium antibodies was significantly higher in Bandamaji than in Dole ( 46 . 1% versus 24 . 8%; χ2 = 14 . 1 , p<0 . 001 ) . The seroprevalence of S . stercoralis infections was 32 . 9% in Bandamaji and 12 . 8% in Dole , which revealed a highly significant difference ( χ2 = 15 . 3 , p<0 . 001 ) . With regard to S . haematobium , four individuals who were found with eggs in their urine showed negative ELISA test results . Finally , 24 individuals were tested positive for S . stercoralis either with the KAP , or the BM , or both methods , but antibody reactions in the ELISA were judged absent . The sensitivities of the A . lumbricoides , S . haematobium , and S . stercoralis ELISAs were 100% , 81 . 8% , and 38 . 5% , respectively , and the specificities were 0 . 6% , 63 . 9% , and 75 . 0% , respectively . Table 2 summarizes the statistically significant ( p<0 . 05 ) risk factors for helminth infections and anemia determined by multivariable logistic regression modeling , stratified by study setting . In rural Bandamaji , males had an increased risk of an A . lumbricoides infection ( OR = 1 . 94 , 95% CI 1 . 03–3 . 65 ) . An incremental increase of age by 1 year reduced the risk of an A . lumbricoides infection ( OR = 0 . 98 , 95% CI 0 . 96–0 . 99 ) . People consuming raw vegetables or salad were more likely to be infected with A . lumbricoides ( OR = 2 . 54 , 95% CI 1 . 27–5 . 10 ) . In peri-urban Dole , no significant risk factors for an A . lumbricoides infection were determined . Participants from rural Bandamaji belonging to the least poor wealth quintile were at a significantly lower risk of a T . trichiura infection than their counterparts belonging to the most poor wealth quintile ( OR = 0 . 28 , 95% CI 0 . 10–0 . 82 ) . In peri-urban Dole , washing hands after defecation was determined as a protective factor against a T . trichiura infection ( OR = 0 . 06 , 95% CI 0 . 01–0 . 26 ) . In both study settings , for an incremental increase of age by 1 year , the risk of a T . trichiura infection decreased ( Bandamaji: OR = 0 . 96 , 95% CI 0 . 94–0 . 97; Dole: OR = 0 . 97 , 95% CI 0 . 94–1 . 00 ) . Males from Bandamaji had an increased risk of a hookworm infection ( OR = 2 . 25 , 95% CI 1 . 23–4 . 12 ) . In Dole , people with a recent travel history were more likely to be infected with hookworm ( OR = 5 . 06 , 95% CI 1 . 21–21 . 06 ) . Belonging to the very poor ( OR = 0 . 11 , 95% CI 0 . 02–0 . 58 ) or least poor wealth quintile ( OR = 0 . 12 , 95% CI 0 . 04–0 . 42 ) and consumption of unpeeled fruits ( OR = 0 . 28 , 95% CI 0 . 11–0 . 73 ) were protective factors against a hookworm infection in Dole . In rural Bandamaji , an incremental increase of age by 1 year reduced the risk of a S . stercoralis infection ( OR = 0 . 97 , 95% CI 0 . 94–1 . 00 ) . In peri-urban Dole , males were significantly more likely to be infected with S . stercoralis than females ( OR = 4 . 11 , 95% CI 1 . 21–13 . 90 ) . Moreover , a recent travel history increased the risk of a S . stercoralis infection in Dole ( OR = 5 . 43 , 95% CI 1 . 08–27 . 27 ) , whereas washing hands after defecation was a protective factor ( OR = 0 . 29 , 95% CI 0 . 09–0 . 96 ) . In both communities an incremental increase of age by 1 year was associated with a lower risk of a S . haematobium infection ( Bandamaji: OR = 0 . 93 , 95% CI 0 . 90–0 . 95; Dole: OR = 0 . 97 , 95% CI 0 . 95–1 . 00 ) . Males from Bandamaji were less likely to be anemic than females ( OR = 0 . 51 , 95% CI 0 . 27–0 . 94 ) , and consumption of raw vegetables or salad was a protective factor against anemia ( OR = 0 . 45 , 95% CI 0 . 22–0 . 93 ) . In Dole , no significant risk factors for anemia were found . As indicated in Table 3 , an A . lumbricoides infection showed a significant positive association with a T . trichiura infection in both communities ( Bandamaji: OR = 6 . 40 , 95% CI 3 . 40–12 . 06; Dole: OR = 17 . 28 , 95% CI 2 . 73–109 . 19 ) . Conversely , a T . trichiura infection was significantly associated with an A . lumbricoides infection in both study settings ( Bandamaji: OR = 5 . 38 , 95% CI 2 . 74–10 . 55; Dole: OR = 20 . 84 , 95% CI 3 . 92–110 . 75 ) . In Dole , people with a T . trichiura infection where likely to harbor a concurrent S . stercoralis infection ( OR = 5 . 34 , 95% CI 1 . 39–20 . 56 ) . A hookworm infection showed a significant positive association with a T . trichiura infection in Bandamaji ( OR = 2 . 95 , 95% CI 1 . 56–5 . 59 ) and with a S . haematobium infection in Dole ( OR = 6 . 84; 95% CI 1 . 91–24 . 49 ) . The multivariable regression models also showed that a S . stercoralis infection was positively associated with a T . trichiura infection ( OR = 4 . 05 , 95% CI 1 . 23–13 . 27 ) , and that a S . haematobium infection was positively associated with a hookworm infection ( OR = 6 . 89 , 95% CI 1 . 80–26 . 43 ) in Dole . In general , heavy S . haematobium infections showed a strong positive association with hookworm infections ( OR = 13 . 09; p = 0 . 008 ) , and a negative association with T . trichiura infections ( OR = 0 . 08; p = 0 . 013 ) . Participants with an A . lumbricoides infection had a decreased risk of anemia in Bandamaji ( OR = 0 . 55 , 95% CI 0 . 31–0 . 98 ) . Adjusting for demographic variables with a P-value of 0 . 2 or lower and stratification by community , we observed the following associations between helminth infections or anemia with self-reported morbidity signs ( recall period: 2 weeks ) : participants from Dole with an A . lumbricoides ( OR = 22 . 75 , 95% CI 2 . 50–206 . 99 ) or S . stercoralis ( OR = 4 . 47 , 95% CI 1 . 01–19 . 69 ) infection had an increased risk of an itching body ( Table 4 ) . Participants from Bandamaji with an A . lumbricoides infection had a decreased risk of coughing ( OR = 0 . 53 , 95% CI 0 . 30–0 . 95 ) , and those infected with T . trichiura had a decreased risk of vomiting ( OR = 0 . 24 , 95% CI 0 . 06–0 . 96 ) . In Dole , a T . trichiura infection increased the risk of stomach ache ( OR = 3 . 31 , 95% CI 1 . 05–10 . 43 ) . Participants from Bandamaji with anemia had an increased risk of an itching body ( OR = 5 . 35 , 95% CI 1 . 65–17 . 36 ) and an increased risk of reporting malaria ( OR = 4 . 98 , 95% CI 1 . 39–17 . 84 ) compared with participants without anemia . In Dole , anemia was a risk factor for fatigue ( OR = 2 . 81 , 95% CI 1 . 14–6 . 89 ) .
Control programs for soil-transmitted helminthiasis , schistosomiasis , and lymphatic filariasis have been implemented in Zanzibar for several years [17] , [21] . The key strategy is chemotherapy-based morbidity control , using albendazole or mebendazole against soil-transmitted helminthiasis , praziquantel against schistosomiasis , and ivermectin plus albendazole against lymphatic filariasis . Importantly , the drugs used in the GPELF also show an effect against strongyloidiasis ( i . e . , ivermectin [13] ) and against soil-transmitted helminthiasis ( i . e . , albendazole [20] ) . The helminth control programs in Zanzibar are considered successful public health interventions because of significant reductions in the prevalence and intensity of helminth infections and high levels of treatment coverage [12] , [18] , [21] . Analysis of our data showed , however that infections with soil-transmitted helminths and S . haematobium are still common , particularly in the rural setting of Bandamaji , where almost three-quarter of the participants were infected with at least one helminth species . Multiple species helminth infections affected almost half of the participants in Bandamaji , but only about one out of six individuals in the peri-urban setting of Dole . Importantly though , infection intensities were mainly low with highest EPGs observed in the youngest age group ( children aged 5–11 years ) . Seroprevalences according to ELISA tests were 99 . 6% for A . lumbricoides , 39 . 2% for S . haematobium , and 26 . 4% for S . stercoralis , but the test specificities were low . More than half of the participants were anemic and , interestingly , the overall prevalence of anemia in peri-urban Dole was significantly higher than in rural Bandamaji ( 64 . 7% versus 50 . 9% ) . Two important limitations of our study are that we did not adhere to a strict randomization procedure for enrollment , and that the number of fully complying individuals was rather low . According to estimates for the year 2007 , the total population in Bandamaji and Dole were 1 , 118 and 2 , 876 , respectively . Hence , our final study cohort consisted of approximately 30% of the population of Bandamaji and 13% in Dole . With regard to the number of fully complying individuals , one should bear in mind that we aimed at collecting three consecutive stool samples per person , employing a suite of diagnostic methods , and that we worked with all age groups of two communities . Repeated stool sampling reduced the study compliance from 79% to 69% for the submission of the first to the second stool sample , and to a level of 48% for submission of all three stool samples . The overall compliance rate is different to the one we reached with repeated stool sampling among school children in two schools in Zanzibar ( 85% ) [30] . School children are , however , readily accessible and the education system provides a convenient platform for deworming campaigns , whereas in the community , research teams and program officers depend on the will and stamina of the individuals not to forget submitting a filled stool container every morning without a daily reminder . The low compliance to the questionnaire survey and concomitant provision of a blood and urine sample is likely a result of the time consuming procedure , competing with other daily activities . Since treatment coverage by the GPELF implemented from 2001–2006 in both study sites was equally high ( mean in Bandamaji: 81 . 9% , mean in Dole: 83 . 0% ) and school-based deworming reached coverage levels of 75% of the at-risk population in Zanzibar in 2006 , there must be other local risk factors abetting different levels of helminth infections and anemia . The distinctive age-dependent patterns of A . lumbricoides , T . trichiura , and S . haematobium infection prevalence and intensity in our study population are consistent with the literature [39] . All other identified risk factors in our study showed setting-specific idiosyncrasies . For example , S . stercoralis infections showed no clear age-profile , but males were at a several-fold higher risk of an infection than females in Dole , similar to the observed gender difference for hookworm infection in Bandamaji . It should be noted that reports on the prevalence and age-profile of S . stercoralis infections are rare , often with conflicting results . Whilst studies from Côte d'Ivoire and China reported a higher prevalence of strongyloidiasis in adults [40] , [41] , in the Peruvian Amazon and in aboriginal communities in Australia mostly children were affected [42] , [43] . In line with findings from Jamaica , our data suggest that an infection with S . stercoralis is independent of age [44] . The observation of a higher S . stercoralis prevalence among males confirms results obtained elsewhere [42] , [45] , but is in contrast to recent findings from Côte d'Ivoire [46] . The higher risk of both hookworm and S . stercoralis infections in males is likely a result of genetic and immunologic determinants , as well as of gender-specific risk behavior . Moreover , and depending on the study setting , several behavioral factors were identified that showed significant associations with helminth infections and anemia: consumption of raw vegetable or salad was a risk factor for an infection with A . lumbricoides , whereas washing hands after defecation and socio-economic status ( least poor wealth quintile ) were significant protective factors against a T . trichiura infection . People washing hands after defection were also less likely to be infected with S . stercoralis . A recent travel history was associated with a higher risk of both hookworm and S . stercoralis infection . Males and the participants consuming raw vegetables or salad were at a lower risk of anemia . In addition to age , sex , socio-economic status , and personal behavior , we believe that there are setting-specific sanitary and environmental risk factors that might abet helminth infections . For example , in District North A , 46% of all households had no toilet facilities in 2004/2005 , whereas in District West only 8% of households had no access to toilet facilities [47] . Hence , the environmental contamination with helminth eggs or larvae is likely to be higher in District North A , and people are at a higher risk of soil-transmitted helminth infections . Moreover , the survival and longevity of helminth eggs or larvae in the natural environment depend on soil type and vegetation , which has previously been discussed for Zanzibar and other African settings [11] , [48] , [49] . The positive associations between ( i ) A . lumbricoides and T . trichiura , ( ii ) hookworm and T . trichiura , ( iii ) S . stercoralis and T . trichiura , and ( iv ) hookworm and S . haematobium observed in the current study in Unguja are in line with previous investigations on helminth associations carried out in Unguja and Pemba [10] , [12] . Interestingly , the adjusted OR indicating an association of hookworm and S . haematobium infection increased from 4 . 3 to 13 . 1 if children harbored heavy S . haematobium infections . This observation is similar to findings from Côte d'Ivoire and Brazil where children with increasing infection intensity of S . mansoni were also more likely to be concurrently infected with hookworm [50] , [51] . Hookworm and Schistosoma spp . infections are leading causes of anemia that can result in growth retardation and cognitive impairment of children [52] , [53] , [54] . Hence , if hookworm infections are associated with or even exacerbate with a concurrent schistosome infection , the risk of chronic anemia and related morbidity is likely to be elevated in co-endemic settings . To control morbidity due to multiple helminth infections , triple co-administration of albendazole , praziquantel , and ivermectin might be an effective strategy . Triple co-administration of the respective drugs has been shown to be safe in co-endemic settings in Zanzibar , where multiple rounds of treatment had been implemented in the past [17] . Since demographic factors , personal risk behavior , and socio-economic status shape the profile of helminth infections , poverty alleviation strategies complemented with health education and improved access to clean water and adequate sanitation , in addition to regular deworming , can help to decrease the burden of helminth infections in Africa and elsewhere in the developing world [3] , [55] . The high prevalence rates of anemia observed in both settings of our study indicate that anemia is still a major public health problem in Zanzibar , which begins early in life [56] . Participants from the rural setting presenting with anemia were more likely to report a malaria infection within the last two weeks . The association between malaria and anemia is well documented [57] . Interestingly , people infected with one or several helminth species concurrently were not at a higher risk of anemia compared with non-infected individuals . Our results are therefore in contrast to a study carried out in the Philippines , where individuals with multiple species helminth infections of light intensity were at an elevated risk of anemia [6] . In the current study , participants from Bandamaji infected with A . lumbricoides were at a lower risk of anemia and cough , and those infected with T . trichiura were less likely to report vomiting within the past two weeks . These findings might point to a potent immuno-modulation by helminths resulting in disease protective immune responses [58] . Since all findings were setting-specific it is , however , also conceivable that the apparent associations were due to local social or environmental determinants , as suggested elsewhere [59] . We were surprised to find a higher prevalence of anemia in the peri-urban community than in rural Bandamaji . Our results might suggest that anemia was driven not only by malaria but also by nutritional factors , and perhaps ethnicity , rather than by ( multi ) helminth infections [60] , [61] . Sero-prevalences of A . lumbricoides , S . stercoralis , and S . haematobium , as determined by ELISA , were several-fold higher than the prevalences found with standard direct diagnostic methods for eggs and larvae , but it must be emphasized that the specificities of all performed ELISAs were low . Likely reasons for these observations are as follows . First , whilst the prevalences and intensity of helminth infections have significantly decreased as a result of large-scale deworming programs in Zanzibar [21] , antibodies from past infections can persist for a prolonged period of time after successful treatment , and hence be detected with ELISA [62] , [63] . Second , it is known that unspecific cross-reactions ( e . g . , from antibodies against antigen from Ascaris or filarial worms ) can occur [64] , [65] . Third , widely used parasitological techniques such as the K-K thick smear lack sensitivity for detecting low-intensity helminth infections [30] , [66] . Indirect diagnostic tools such as ELISA might be more sensitive [67]–[69] . While there are many explanations for higher helminth seroprevalences determined with ELISA , it is unclear why the ELISAs failed to detect four S . haematobium and 24 S . stercoralis infections diagnosed by microscopy . Most study participants , however , had either a very low antibody response to all performed ELISAs or the measured OD was marginal below the cut-off level , suggesting that the thresholds proposed in the manufacturer's manual need careful revision , at least for our study setting . Summarizing , our data confirm that people living in areas highly endemic for helminthiases are immunologically activated as a result of previous infections [70] . Since helminths are masters in modulating host immunity they are likely impacting on co-infections , allergy , and immunizations [58] . Therefore , it will be important to incorporate gained knowledge on the epidemiology of immunological markers in future public health decisions . Our study indicates that , despite considerable progress made in the control of helminthiases in Zanzibar [12] , [18] , [21] , the “worm-problem” and anemia in Zanzibar remains a formidable challenge and cannot be overcome by preventive chemotherapy alone [71] . It should be noted that the GPELF , which regularly deployed albendazole plus ivermectin and likely had a beneficial effect on soil-transmitted helminthiasis , was terminated in 2006 . The patterns of helminth infections and anemia in rural and peri-urban communities and the identified risk factors emphasize that the pressure of helminth transmission in Zanzibar is still pervasive and that additional control measures are needed to consolidate progress made to date with preventive chemotherapy . With new discussions exploring options for further reduction of helminthiases in Zanzibar and elsewhere , including shifting the focus from morbidity control to transmission control , there is a need for integrated control programs , acting beyond preventive chemotherapy [55] , [72] , [73] . Indeed , greater steps should be taken to enforce health education , and action is needed to improve access to clean water and adequate sanitation ( e . g . , by community-led total sanitation ) . These measures will also result in an enhanced socio-economic status of people , and hence alleviate poverty , which is the key factor for the control and ultimate elimination of helminthiases . | In many parts of the developing world , parasitic worms and anemia are of considerable public health and economic importance . We studied the patterns and risk factors of parasitic worm infections in a rural and a peri-urban community on Zanzibar Island , Tanzania , in the context of national deworming programs . We invited 658 individuals aged between 5 and 100 years and examined their stool and urine for the presence of parasitic worm eggs . Additionally , we obtained a finger-prick blood sample to estimate the level of anemia and to assess for specific immune reactions against parasitic worm infections . We found that , despite large-scale deworming efforts in Zanzibar over the past 15 years , three-quarter of the rural participants and half of the peri-urban residents were infected with parasitic worms . Every second participant was anemic . Risk factors for a parasitic worm infection were age , sex , consumption of raw vegetables or salad , recent travel history , and socio-economic status . For a sustainable control of parasitic worm infections and prevention of anemia , access to safe and efficacious drugs , complemented with health education and improvements in water supply and adequate sanitation are necessary . | [
"Abstract",
"Introduction",
"Materials",
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"Discussion"
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] | 2010 | Patterns and Risk Factors of Helminthiasis and Anemia in a Rural and a Peri-urban Community in Zanzibar, in the Context of Helminth Control Programs |
HIV-1 maturation inhibitors ( MIs ) disrupt the final step in the HIV-1 protease-mediated cleavage of the Gag polyprotein between capsid p24 capsid ( CA ) and spacer peptide 1 ( SP1 ) , leading to the production of infectious virus . BMS-955176 is a second generation MI with improved antiviral activity toward polymorphic Gag variants compared to a first generation MI bevirimat ( BVM ) . The underlying mechanistic reasons for the differences in polymorphic coverage were studied using antiviral assays , an LC/MS assay that quantitatively characterizes CA/SP1 cleavage kinetics of virus like particles ( VLPs ) and a radiolabel binding assay to determine VLP/MI affinities and dissociation kinetics . Antiviral assay data indicates that BVM does not achieve 100% inhibition of certain polymorphs , even at saturating concentrations . This results in the breakthrough of infectious virus ( partial antagonism ) regardless of BVM concentration . Reduced maximal percent inhibition ( MPI ) values for BVM correlated with elevated EC50 values , while rates of HIV-1 protease cleavage at CA/SP1 correlated inversely with the ability of BVM to inhibit HIV-1 Gag polymorphic viruses: genotypes with more rapid CA/SP1 cleavage kinetics were less sensitive to BVM . In vitro inhibition of wild type VLP CA/SP1 cleavage by BVM was not maintained at longer cleavage times . BMS-955176 exhibited greatly improved MPI against polymorphic Gag viruses , binds to Gag polymorphs with higher affinity/longer dissociation half-lives and exhibits greater time-independent inhibition of CA/SP1 cleavage compared to BVM . Virological ( MPI ) and biochemical ( CA/SP1 cleavage rates , MI-specific Gag affinities ) data were used to create an integrated semi-quantitative model that quantifies CA/SP1 cleavage rates as a function of both MI and Gag polymorph . The model outputs are in accord with in vitro antiviral observations and correlate with observed in vivo MI efficacies . Overall , these findings may be useful to further understand antiviral profiles and clinical responses of MIs at a basic level , potentially facilitating further improvements to MI potency and coverage .
Currently there are more than 1 . 2 million individuals ( age 13 years older ) in the United States ( CDC data ) [1] and more than 35 million worldwide infected with HIV , with 39 million people already having died from the disease and 2 . 3 million new cases reported in 2013 . [2] There are presently >35 FDA-approved HIV therapies or combinations of agents which can be categorized into different classes: NRTIs , NNTRIs , PIs , integrase and entry inhibitors , ( the latter includes attachment and fusion inhibitors , along with CCR5 antagonists ) . [3 , 4] However , co-morbidities associated with long-term use of antiretrovirals ( ARVs ) [4–6] and the continued development of resistance remains a problem . [7 , 8] Thus , there is a continuing need for new HIV-1 drugs which lack cross-resistance to existing classes and have excellent long term safety profiles . HIV-1 maturation inhibitors ( MIs ) are a class of agents that may be effective in the treatment of HIV-1 . [9–12] MIs disrupt the final step in the HIV-1 protease-mediated cleavage of the HIV-1 Gag polyprotein between capsid ( CA ) and spacer peptide 1 ( SP1 ) , a step which is responsible for a major conformational rearrangement of viral proteins within the virion that leads to the production of infectious virions . [13–15] The first generation HIV-1 maturation inhibitor , bevirimat ( BVM ) , was halted in development[16] due to lack of clinical response in subjects whose viruses contained certain polymorphic Gag variants present in ~50% of the subtype B population , with such variations common among non-subtype B HIV-1 viruses . [17–27] Despite this result , BVM provided proof of concept ( POC ) in the clinic [28 , 29] that HIV-1 maturation inhibitors ( MIs ) per se might provide an effective alternative , should a next generation agent possess suitable pan-genotypic coverage . [30–32] BMS-955176 ( GSK3532795 ) was developed as a second generation MI that possesses antiviral activity against viruses containing BVM-resistant Gag polymorphisms . [9 , 19 , 23 , 33–40] It is currently in Phase 2b clinical trials . [41–43] However , an understanding of the mechanism for how BMS-955176 achieves this improved antiviral coverage has not been described . Such an understanding at the mechanistic level is of intrinsic interest , potentially providing further insights into the maturation process itself , and the biology and biochemistry of HIV-1 infection . Of clinical importance , such understanding may also be of value to help guide the development of newer MIs with further improvements to MI activity , genotypic coverage and spectrum . We took three approaches to address how BMS-955176 achieves these improvements to antiviral coverage . In the first , details of the antiviral dose-response profiles of BVM and BMS-955716 with respect to viruses containing various Gag polymorphs were studied . In a second approach , the mechanism of cleavage of capsid/spacer peptide 1 ( CA/SP1 ) was evaluated using a novel LC/MS assay to quantitatively characterize the kinetics of cleavage HIV-1 Gag VLPs as a function of polymorph , while also determining the inhibitory effects of BVM and BMS-955176 in that system . Thirdly , the affinities and kinetics of dissociation of these MIs to these same Gag polymorphs in VLPs were measured using a radioligand binding assay . Results reported herein indicate that reduced BVM antiviral activities toward certain polymorphs ( elevated EC50 values ) were accompanied by incomplete ( less than 100% ) inhibition of antiviral activity , even at saturating BVM concentrations . Thus , depending on polymorph , BVM may be described as a partial antagonist . On the other hand , BMS-955176 exhibits a significantly greater ability to maximally inhibit these Gag polymorphs . Biochemical characterization indicates that improvements to polymorphic coverage ( both lower EC50s and higher degrees of maximal antiviral inhibition ) are a result of its higher affinity for its target ( Gag ) , which was shown to primarily be a result of its slower rate of dissociation . The antiviral and biochemical data herein reported were integrated into a model that calculates rates of CA/SP1 cleavage as a function of MI concentration and Gag polymorph , predicting in vitro antiviral profiles and estimating in vivo efficacy . These findings offer new insights into MI activity and mechanism and may prove useful to understanding the pre-clinical and clinical responses of MIs at a mechanistic level , potentially facilitating further improvements to newer MIs .
MT-2 cells were obtained from the NIH AIDS Research and Reference Reagent Program; 293T cells were obtained from the ATCC . Cell lines were sub-cultured twice a week in either RPMI 1640 ( MT-2 ) or DMEM ( 293T ) media ( Gibco ) , supplemented with 10% heat inactivated fetal bovine serum ( FBS , Gibco ) , and 100 units/mL penicillin with 100 μg/mL streptomycin ( Gibco ) . The parent WT virus was generated at Bristol-Myers Squibb from a DNA clone of NL4-3 obtained from the NIH AIDS Research and Reference Reagent Program[44] and contains the Renilla luciferase marker in place of viral nef , and the substitution of Gag P373 for serine , the most common B subtype variation at that position among B subtype viruses ( NLRepRlucP373S ) . NLRepRlucP373S ( WT ) was modified to contain changes in Gag ( for example , V362I , V370A , A364V , ΔV370 , [40] the latter three of which encode high level resistance to BVM . [33 , 40 , 45] The recombinant viral DNA was then used to generate virus stocks by transfecting 293T cells ( Lipofectamine PLUS kit , Invitrogen ) . Titers of all stocks were determined in MT-2 cells , using luciferase as the endpoint ( Dual-Luciferase Reporter Assay System , Promega ) . [40 , 46] The TCID50/ml ( tissue culture infectious dose ) was calculated by the method of Spearman-Karber . [47] Compound susceptibilities of NLRepRlucP373S variants were examined using a multiple cycle infectivity assay as follows[40]: MT-2 cell pellets were infected with virus and re-suspended in cell culture medium . After a 1-hour pre-incubation at 37oC/CO2 , cell-virus mixtures were added to a dose range of compound in 96-well plates at a final cell density of 10 , 000 cells per well . All compounds were tested at 1% final DMSO concentration . After 4–5 days of incubation at 37°C/5% CO2 , virus yields were determined by Renilla luciferase activity ( Dual-Luciferase Reporter Assay System , Promega ) and the signals read using an Envision Multilabel Reader ( PerkinElmer Product number: 2104 ) . Maximal percent inhibition ( MPI ) values were calculated using the equation: MPI = ( 1- ( signal from average at two highest drug concentrations/signal from no-drug control ) * 100 ) . MI susceptibilities were also determined using an assay format similar to that reported . [36 , 40 , 48] which restricts viral growth to one replication cycle as follows: In a first step . 10 μg of the proviral clone of NLRepRlucP373S variant ( containing the appropriate Gag substitution ) and 8 μg of plasmid pSV-A-MuLV-env ( MuLV envelope gene under control of the SV40 promoter , NIH AIDS Research and Reference Reagent Program , Cat# 1065 ) were co-transfected ( calcium phosphate , Invitrogen , K2780-01 ) into 293T cells ( 60–70% confluence , T75 flask ) . After overnight incubation at 37°C/5% CO2 , the transfected cells were washed , trypsin treated , and re-suspended in fresh medium at a density of 5 x 105/mL . Cells were then distributed ( 100 μL/well ) to 96 well plates that contained 100 μL of media with compound ( compound was 3x serially diluted in DMSO , 1% final concentration of DMSO ) . In a second step , after 30 hours at 37°C/5% CO2 , 100μL of supernatant ( containing the newly produced virus ) was transferred to a second 96 well plate to which fresh 293T cells ( 3x104/well ) were added . Cultures were maintained for 2 days , after which cell-associated Renilla luciferase activity was measured upon the addition of Enduren ( EnduRen Live Cell Substrate , Promega , catalog # E6485 ) and the signals read using an Envision Multilabel Reader ( PerkinElmer Product number: 2104 ) . MPI values were calculated as that compound concentration which inhibits 50% of the maximal signal ( no-drug control ) as described above . To demonstrate the late inhibitory phenotype of BMS-955176 , the above single cycle assay was modified by the use of the HIV-1 envelope-deleted derivative pNLRepRlucP373 Δenv , [40] transfected with plasmid encoding HIV-1 LAI envelope ( pLAIenv was constructed within BMS , contains the entire sequencing encoding LAI GP160 under control of the CMV promoter ) . LAI pseudotyped virus produced in a first step in the presence of inhibitor was added to MT-2 cells in the second step , instead of 293T cells as performed above . HIV-1 virus-like particles ( VLPs ) are non-infectious particles that are made through transfection of a partial HIV-1 genome and contain only the Gag structural protein . VLPs used in these experiments [35 , 36 , 40] did not contain HIV-1 genes other than gag , and were prepared as follows: a synthetic gene ( GagOpt ) [49–51] , under the control of the CMV promoter in plasmid 1_pcDNAGagOpt , was constructed to encode full length HIV-1 LAI Gag , with codons optimized for expression in mammalian cells . Various GagOpt clones were used , containing the coding sequence of LAI Gag or variant Gag polymorphs , starting from the N-terminus of matrix ( MA , amino acid position 1 ) and extending to the stop codon of p6 . The VLPs were produced[52 , 53] by transfection ( Mirus Bio LLC , TransIT®-LT1 , cat# MIR 2300 ) of 293T cells ( 70–80% confluency in a T175 flask ) with 18 μg of the appropriate pGagOpt plasmid . After 2 days of incubation at 37°C , supernatants ( containing secreted VLPs ) were cleared from cell debris by filtration ( 0 . 45-μm filter , Millipore #SCHVU01RE ) . The VLP particles were then pelleted through a 20% sucrose cushion at 25 , 000 rpm in an SW28 rotor for 2 hours , re-suspended in PBS at a total protein concentration of about 1000 μg/mL and then stored at -80°C . Purified VLPs ( ~100 ng ) were incubated at room temperature for 10–30 min in 10 μL of VLP buffer ( 50 mM MES pH 6 . 0 , 100 mM NaCl , 2 mM EDTA and 2 mM DTT ) supplemented with 0 . 06% Triton X-100 to remove the VLP lipid bilayer . Delipidated VLPs ( ~100 ng ) were incubated with 3 μM MI ( 0 . 1% final DMSO ) at 22°C for 2 hours , and then digested with HIV-1 protease by adding 1 μL of 2 . 7 μM of HIV protease ( final concentration 0 . 27 μM , HIV-1 protease constructed to contain substitutions that limit auto-proteolysis: Q7K/L10I/I13V/L33I/S37N/R41K/ L63I/C67A/C95A ) [54] One μL samples were taken at the indicated time points and digested with trypsin as follows: one μL of each HIV-1 protease digested sample was added to 24 μL of 50 mM ammonium carbonate ( pH 8 ) containing 4 mM DTT . Samples were incubated at 60°C for 60 minutes , and then alkylated by the addition of 1 μL of 100 mM iodoacetamide . Samples were then kept in the dark for 30 minutes . Subsequently , 1 μL of 0 . 1 mg/mL reconstituted trypsin ( Promega sequence grade modified trypsin , cat# 9PIV511 ) was added to each sample , and trypsin digestion was allowed to proceed at 37°C overnight . Reactions were stopped with 1 μL of formic acid , and peptides were analyzed by LC/MS . For MI inhibition studies MI ( 3 μM >500-fold antiviral EC50 , 2 hour pre-incubation ) MIs were first added to VLP to effect binding , after which time HIV protease was added to catalyze cleavage . Under these conditions the molar ratio of MI to Gag monomer is approximately 30-fold . Liquid chromatography/mass spectrometry ( LC-MS ) analysis was performed using a Waters nanoAcquity UPLC system interfaced with a Thermo Scientific LTQ XL Orbitrap mass spectrometer affording nanoflow-LC/accurate mass data . Data were acquired by positive ion electrospray ionization using a Michrom Bioresources , Inc . Advance CaptiveSpray ion source operated at 1 . 5 kV and a transfer tube lens set at 150°C . Data on unique tryptic peptides of interest was acquired by single ion monitoring ( SIM ) using a 5 amu window in profile mode at a resolution of 7500 @ ½ ht . NanoLC analysis was carried out using a Waters Symmetry C18 180 μm x 20 mm 5 um ( PN-186003514 ) trap column and a Microm Magic C18AQ 0 . 1 x 150 mm ( PN-CP3/61271/00 ) analytical column . Trapping was performed at 5 μl/minute for 2 minutes at the initial gradient composition prior to the analytical gradient . The mobile phase composition was water ( MP-A ) and acetonitrile ( MP-B ) with each containing 0 . 1% formic acid . The analytical gradient was as follows 5%B to 35% MP-B over 30 min ( ramped to 70% MP-B followed by equilibration at 5% MP-B ) at a flow rate of 500 nL/minute . The mobile phase composition was water ( MP-A ) and acetonitrile ( MP-B ) with each containing 0 . 1% formic acid . Three microliters injection volumes were used for each sample . Data was analyzed using Thermo Xcalibur Processing software 3 . 0 . 63 and Thermo Xcalibur Quanbrowser software 3 . 0 . 63 . Areas were measured using the plus 2 charge state mono isotopic mass of the peptides of interest +/- 0 . 2 amu from the peak apex . The raw peak area for the SQ peptide ( SLFGNDPSSQ , internal trypsin cleavage fragment at the C-terminal terminal end of Gag ) , was used as an internal control for normalization of the response for the peptides of interest . The data for the AM peptide ( Gag SP1 , generated by HIV-1 Pr cleavage at the N- and C-termini of SP1 ) , the VM peptide ( generated by HIV-1 Pr cleavage between SP1 and nucleocapsid , then cleavage by trypsin ) and the VR peptide ( cleavage by trypsin only , no internal cleavage by HIV-1 Pr ) were normalized against the data from the SQ peptide . The percent of total = 100 x [AM/SQ / ( AM/SQ + VM/SQ + VR/SQ ) ] , where AM/SQ + VM/SQ + VR/SQ = the sum of all the peptide fragments encompassed within the two trypsin cleavage sites on either side of the SP1 peptide . Specific binding of MIs to VLPs were determined using a scintillation proximity ( SPA ) radiolabeled binding assay . VLPs ( 0 . 5 to 1 . 2 μg in PBS ) were mixed with 100 μg of SPA beads ( PBS suspension , PVT WGA SPA beads , PerkinElmer , cat # RPNQ0250 ) in 40 μL of total volume per well ( 96-well plate , Corning , white low binding , cat# 3600 ) After 1-hour incubation at room temperature , the volume was increased to 180 μL /well by the addition of binding buffer ( 100 mM Tris , pH 6 . 5 , 2 mM EDTA , 0 . 03% Tween-20 , 5 mM MgCl2 ) . The final concentration of DMSO in the assay was 10% by volume . For determination of Kd values by a competition method , 20 nM of [3H]-BMS-977660 ( a C:20 double bond reduced ( tritiated ) form of BMS-955176 ) [35 , 40] was added to the VLP/bead mixtures , to which was added a serial dilution ( 0 . 04–3000 nM ) of non-radiolabeled MI . After 4 hour-equilibration at room temperature , bound [3H]-BMS-977660 was measured using a Top Count plate reader ( PerkinElmer ) . The data were fit to an equation for heterologous competition ( GraphPad v 5 . 1 ) . MI dissociation rates were measured by adding 30 nM [3H]–BMS-885221 ( a C:20 double bond reduced form of BVM ) or 20 nM [3H]–BMS-977660 to SPA bead/VLP ( 0 . 5 to 1 . 2 μg ) complexes , allowing binding to reach equilibrium for 3 hours at room temperature . After this time a 40-fold molar excess of unlabeled competitor MI was added to effect irreversible displacement of the [3H] MI . Kinetics of disappearance of the bound 3H MI were monitored using a Microbeta2 plate reader ( PerkinElmer ) and the data fitted to a first order exponential equation ( GraphPad Prism v5 . 1 ) . Details of the model are shown in later in the Results section . In the presence of an MI , the rate of CA/SP1 cleavage , and thus the formation of mature virus , is derived below . Since the measured dissociation rate constants of the MIs ( koff ) are faster than the innate rates of CA/SP1 cleavage ( k1 ) for the WT and polymorphic viruses , a rapid equilibrium assumption was employed to derive the observed rate constant ( kclv , ob ) to form mature virus ( C ) . With this assumption , the association and dissociation rates of MI binding are the same . The concentration of total immature virus equals the sum of free immature virus ( B ) plus the MI bound immature virus ( A ) and is defined as Imtotal . Replacing [A] in Eq 2 from Eq 1: [Imtotal]=[MI][B]Kd+[B] ( 3 ) Rearranging Eq 3 , the relationship between B and Imtotal [B]=Kd[MI]+Kd[Imtotal] ( 4 ) The formation of mature virus ( C ) , and the depletion of total immature virus ( Imtotal ) have the same rate , thus d[C]dt=−d[Imtotal]dt=k1[B]+k2[A]=k1[B]+k2[MI]Kd[B] ( 5 ) Substituting [B] in Eq 5 with Eq 4: d[C]dt=−d[Imtotal]dt= ( k1*Kd[MI]+Kd+k2*[MI][MI]+Kd ) [Imtotal] ( 6 ) Integrating Eq 6 , the solution of equation for the cleavage of CA/SP1 , and thus formation of mature virus ( C ) is Ln[C][T]== ( k1*Kd[MI]+Kd+k2*[MI][MI]+Kd ) t ( 7 ) where t is the time , assuming at t = 0 there is no cleaved CA/SP1 or mature virus existing , and T is the total concentration virus . The observed rate constant ( kclv , ob ) to form capsid from CA/SP1 , and thus mature virus , in the presence of MI is: kclv , ob=k1*Kd[MI]+Kd+k2*[MI][MI]+Kd ( 8 )
Previous reports indicated that a first generation MI , BVM , demonstrated poor antiviral activity both preclinically and in a POC study toward clinical isolates[28 , 29] containing polymorphic substitutions in Gag around the site of its mechanism of action , i . e . , at or near the HIV-1 protease-mediated cleavage site between capsid ( p24 ) and spacer peptide 1[9 , 19 , 20 , 23 , 33 , 45] These polymorphs include substitutions at Gag positions V362 , Q369 , V370 and T371 . [19–21] BMS-955176 was identified as a clinical candidate with improved potency against viruses containing these polymorphic substitutions , low human serum binding and excellent PK properties [35–38 , 40 , 55] BMS-955176 ( Fig 1 ) retains potent activity toward these polymorphic variants in vitro and was active in a Ph2a POC study[41–43] As shown in Table 1 , [40] BMS-955176 is 5 . 4-fold more potent than BVM toward WT virus , and polymorphic viruses retain sensitivity to BMS-955176 , with FC values ( EC50/WT EC50 ) between 1- and 6 . 8-fold . [35 , 36 , 40] The protease inhibitor nelfinavir was used as a control , which exhibits similar antiviral characteristics towards all the polymorphic viruses . By comparison , BVM exhibits significantly reduced activity toward these variants ( up to >1000 fold ) . For example , BMS-955176 retains activity toward variants with substitutions at Gag V370 by alanine or methionine ( 1 . 4- and 1 . 5-fold , respectively ) , and V362I ( 2 . 4-fold ) , as compared to 54- , 177- and 7 . 2-fold losses of sensitivity by BVM , respectively . In addition , BMS-955176 retains activity toward virus with V370A/ΔT371 and ΔV370 substitutions , both minor polymorphs in subtype B , but characteristic of subtype C isolates[24] ( FCs of 3 . 5 and 6 . 8-fold , respectively ) . By comparison , BVM is >100-fold less active toward both V370A/ΔT371 and ΔV370-containing viruses . An early BMS compound in the series leading to the identification of BMS-955176 was BMS-1 ( Fig 1 ) , [37] with an antiviral profile similar to BVM . It was included in this study to determine if the results were able to be generalized beyond BVM and BMS-955176 . BMS-955176 does not inhibit A364V[40] , a resistance mutant selected for by BVM in vitro[33] and also reported in two HIV-1 subjects in a clinical trial with BVM . [56] Overall , these results indicate that BMS-955176 exhibits significantly improved in vitro antiviral activity toward polymorphic variations in Gag which result in reduced sensitivity to first generation MIs . With these results in hand we initiated virological and biochemical studies whose aim was to understand the mechanistic basis for the improved antiviral profile of BMS-955176 . Earlier biochemical studies had noted that while BVM disrupts the final step of HIV-1 maturation , that of CA/SP1 processing , this disruption is not an absolute block: some mature CA is generated , even at high concentrations of the compound . [57] We considered it possible that partial biochemical inhibition might translate into partial inhibition in antiviral assays . This concept was evaluated by conducting detailed studies of the antiviral inhibition dose response curves of BVM toward less susceptible Gag polymorphs , focusing on the degree of inhibition at the highest BVM concentrations tested . Initial studies made use of a multiple cycle ( MC ) infectious virus assay using HIV-1 luciferase reporter viruses . In this format , a low viral input ( multiplicity of infection typically 0 . 002–0 . 005 ) was used , and therefore multiple rounds of virus release and viral re-infection were required to achieve sufficient luciferase signal for detection at the assay endpoint ( typically 4 days post infection ) . The results are shown in Fig 2 and Table 2 . In this format , BVM inhibition of the control WT virus ( percent maximal inhibition ( MPI +/- Stdev ) of 98 . 4 +/- 1 . 0 ) was similar to that achieved by NFV , an HIV protease inhibitor used as a positive control ( ( percent maximal inhibition ( MPI ) of 98 +/- 0 . 9 compared to 100% , respectively ) . By comparison , BVM inhibition of the polymorphic viruses V362I and V370A reproducibly achieved only partial inhibition ( MPI values of 81 . 3 +/- 2 . 0 and 65 . 4 +/- 3 . 8 , respectively ) . Improvement to the MPI of the V362I and V370A viruses was not achieved at higher BVM concentrations , as a plateau in inhibition at a concentration occurred at approximately 1 μM . Representative examples of dose response curves are shown in Fig 2 for BVM , BMS-1 and BMS-955176 . The ΔV370 virus variant is resistant to BVM , exhibiting an MPI of 9 . 2+/- 6 . 3 ( Table 2 ) . Control experiments performed with 3 and 6 μM MI dissolved in 10% FBS media vs . PBS buffer for up to 4 days , found that upon subsequent evaluation the concentration of MI between 80–100% in the media , indicating no loss due to precipitation of MI under these conditions . Average recovery in the PBS condition was ~50% , indicating precipitation and binding to the walls of the tube ( S1 Fig ) . This result indicates that the plateau in inhibition is not an artifact of limited MI solubility under the cell culture conditions . Secondly , as discussed later below , ( behavior of BMS-955176 toward the ΔV370 virus ) , there is an obvious plateau in inhibition at 100 nM in a single cycle assay of approximately 50% of maximal , but in a multiple cycle assay the maximal percent inhibition ( MPI ) is higher ( 91 . 9% ) . Such pronounced plateaus were observed in other single cycle assays ( see values in Table 2 ) . If solubility were to be the limiting factor , both single and multiple cycle formats would be expected to provide similar plateaus . BMS-1 , an early compound in the development of the structure activity relationship ( SAR ) leading to the identification of BMS-955176 , shares structural similarity ( Fig 1 ) to both BVM and BMS-955176 but differs from BVM by replacement of the C3 dimethylsuccinic acid by a benzoic acid . [37] Similar to BVM , the first generation BMS-1 ( FC values similar to BVM , Table 1 ) exhibits a high MPI against wild type virus ( 97 . 0 +/- 0 . 5 ) , but only partially inhibits V362I and V370A viruses ( MPIs = 71 . 4 +/- 5 . 4 and 63 . 2 +/- 4 . 7 , respectively ) and does not inhibit ΔV370 containing virus ( MPI = 3 . 7 +/- 2 . 9 ) ( Fig 2B and Table 2 ) . By comparison , BMS-955176 exhibits high MPI values of 100 +/- 0 . 4 , 98 . 2 +/- 1 . 3 , 93 . 8 +/- 1 . 8 and 91 . 9 +/- 4 . 4 for WT , V370A , V362I and the ΔV370 containing viruses , respectively ( Fig 2C and Table 2 ) . To further probe the phenomenon of incomplete inhibition of various polymorphic viruses , we employed a 2-step single cycle assay in which HIV-1 LAI pseudotyped virus is first released into the supernatant by co-transfection of NLRepRlucP373Δenv and pLAI envelope plasmids into 293T cells in the presence of MI . [40] Subsequently , the supernatant is harvested and used for infection of MT-2 cells in a second infection step . In a manner similar to the Magi assay[48 , 58] a signal in the second infection step indicates that infectious virus had been produced in the transfection step . However , subsequent rounds of infection are prevented as virus produced in the second stage lacks an HIV-1 envelope , and is thus unable to infect MT-2 cells . An inhibitor which blocks the production of infectious virus in the transfection stage of the assay will score as inhibitory in the second stage of the assay . Since this assay monitors the events that have taken place in a single cycle of infection , we refer to this format as a single cycle assay , or SC assay . Control experiments established that when a late inhibitor such as nelfinavir is added at the transfection step , luciferase activity is inhibited in the infection stage ( Fig 3A ) . However , when NFV is added only at the infection stage , luciferase production is not inhibited ( Fig 3B ) . The HIV-1 attachment inhibitor , BMS-378806[59] is fully active ( Fig 3B ) , as expected for an agent which inhibits early in the HIV-1 life cycle . The MIs BVM and BMS-955176 behaved similarly to nelfinavir , inhibiting luciferase production only when added in the first step of the assay , consistent with their late mechanism of action . As shown in Table 2 , BVM exhibited an SC MPI value towards WT virus of 82 . 3 +/- 2 . 7 , less than BMS-955176 in this more demanding format , while BVM barely inhibited the V370A virus ( MPI of 19 . 0 +/- 3 . 5 ) , a result which is qualitatively similar to that obtained using the MC format . The ΔV370 variant is resistant to BVM in this assay . In contrast , BMS-955176 exhibits SC MPI values of 93 . 0 +/- 2 . 5 , 76 . 5 +/- 3 . 0 and 45 . 9 +/- 7 . 4 towards the WT , V370A and ΔV370 viruses , respectively ( Table 2 ) . Overall , MPI values in both the SC and MC formats follow the same trend , but SC MPI values are reproducibly lower , presumably due to the fact that viral challenge is higher in the transfection format vs . infection ( MC , low multiplicity of infection = 0 . 005 ) , and the absence of multiple cycles which inhibit breakthrough virus from within each preceding cycle . Antiviral dose response curves for inhibition of the ΔV370 virus by BMS-955176 are compared using the two formats ( MC assay , Fig 4A and SC assay , Fig 4B ) . Fig 4C shows the differences in MPI values from Fig 4A and 4B , where the control NFV exhibits full inhibition in both formats . BMS-955176 inhibition of ΔV370 in the MC assay did not reach the 100% control value of NFV ( Table 2 , Fig 2C ) . The single cycle assay provides a wider dynamic range from which to understand the nature of the stable incompletely inhibited plateau , as compared to the multiple cycle assay ( ~100 nM BMS-955176 toward ΔV370 ( Fig 2B ) ) . To understand the partial antiviral inhibition results we considered the basic framework for the underlying mechanism of maturation inhibition , i . e . its capacity to block the last cleavage step during virion biogenesis , that of CA/SP1 cleavage by HIV-1 protease . [30 , 31] As depicted in Fig 5 , maturation inhibitor ( MI ) binds to the immature HIV Gag polyprotein in the vicinity of the cleavage site[60–62] to produce the MI-bound form ( A ) , in which CA/SP1 is protected from HIV-1 protease cleavage . As reported , action of MIs on Gag VLPs requires that Gag be fully assembled in its quarternary state , [9 , 63] in concordance with this we have observed that heat inactivation of VLPs abrogates specific MI binding . Binding is reversible , [35 , 40] with association and dissociation rate constants defined as kon and koff respectively . The innate cleavage rate constant ( k1 ) determines the efficiency of the irreversible conversion from immature virus ( B ) to mature virus ( C ) . Based on the observed maximal percent inhibition ( MPI ) values from the cellular antiviral assays , we hypothesize that the MI-bound immature virus ( A ) can also be cleaved by HIV-protease , but at a reduced rate ( k2 , where k2 < k1 ) , thus accounting for the production of virus , as a function of polymorph and MI , even at saturating concentrations of MI . In this model ( derivation in Materials and Methods ) k1 is specific for each Gag polymorphism , while k2 is a function of both MI and Gag polymorphism . Thus , mature virus C will be produced as a function of time in a manner dependent on the steady state concentrations of both the free immature form B and the MI-bound immature form A , and dependent on their respective rate constants , k1 and k2 , for HIV-1 protease cleavage of CA/SP1 . To challenge this scheme and model this process , we created appropriate biochemical assays needed to obtain the requisite protease CA/SP1 cleavage rate constants and MI affinities toward the WT and polymorphic variant viruses . An LC/MS analysis method to quantify the specific event inhibited by MIs was developed that measured the HIV-1-mediated cleavage of CA/SP1 ( p25 ) to CA ( p24 ) and SP1 through quantitation of a peptides released after subsequent trypsin cleavage ( Fig 6A ) . [35] This method entails exposure of HIV-1 Gag virus-like particles to HIV-1 protease in vitro in the absence or presence of MIs , followed by trypsin cleavage of the resulting HIV-1 protease-mediated products . The starting parental material ( no cleavage at either SP1/NC or CA/SP1 ) is referred to as peptide VR , by virtue of the N- and C-terminal amino acids of the peptide produced by trypsin cleavage of Gag ( Fig 6B ) . Cleavage by HIV-1 protease between SP1 and NC at site H1 , and subsequent cleavage by trypsin , produces intermediate peptide VM ( Fig 6B ) . The N-terminal valine of VM is derived from trypsin cleavage while the C-terminal methionine is derived from HIV-1 protease cleavage . Lastly , HIV-1 protease cleavage at CA/SP1 at HIV-1 protease site H2 produces peptide AM ( Fig 6B , peptide nomenclature as further described in Materials and Methods , peptide AM = Gag peptide SP1 ) . This method is suitable for monitoring the 3 species simultaneously , allowing for measurement of the kinetics of cleavage at both CA/SP1 and NC/SP1 ( representative experiments for wt and A364V are shown in Fig 7C and 7D , respectively ) . In the example of Fig 7C ( wt ) , an average of two independent experiments at an HIV-1 protease concentration of 270 nM , the parent peptide VR disappears first due to rapid cleavage between SP1 and NC , as has been reported . [13–15] Rapid disappearance of VR is accompanied by the release of intermediate VM ( VM is essentially the surrogate for p25 ) , which decreases as AM ( SP1 ) is formed . AM ( SP1 ) peptide appears slowly , gradually increasing with time , but its formation remains incomplete at the last time point ( 240 minutes ) under this set of conditions . By comparison , for A364V ( Fig 7D ) the disappearance of parent VR is similarly rapid vs . wt , while the appearance of intermediate VM , and product AM ( SP1 ) are faster than wt . Measured rate constants ( kclv , ob at 270 nM HIV-1 protease ) for the cleavage of CA/SP1 by HIV-1 protease at CA/SP1 from WT and BVM-resistant polymorphic VLPs are shown in Table 3 . For comparison to the cleavage rate data , multiple cycle antiviral sensitivities from Table 1 are also shown in this table . As might be expected , absolute cleavage rates were a function of the HIV-1 protease concentration; they were linear over the range of 67–540 nM HIV-1 protease ( S2 Fig ) , indicating no loss of proteolytic activity within this time window , as expected for use of protease specifically engineered to not undergo autoproteolysis . [54] CA/SP1 cleavage of WT VLPs was the slowest , while VLPs containing V370A and V362I were cleaved approximately 3-fold faster than WT ( Table 3 ) . The subtype C-like surrogate polymorphic VLPs , V370A/ΔT371 and ΔV370 , were cleaved 2 . 2- and 2 . 7-fold faster than WT . By comparison , A364V , the completely BVM and BMS-955176-resistant variant , [9] was cleaved ~10-fold faster than WT , as reported . [64] A set of representative AM peptide ( SP1 ) appearance curves is shown in S3 Fig: the order of appearance of SP1 product is A364V > V370A , V362I , ΔV370 > V370A/ΔT371 > WT , which is a similar , but in inverse order , to the antiviral sensitivities of these polymorphic viruses to BVM ( Tables 1 and 2 ) . HIV-1 protease specifically designed to inhibit auto-proteolysis was used , [54] as initial experiments of wt HXB2 HIV-1 protease produced unsatisfactory results in terms of non-linearity of cleavage with time . As can be seen in S2 Fig , there is linearity of cleavage for wt for concentrations of protease up to 540 nM , an indication of no loss of proteolytic activity , with the kinetic data reported in Table 3 performed using 270nM protease . There was non-linearity for A364V cleavage at 270nM protease at longer time points , thus the rate constant data for A364V was derived from within the linear range only . A sub-analysis of the rates in the linear range over multiple concentrations of protease indicated that the relative 2nd order rate constant for A364V ( S2 Fig ) is 9-fold faster than wt , in agreement with the 1st order constant , and indicating that the first order rate constant accurately captures this information . The relative rate of cleavage of A364V ( 9 . 7-fold ) is in accord with a value previously published ( 7 . 6-fold ) . [64] BVM and BMS-955176 were evaluated for their abilities to inhibit CA/SP1 cleavage of the Gag polyprotein using the LC/MS analysis method . Preliminary experiments established that MI binding to VLPs reached equilibrium within 2 hours , so incubations with MI were maintained , prior to adding protease . As shown in Fig 8A ( left panel ) , 3 μM BVM or BMS-955176 inhibit the production of final product AM ( SP1 ) from wt VLPs . In addition , inhibition of cleavage data by the MIs are not due to non-linear rates of cleavage , due artifactually from loss of proteolytic activity , but rather , are due to innate differences in cleavage rates ( see above , protease engineered to limit autoproteolysis and cleavage rates calculated from within the linear range ) . However , BVM inhibition of WT CA/SP1 cleavage was not maintained throughout the entire time course of the cleavage experiment , as it dropped from 39% inhibition at 2 hours to 1% inhibition at 4 hours ( Fig 8A ) . On the other hand BMS-955176 exhibited sustained inhibition over the 4 hour period with WT VLPs . This persistence of in vitro CA/SP1 cleavage inhibition trended with the antiviral cell culture MPI values ( Table 2 ) . For example , the sustained inhibition of cleavage of WT CA/SP1 by BMS-955176 correlates to its single cycle MPI value of 93% ( 100% for multiple cycle MPI ) towards WT virus in cell culture , whereas the loss in in vitro inhibition of CA/SP1 cleavage at longer time points by BVM towards WT correlated to its single cycle MPI of 82% ( 98% for multiple cycle MPI ) . VLPs containing the ΔV370 polymorphism were also evaluated in this assay . BMS-955176 inhibited ΔV370 cleavage to a degree similar to BVM inhibition of WT at the earliest time point ( 30 minutes ) and did exhibit time-dependence , but the loss of inhibition was slow , with ΔV370 cleavage still remaining partly inhibited ( 13% ) at the 4 hour time point . BVM was not inhibitory at any time point toward ΔV370 containing VLPs . Again , the time-dependent inhibition in this assay correlates to lower MPI values in cell culture with BMS-955176/ΔV370 values of 46% for its single cycle MPI ( 92% for multiple cycle MPI ) and BVM/ΔV370 values of -26% ( SC MPI ) and 9% ( MC MPI ) . Interestingly , while BVM did not inhibit cleavage of A364V , BMS-955176 reproducibly exhibited a small degree ( ~10% ) of inhibition at the earliest time point , but was not inhibitory by 2 hours ( Fig 8B ) . Multiple cycle MPI values for both compounds against A364V containing virus were near zero . Thus , lower MPI values are correlated to both reduced antiviral potency ( elevated MC EC50s ) and a time-dependent loss of in vitro inhibition of CA/SP1 cleavage . Conversely , higher antiviral MPI values are correlated with greater antiviral potency ( lower MC EC50s ) and correlated with persistence of in vitro inhibition of CA/SP1 cleavage over time . To complete the data required to model MI inhibition of CA/SP1 cleavage ( Fig 5 ) as a function of MI and Gag polymorph , specific binding affinities of BVM , BMS-1 and BMS-955176 toward VLPs containing Gag polymorphs were determined through the use of a competitive radioligand binding assay ( Table 4 ) . [40] Examples of competition displacement assay results are provided in S4 Fig , including BMS-955176/A364V . BMS-955176 affinity for WT Gag VLPs was 3 . 2 nM , with slightly lower affinity for V362I ( 4 . 3 nM ) , and reduced affinity ( 2- and 10-fold ) for V370A and ΔV370 VLPs , respectively . By comparison , BVM affinity toward WT was 5 . 4 nM , which was reduced 2 . 9- , 9 . 1- and 48-fold toward V362I , V370A and ΔV370 , respectively . BMS-1 affinities were 3-5x reduced , as compared with BVM . The binding of BMS-955176 toward A364V was measurable ( Kd 98 +/- 13 nM ) , but severely attenuated . At a concentration of 3 uM , BVM only partly inhibited [3H]-BMS-977660 ( BMS-‘176* ) . Total radiolabel binding to A364V was low; a reliable Kd could only be determined for BMS-955176 . An adaptation of the binding assay was used to measure the kinetics of MI dissociation , as has been described for the determination of kinetics of dissociation of [3H] HIV integrase inhibitors from HIV-1 integrase . [65] Pre-formed MI/VLP complexes were treated with a large molar excess of a competitor MI , and the kinetics of dissociation followed over time . Representative examples of dissociation data are shown in S5 Fig , with data tabulated in Table 5 . Dissociation half-lives for BVM and BMS-955176 were determined using their related C20:C29 double bond reduced tritiated derivatives , [3H] BMS-885221 ( [3H BVM* ) and [3H] BMS-977660 ( [3H ‘176* ) , whose antiviral profiles are identical to BVM and BMS-955176 , respectively . Dissociation half-lives for [3H]-BVM* and [3H]-176* were similar for WT VLP ( 41 vs . 51 minutes , Table 5 ) . However , [3H]-BVM* dissociates rapidly from V370A and ΔV370 VLPs ( ≤ 3 minutes ) and with an intermediate rate from V362I . By contrast , [3H]-176* displays similar dissociation kinetics towards all four VLPs . Dissociation of [3H]-176* from A364V was difficult to measure due to the low value of specific binding: T1/2 was rapid ( < 2 minutes ) . Rates of dissociation of BVM from V370A , V362I and ΔV370 were >12 , 2 . 0 and >19-fold faster , respectively , compared to BMS-955176 ( Table 5 ) . This is similar in magnitude to the decreased affinities of BVM for these VLPs ( 9 . 1- , 3 . 5- and 48-fold , respectively , as compiled in Table 4 ) . The antiviral potencies in cell culture toward the viruses with these polymorphs share the same trend as the affinity and off rate data: when compared with BVM , BMS-955176 binds with higher affinity and dissociates more slowly from the polymorphic VLPs , a result which is qualitatively correlated with its improved ability to inhibit replication of the cognate polymorphic viruses ( Table 1 ) . Interestingly , while BMS-955176 affinity ( Table 4 ) and dissociation rates ( Table 5 ) for WT , V362I and V370A are correlated ( similar Kd values , similar dissociation half-lives ) , thus indicating that affinity is mainly driven by dissociation rates , affinity of BMS-955176 toward ΔV370 is reduced 10-fold as compared to WT ( Table 4 ) , though the dissociation rate is reduced by only 1 . 1-fold . This may indicate that reduced BMS-955176 affinity toward ΔV370 is due to a slower rate of association or possibly more complex multi-step binding kinetics , as has been observed for HIV-1 integrase strand transfer inhibitors . [66] This slower association rate implies a less pre-organized binding site , hindering the association of the ligand to its binding site , a point later addressed in the Discussion section . Biochemical studies of rate constants of polymorphic cleavages ( Table 3 ) , on one hand , and the binding affinities of MIs ( Table 4 ) , on the other hand , indicate that there is a qualitative relationship of each to the efficacy of a given MI to inhibit viral replication of a given polymorphic virus . From cellular assays , a plateau in inhibition ( MPI values of <100% ) suggests an escape mechanism that appears to contribute to reduced efficacy of a given MI towards different polymorphic viruses . Here , a model integrates both biochemical and cellular data to provide a more quantitative estimation of MI inhibition of CA/SP1 cleavage , and thus formation of mature viruses in vivo . The model ( detail in Materials and Methods ) has two terms which describe the observed rate of cleavage ( kclv , ob ) at CA/SP1 by HIV-1 protease in the presence of MIs . The first term describes the cleavage of the immature virus in the unbound state ( B ) ( Fig 5 ) . This term incorporates the innate cleavage rate constant k1 for different polymorphs and the concentration of the MI and its affinity ( Kd ) for that polymorph . This is straightforward , and in accord with a simplified model ( referred to here as model 1 ) in which only unbound state ( B ) is subject to protease cleavage . However , the observation of incomplete inhibition in antiviral assays ( Table 2 , MPI <100% ) and the time-dependent loss in inhibition in in vitro cleavage assays ( Fig 8 ) points to some degree of escape from inhibition as a function of both MI and polymorph , despite saturating concentrations of MI ( Table 2 , MPI <100% ) . To explicitly account for these observations , a second term was incorporated into the model for the rate of cleavage of immature virus in the MI-bound state ( A ) , which is cleaved with rate constant k2 , unique for each MI and polymorph . Essentially , the addition of this second term puts a cap on the maximal degree of inhibition of cleavage ( referred to here as Model 2 ) . Model 2a uses MPI values from the MC antiviral assay , with the concept that viral escape in the multiple cycle infection assay is a more realistic representation of clinical HIV replication ( multiple cycle ) , as compared to the situation in the SC assay ( Model 2b ) . An example of this approach is provided by BVM inhibition of the V370A virus where the antiviral MC MPI is 65 . 4% . This indicates that the fractional degree of cleavage arising from the bound form ( A ) is ( ( 100–65 . 4 ) /100 ) = 0 . 345 of that arising from the unbound form ( B ) . Thus the rate constant k2 = 0 . 345*k1 . Model 1 and 2a reductions in the rates of cleavage of WT , V370A , V362I and ΔV370 by BMS-955176 and BVM are depicted in Fig 9 as log10 reductions in cleavage rates vs . the uninhibited ( no MI ) control . The upper panels ( Fig 9A and 9B ) were modeled with only the first term included ( biochemical data only , model 1 ) , while the lower panel was modeled with both terms included ( biochemical and MC antiviral MPI data , model 2a ) . A key result for model 1 is that based solely on biochemical data , its estimation is in rough alignment with the antiviral results for these variants . A key result of model 2a ( lower panel ) is that there is a plateau in the degree of inhibition that depends on MI and polymorph , a direct result of the inclusion of the antiviral MPI data , bringing model 2a into closer alignment with the antiviral MPI data . A quantitative comparison between BMS-955176 and BVM can be made from the modeling approaches at a selected MI concentration , for example at 300 nM MI ( reductions spanning the entire range of concentrations are plotted in Fig 9 , and tabulated in S1 and S2 Tables ) . At this concentration , BMS-955176 log10 reductions in WT virus ( from the MC MPI data alone ) , log10 reductions in WT VLP cleavage rates ( from model 1 ) and log10 reductions in WT VLP cleavage rates from model 2a are < -2 . 00 log10 , -1 . 98 log10 and -1 . 96 log10 ( Table 6 ) , respectively . For BVM , these values are -1 . 80 log10 , -1 . 75 log10 and -1 . 48 log10 , respectively . Differences among the methods for the two MIs are somewhat larger for V362I ( BMS-955176: -1 . 20/-1 . 85/-1 . 12 vs . BVM: -0 . 73/-1 . 30/-0 . 64 ) , and V370A ( BMS-955176: -1 . 73/-1 . 67/-1 . 41 vs . BVM: -0 . 46/-0 . 85/0 . 36 , respectively ) . Log10 calculated reductions by BMS-955176 toward ΔV370 ( BMS-955176: -1 . 09/-1 . 00/-0 . 76 ) are lower , as compared to wt and V370A , in accord with clinical results for subtype C viruses , [43] while values for BVM indicate essentially no antiviral activity ( -0 . 04/-0 . 33/-0 . 02 ) toward the ΔV370 polymorph . Model 1 wt reductions for BVM ( -1 . 75 ) , are similar to that from the antiviral MPI data ( -1 . 80 ) . Model 1 V362I , V370A and ΔV370 predictions are somewhat larger vs . antiviral data . By comparison , the inclusion of MC antiviral MPI data ( model 2a ) results in lower predicted log10 reductions for wt and polymorphs , in line with the antiviral data for BVM . [18 , 68] Table 6 also contains calculated log10 reductions for wt , V362I , V370A and ΔV370 , using a modification of model 2 in which MPI values are taken from the SC assay ( model 2b , no term for k2 ) . In these cases , model 2b gives similar results to those taken directly from log10 viral reductions calculated directly from the SC antiviral MPI values ( as to be expected given the weight of the SC MPI-derived term in the equation which dominates the response over that of the biochemical-only model 1 ) , and under-predicts the clinical responses . Model 2a time courses for the appearance of cleavage product SP1 peptide by HIV-1 protease for WT , V362I , V370A and ΔV370 VLP , and inhibition profiles by 300 nM BVM or BMS-955176 , are shown in Fig 10 . As further detailed in S1 Table ( model 1 , no MPI data included ) , 300 nM BMS-955176 reduces the rate of cleavage of WT , V362I , V370A and ΔV370 by 95 , 71 , 47 and 10-fold , respectively . BVM is effective at reducing the rate of WT cleavage ( 57-fold ) , less effective toward V362I ( 20-fold ) , much less effective toward V370A ( 7 . 1-fold ) , and ineffective toward ΔV370 ( 2 . 2-fold ) . Model 2a ( S2 Table ) indicates that 300 nM BMS-955176 reduces the rate of cleavage of WT , V362I , V370A and ΔV370 by 91- , 13- , 25- and 6-fold , respectively . By comparison , model 2a indicates that while BVM is effective at reducing the rate of WT cleavage ( 30-fold ) , it is far less effective toward V362I ( 4 . 4-fold ) and ineffective toward V370A ( 2 . 3-fold ) and ΔV370 ( 1 . 1-fold ) . Another way to visualize the results is to compare antiviral dose-response curves to those generated from the models across all concentrations . Plateaus in antiviral inhibition are apparent , particularly for BVM and BMS-1 toward polymorphic variants , as noted in Table 2 . This is shown in Fig 11 , which displays the antiviral dose-responses ( MC assay ) for combinations of BVM , BMS-1 and BMS-955176 toward wt , V362I , V370A and ΔV370 viruses , compared to the calculated values from models 1 and 2a ( exception: the combination of BMS-1 with ΔV370 was not performed ) . The results illustrate that the antiviral data is in better alignment with model 2a compared to model 1 . The data also highlight that binding per se is insufficient to result in a complete antiviral effect ( MPI values of 100% ) for several combinations of MIs and polymorphic variants , for example for BVM and BMS-1 toward V362I , V370A and ΔV370 , despite binding , ( Fig 11 and Table 4 ) . Clinical viral load reduction ( VLR ) data from BMS-955176[41–43 , 70] and BVM clinical trials[18 , 28 , 68] are shown in Table 6 . Clinical VLR reductions were compared to reductions in rates of CA/SP1 cleavage using the different models at a concentration of 300 nM ( fold reduction values relative to no MI added to each particular virus ) . This concentration of MI was chosen for the comparison for two reasons . First , BVM trough concentrations of >20 μg/mL were associated with the best clinical responses[18 , 28 , 68] and based on a BVM antiviral serum shift of 130-fold[35 , 36 , 40] the implied free concentration of 20 μg/ml BVM is 263 nM . Similarly , the clinical response of BMS-955176 in a 10 day Ph2a study reached a plateau at C24 exposures between 713 and 1289 nM[67] ( mean = 1521 nM ) , implying a mean free concentration ( based on a reported free fraction of 0 . 14 ) [40] of 213 nM . Thus , modeling was compared at 300 nM for both MIs . The maximal median decline for subjects having a WT genotype at 40 mg QD dosing by BMS-955176 in a Ph2a POC 10 day monotherapy study was ( -1 . 75 ) log10 ( Table 6 ) . [41] This value is slightly less than both the model 1 and 2a values ( ~-2 log10 ) , and less than the value directly calculated from the MPI in the MC assay ( < -2 . 00 log10 ) . With respect to subjects harboring Gag polymorphisms ( Gag amino acids 362 , 364 , 370 , 371 ) at a dose of 40 mg BMS-955176 , a comparison can be made to V370A , with V370A acting as a kind of surrogate for such polymorphisms ( there is currently no available data breaking out patient responses to individual polymorphic viruses ) . Model 1 values for polymorphs V370A and V362I ( -1 . 67 and -1 . 85 log10 , respectively ) , or the values directly calculated from the MPI in the MC assay ( -1 . 73 and -1 . 20 log10 ) , are similar to the clinical response of BMS-955176 reported for polymorphs ( -1 . 71 log10 ) , while the projected values from model 2a for V370A and V362I , incorporating the MC MPI data ( -1 . 41 and -1 . 12 log10 , respectively ) , are somewhat lower than reported for subjects with these polymorphic genotypes . The model 2b V370A value ( -0 . 61 log10 ) , using single cycle MPI data , greatly underestimates the clinical result for subjects harboring polymorphic viruses , thus suggesting that SC MPI values are likely too stringent , leading to an under-estimation of clinical responses ( Table 6 ) . For BVM , the mean decline for those subjects achieving trough concentrations of >20 μg/mL[28 , 29] with a WT genotype at 250–400 mg QD in a Ph2 14 day monotherapy POC study was -1 . 26 log10 . [18] This value is lower than both the WT model 1 calculated decline ( -1 . 75 log10 ) or the value directly calculated from the MPI in the MC assay ( -1 . 80 log10 ) . The model 2a value ( -1 . 48 log10 ) is closer to the clinical data . A mean -0 . 21 log10 decline was noted in subjects harboring Gag polymorphisms ( Gag amino acids 369 , 370 , 371 ) at doses of 250–400 mg BVM , [68] which may be compared to the V370A and V362I polymorphic viruses used in this study . The model 1 BVM declines ( -0 . 85 and -1 . 30 log10 ) over-predict the clinical response , while the projected declines calculated from the MPI values ( solely from the MC assay ) for these two variants ( -0 . 46 and -0 . 73 log10 ) or model 2a ( -0 . 36 and -0 . 64 log10 ) , respectively , are in closer alignment for these types of polymorphic patient viruses ( -0 . 21 log10 ) . The calculated reductions in CA/SP1 cleavage rates for WT and polymorphic viruses V362I and V370A at 300 nM MI ( Table 6 ) are compared in Fig 12 . Overall , of the models , model 2a , incorporating MC assay MPI values , provides a better correspondence to both antiviral dose response curves ( Fig 11 ) and clinical viral load reductions ( Fig 12 ) .
An early MI failed in the clinic due to inability to inhibit ~50% of viruses containing polymorphic variation in Gag near the site of MI action . The 2nd generation MI , BMS-955176 , is active toward these viruses . In this study we sought to understand the mechanistic origin for the improved antiviral activity of BMS-955176 , and to model this behavior as a function of Gag polymorph cleavage rates , MI affinity and MI concentration , with consideration as to how this information relates structurally to MI binding . Such an approach may have utility in interpreting pre-clinical antiviral results and clinical data on MI action , and may also be helpful in the discovery of MIs with further improvements to potency and spectrum . The higher affinity of BMS-955176 toward Gag polymorphs appears to be a predominant driver for better antiviral activity toward Gag polymorphs ( both lower EC50 values as well as higher MPI values ) . Similarly , higher BMS-955176 affinity is apparently an important driver for the superior performance in in vitro cleavage assays . BMS-955176 inhibition is maintained against WT at all time points ( 4 hours ) , while BVM inhibition is lost over time . Consistent with the overall relationship , in a case where BMS-955176 has a phenotype of partial time-dependent inhibition ( in vitro cleavage for ΔV370 , Fig 8B ) , this was correlated to an elevated FC in antiviral assays ( Table 1 , 6 . 8-fold ) and a reduced MPI value in the MC assay as compared to WT ( 93 vs . 100% ) . One of the features of the inter-relationships of the data is that Kd values are linearly correlated with both MPI values and FC antiviral EC50 values ( R2 0 . 96 ) , with greater affinity providing higher MPI values and lower FC . Antiviral and biochemical data were integrated into a model for calculating the reduction in the rate of cleavage of CA/SP1 by a given MI/polymorphic combination . Modeled reductions in rates of CA/SP1 cleavage by BMS-955176 and BVM were compared to antiviral data in cell culture and viral load reductions observed clinically with these MIs using several models , the most relevant model being one which incorporates both biochemical MI affinities for its Gag target , innate cleavage rates for the viruses and values for MPI from multiple cycle anti-viral data ( Model 2a ) . At a dose of 40 mg QD BMS-955176 in a 10 day monotherapy POC study , the maximal median viral load declines for subjects having WT or polymorphic genotypes were -1 . 75 log10 and -1 . 71 log10 , respectively , in alignment to values calculated from model 2a ( wt: -1 . 96 log10 , V370A: -1 . 41 log10 , V362I: -1 . 12 log10 ) . Similarly , at doses of 250–400 mg QD BVM in a Ph2a 14 day monotherapy study of subtype B patients , the mean viral load declines for subjects having a WT or polymorphic genotype were -1 . 26 log10 and -0 . 21 log10 , respectively , in the range of values calculated from model 2a ( wt: -1 . 48 log10 , V370A: -0 . 36 log10 ) . These studies determined that in vitro inhibition of HIV-1 replication by early generation MIs BVM and BMS-1 does not always reach 100% . It should also be noted that for one polymorphic variant ( ΔV370 ) BMS-955176 also does not always reach 100% inhibition as well; albeit to a significantly reduced degree . This observation with respect to early generation MIs was observed across polymorphs , and was correlated with a reduction in antiviral potency ( increased fold change EC50 values ) by a particular MI toward the particular virus containing that Gag polymorph . For example , in a multiple cycle assay , BVM maximally inhibits the replication of HIV-1 Gag V370A by 65 . 4% , and , in a single cycle assay , by 19% , exhibiting a 54-fold change in its multiple cycle EC50 . These observations suggest that , depending on polymorph and MI , this phenomenon is analogous to one of partial antagonism . In seeking the mechanistic origins of this behavior we initially considered a simplified model for MI inhibition of CA/SP1 cleavage of viral particles in which cleavage only takes place in that fraction of particles not bound to the MI . Thus , CA/SP1 cleavage should continue apace on the MI-unbound particles at a rate determined by the steady state fraction of unbound MI . Because of this , model 1 places no upper limit on the degree of maximal inhibition: at saturating MI concentrations the fractional amount of unbound Gag will approach zero , and thus complete inhibition is to be expected . However , the antiviral phenotype of incomplete inhibition in cell culture at saturating BVM concentrations argues against this simple model , thus suggesting the need for a modification to the model to explicitly include a term which ultimately places an upper value on the degree of maximal inhibition . For this purpose we made use of the MC MPI values , which we interpret as a direct functional readout of viral escape from MI action in cell culture . Parameterizing the biochemical-only model ( model 1 ) required determination of the appropriate biochemical values for the innate rates of HIV cleavage and the affinities of MIs toward assembled Gag virus-like particles . These measurements were made by developing two assays . In the first , we made use of an LC/MS-based assay that directly measures CA/SP1 cleavage vs . time , thus providing rate constants for this process as a function of polymorph . These results showed that Gag polymorphic variants that are less susceptible to inhibition of replication by early generation MIs BVM and BMS-1 ( Table 1 ) are cleaved 2 . 7–9 . 7-fold more rapidly than the WT ( Table 3 ) and they correspondingly exhibit the most pronounced incomplete inhibition profiles ( MPIs <100% ) in antiviral assays ( Table 2 and Fig 11 ) . In a qualitative sense , poorer antiviral coverage of these polymorphs appears to be in part a consequence of poorer MI affinity for Gag , but also is a reflection of a lack of ability of BVM and BMS-1 to fully inhibit when bound , i . e . , consistent with the proposed pathway in which cleavage occurs despite MI binding ( k2-mediated , Fig 5 ) . This results in what is in essence partial antiviral antagonism , as a function of MI and polymorph , which cannot be overcome by merely increasing MI concentration . V362I is more sensitive to BVM inhibition vs . V370A . Though superficially posing a challenge to a model in which efficacy of inhibition of CA/SP1 cleavage is entirely a function of cleavage rate , this is not the case for model 2a , where terms 1 and 2 of equation ( see model for inhibition ) also incorporate the Kd value for the binding of the MI . In this case BVM affinity for V370A is 9 . 1-fold poorer than wt , while BVM affinity for V362I is 2 . 9-fold reduced . This 3-fold higher affinity for V362I contributes , in part , to allowing BVM to maintain , albeit incompletely and right shifted , activity toward V362I , while losing activity toward V370A . Mechanistically , what structural model might explain the result of escape from inhibition , despite binding ? The following proposed model is based on a number of reported observations . First , NMR studies indicated that the superstructure around CA-SP1 in the region of MI binding ( SP1 ) is in dynamic equilibrium between a random coil and an alpha helix . [71] In support of this dynamic equilibrium model , small changes to buffer and detergent alter the helicity of the SP1 region [72] while point mutations predicted to reduce helicity destroy particle production . [73–75] Earlier cryo-electron tomography work on immature particles found that the extension of SP1 from the C-terminal region of CA could be fitted as a six-helix bundle , leading to a proposal that cleavage at CA-SP1 acts as a molecular switch , facilitating the final conformational changes required for capsid rearrangement and core condensation . [76 , 77] A deeper structural understanding is now at hand with the report of a cryo electron tomography structure of the immature assembled Gag lattice at 3 . 9 angstrom resolution and a crystal structure reported at at 3 . 2 angstrom resolution [61 , 62] The structures indicate that the CA-SP1 cleavage site is hidden within this 6-helix bundle , and protected from cleavage due to inaccessibility , a structural explanation for why cleavage at this site is the slowest of the Gag cleavages . [15] MI binding is suggested to rigidify the structure and likely shifts the equilibrium of the superstructure in favor of the 6-helix structure , thus reducing the propensity for unraveling and presentation of the cleavage site . This is in accord with a report that BVM binding increases the stiffness of immature virions . [78] The formation of a more ordered helical state as a consequence of MI binding in this region , shown by cross linking studies of BVM analogs at sequences overlapping or proximal to the CA-SP1 cleavage site , is also consistent with previous biochemical data on the effect of bevirimat on Gag processing , and with genetic data from resistance mutations . [60] The results reported in this study are in alignment with these structural results and proposal for the role of polymorphic or MI resistance changes which increase cleavage site presentation . As compared to wt , the more rapid innate rates of CA/SP1 cleavage of certain polymorphs are therefore explainable as a reflection of a decrease in the stability or equilibrium concentration of the bundle , i . e . , the inherently greater degree of disorder in the cleavage region allows for the presentation of the protease recognition site in its extended conformation a greater proportion of the time . The modeled biochemical and viral data , which showed improved inhibition of in vitro cleavage and higher maximal antiviral inhibition by BMS-955176 are consistent with a global explanation for the broader antiviral coverage of BMS-955176 vs . BVM: the increased affinity of BMS-955176 for its binding site increases the concentration and perhaps structural integrity of the quarternary structure of the assembled 6-helix bundle This thereby decreases dynamic fraying of the structure which would otherwise lead to protease cleavage . With respect to the observed phenotype of partial antagonism by certain MI/polymorph combinations , the data suggests that binding in and of itself is not always sufficient to induce changes in the local geometry needed to completely prevent protease recognition of CA/SP1 and thereby completely block cleavage . This may be the case for V362I vs . V370A . While these two polymorphs are cleaved with similar rates ( Table 3 ) , they exhibit differing MPI values depending on MI . The wt MI-bound Gag structure is likely innately more ordered to begin with , while polymorphic variants , with greater innate flexibility and reduced local order , retain some bias in this direction , despite MI binding ( Fig 5 , pathway 2 ) , rendering them partly susceptible to cleavage even in the MI-bound state . This suggests that depending on the particular effects induced on the local conformation by a given polymorphic change and the particular binding poise of an MI , the consequences of that binding may be only partially transmitted to the key conformation changes that are meaningful for antiviral activity , that of maintaining reduced access of the CA/SP1 cleavage site to protease . Thus , biochemically one observes a time dependence to the in vitro cleavage inhibition , while in antiviral assays , less than maximal antiviral inhibition . This is an escape mechanism . At the structural level , the ability of the 2nd generation MI BMS-955176 to induce greater protection from cleavage of polymorphs is possibly due to additional binding contacts within the Gag structure , reflected in its higher binding affinity , but a detailed explanation must await MI bound structures . Such binding presumably contributes to a greater stabilization of that local conformation ( presumably increased helicity of SP1 ) which renders the system less sensitive to protease recognition/cleavage . Further , while the generality of the conclusion that faster innate rates of polymorphic cleavage are a reflection of greater flexibility and accessibility of the CA/SP1 site to protease recognition and cleavage seems therefore to be sound , further studies are needed to understand the structural details of MI binding , in particular to shed light on those cases where saturable binding is still not maximally productive ( partial antagonism ) . Given the similar dissociative off rates of BMS-955176 toward wt and ΔV370 VLPs , but the higher affinity toward wt , the calculated rate of association toward the ΔV370 variant is implied to be ~9-fold slower than wt ( from consideration of a simple 1 step binding model ( kon = koff x Kd ) . This slower on rate may reflect a more unstructured MI-unbound structure in the vicinity of the ΔV370 MI binding site ( as compared to , for example , V370A , with a calculated relative on rate similar to wt ) . From the published structure , position 370 is at the end of the 6-helix bundle , so potentially deletions in this region introduce unzipping and greater local disorder , with such a disordered state obscuring the trajectory of MI binding , and thereby inducing an entropic penalty to binding . While further work is clearly needed to more fully understand the relationship of modeled to antiviral and clinical results , the approach described herein to understand MI activity and mechanism should prove useful to potentially facilitate further improvements to MI potency and coverage . | HIV-1 continues to be a serious health threat , with nearly 40 million infected individuals worldwide . Despite effective treatment options , issues with resistance and drug toxicities illustrate the need for new drugs with novel mechanisms . Maturation inhibitors ( MIs ) block a key protease cleavage within its target , preventing formation of infectious HIV-1 virus . A first generation MI , ( bevirimat ) , failed in clinical studies due to lack of broad spectrum activity , a result of amino acid polymorphisms around the site of action . BMS-955176 ( GSK3532795 ) is a second generation MI active against these polymorphisms , and is currently in a Phase 2b study . We used a combination of antiviral and novel biochemical approaches to understand the mechanism for these spectrum differences . We find that while bevirimat exhibits incomplete antiviral activity , even at saturating drug concentrations , BMS-955176 exhibits greater ability to maximally inhibit these viruses , in part due to higher affinity for its target . These data were integrated into a semi-quantitative kinetic model whose outputs are in accord with in vitro antiviral observations and correlate with observed in vivo MI efficacies and the results of recent crystal and cryo-electron tomography structures . Our findings offer insights into MI activity and mechanism and may prove useful to help guide development of new MIs , with potential applicability to other virus systems and inhibitors . | [
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"... | 2016 | Mechanistic Studies and Modeling Reveal the Origin of Differential Inhibition of Gag Polymorphic Viruses by HIV-1 Maturation Inhibitors |
This paper presents a method for automated detection of complex ( non-self-avoiding ) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects . Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers , where data from traditional trackers is limited . We restrict the input available to the algorithm to a single digitized frame , such that manual initialization is not required and the detection problem becomes embarrassingly parallel . Consequently , the proposed algorithm does not propagate detection errors and naturally integrates in a “big data” workflow used for large-scale analyses . Using this framework , we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes . Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals ( e . g . , for mating assays ) or different species .
The nematode Caenorhabditis elegans is a simple animal model system , widely used to study the genetic foundations of behavior . Among its key advantages are its tractable genetics , short life cycle , relatively simple anatomy and behavior patterns , and evolutionary conserved pathways [1–3] . The locomotion patterns of C . elegans have been extensively studied . Historically , this was largely done relying on visual phenotyping . In recent years , several machine vision tools have been developed for automated posture analysis , collectively referred to as “trackers” , and spanning a range of capabilities [4–10] . Accurate identification of head and tail and reconstruction of the midline of the body are important steps in automated analyses of C . elegans postures . Typically , the topological genus of images of wild-type animals is zero , i . e . , the body image only rarely forms closed loops . However , loops are observed in coiling mutants and more rarely in wild-type or other mutants . Existing trackers were rarely used to automate the study of severe coiler phenotypes , plausibly because in such cases they either require frequent manual intervention or may misidentify the posture [11–14] . Therefore , we refer to such non-self-avoiding postures as complex . Generative statistical models describe the expected images given a particular posture . This expectation is formulated in terms of a probability distribution , referred to as the data or likelihood term [15] . Knowledge about the data , such as expected body length or smoothness , is accounted for by specifying an a priori distribution of postures . The algorithm then optimizes the posterior probability of the posture , i . e . , the product of the likelihood and the prior term . This framework can enable the identification of complex postures . Here , we present a posture detection method based on generative statistical models and a coarse-to-fine strategy . Our approach allows a computationally efficient implementation and yields reliable detections of many complex postures . First , a small set of characteristic features for the head and body regions is defined as functions of oriented edges in the image . Next , we formulate a statistical model describing the likely configurations of these features given a hypothesized posture . At run time , we search for the maximum a posteriori posture of the worm given the observed image . This calculation yields a coarse outline of the animal . To refine the outline , a second statistical model is employed which directly uses the edge information in the image and hence enables more precise identification . The advantage of this coarse-to-fine technique is computational efficiency . The coarse search runs over a grid which is much smaller than the original image grid . The fine search is then initialized using the result of the coarse detection and is only required to explore a small subset of possibilities . Searching for the posture at the fine scale without considering the information obtained from a coarse search would have been computationally intractable . To demonstrate the utility of our method , we assayed wild-type animals and several mutant strains that were previously associated with a coiler phenotype .
A coarse statistical model was defined to identify approximate positions of the head , tail and midline of the animal . The model is based on prominent features of the head and the body that are identified on a coarse-grained grid H , in which every point corresponds to a block of pixels in the original image grid G . Through trial and error , we found that a coarse grid unit length of one quarter of the width of the worm offered a good tradeoff between efficiency and accuracy . The key components of this model and the resulting detection are descried below . The estimated point sequence θ→ on the coarse grid facilitates finding the boundaries of the animal body at the resolution of the original grid , from which a refined midline can be derived . The key components of this model and the resulting detection are described below . To test the proposed algorithm we assayed wild-type animals and mutants that were previously reported to exhibit coiling . Of particular interest were three strains with severe locomotion defects . The Gq protein alpha subunit ortholog , encoded by egl-30 , was shown to affect locomotion , viability , egg laying , synaptic transmission , and pharyngeal pumping [16–28] . The voltage-insensitive cation leak channel , a subunit of which is encoded by unc-77/nca-1 , assists transmission of presynaptic activation from the cell body to the synapses [29 , 30] . The unc-8 gene encodes a putative mechanosensory channel [31] . A gain-of-function ( gf ) allele of either of these genes result in exaggerated body bends and coiling [27 , 30 , 31] . Examples of successfully detected complex postures for six mutants that display a coiler phenotype are shown in Fig 3E and S2 Movie . In this work , anterior coils were defined as periods when the head was in close proximity to any point along the body ( within 5% of the midline ) . Posterior coils were similarly defined for the tail . We note that these definitions were not mutually exclusive ( Fig 4A ) . The rate of detections using the generative statistical algorithm was compared to that of a previously described image analysis tool [9] , which uses a standard morphological approach for single-frame detection that solely relies on the contrast between object and background ( see also the Discussion section ) . The statistical approach yielded midlines of appropriate length for >90% of the images and these midlines were very similar to those obtained using the morphological approach , when the latter was available . For coiler mutants , the differences between the two algorithms mirrored the abundance of coils , indicating that detecting complex postures was key to the observed improvements ( Fig 4B ) . As a coarse measure of the severity of different coiling defects , the durations and frequencies of coiling were measured for each of the strains tested . Typical timescales were obtained by fitting the data to a Weibull distribution [32] ( Fig 5A ) and the full distributions for a severe coiler , egl-30 ( gf ) , are depicted in S2 Fig . The proposed algorithm thus enabled us to obtain a nearly continuous record of posture dynamics , uninterrupted by coiling . To examine the relation between coiling and locomotion , we derived the propagation of dorsoventral body-bends from the time-series of postures as previously described ( see Materials and Methods section and [9] ) . Coiling events in wild-type animals were rare , their durations were short , and they rarely interrupted directed locomotion ( Fig 5A and 5B ) . In contrast , the majority of bouts of directed locomotion were interrupted by coiling in egl-30 ( gf ) , and unc-8 ( gf ) mutants and coils were longer and more frequent than wild-type in these mutants ( Fig 5A and 5B ) . During continuous periods in which the posture of C . elegans is non-self-avoiding , the directional propagation of body bends can be disrupted to varying degrees . Therefore , an alternative measure of the impact of coiling can be obtained by asking how it affects directed locomotion . To address this , we measured the propensities for directed locomotion during coiling events . In each case , these propensities were compared to their baseline values , i . e . , when coiling was absent ( Fig 5C ) . Wild-type animals mostly progressed forward during a coil . This was the case since wild-type coiling was largely caused by Ω-turns which facilitate turning and are not detrimental to directed locomotion ( see below ) . In egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants , propensities for forward or backward locomotion exceeded their baseline levels during anterior or posterior coiling , respectively ( Fig 5C ) . Thus , in these mutants directed propagation of dorsoventral bends could be sustained despite coiling and , as further shown below , locomotion and coiling were likely linked . Taken together , these results suggest that the proposed statistical approach can be used to characterize coiler phenotypes . Principal component analysis ( PCA ) was proposed as an unbiased and efficient approach for describing C . elegans behavior [7] . It has been used to characterize the dimensionality and dynamics of locomotion , as well as behavioral motifs [7 , 8 , 33 , 34] . When complex postures are largely inaccessible , two of the leading modes describe sinuous oscillations associated with directed locomotion and a third is associated with turning [7 , 34] . We constructed an ensemble of complex postures by equally sampling the coiled postures of the mutants we assayed . The resulting two leading modes were associated predominantly with anterior and posterior curvature . Thus , the severity and direction ( dorsal/ventral ) of anterior and posterior coils corresponded to the amplitudes and signs of these modes , respectively ( Fig 6A and 6B ) . The third mode contributed opposite curvatures to the edges and the mid-body . Additional modes introduce higher order corrections and more than 95% of the variance in the data was accounted for by the leading six modes ( Fig 6A and 6B ) . Identifying typical coiling postures is ambiguous due to their broad distributions . Nevertheless , a heuristic definition can highlight prominent features and provide a useful starting point . We used k-means clustering to sub-divide the dataset of amplitudes of the modes composing coiled postures . Scree plots [35] would typically lead to dividing a coiler dataset to k = 6–8 similarly sized clusters . However , we found that generating a larger number of smaller clusters was useful: the centroids of the most populated small clusters resembled postures that were frequently observed in the raw data . Representative examples of cluster centroids and the postures that were reconstructed from them were projected onto the plane of the two leading modes and depicted in Fig 6B . The dynamics of the amplitudes during continuous periods of coiling could vary between different animals and different types of coils . At the two extremes , the duration of a coil could be spent in a static posture that is not easily released or in a continuous sequence of exaggerated body-bends ( also referred to as loopy motion ) . The amplitudes of the leading modes demonstrate that posterior coils of egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants were highly dynamic , such that ventral ( positive amplitudes ) and dorsal ( negative amplitudes ) coiling were averaged out while the animal exhibited a continuous succession of non-self-avoiding postures . In contrast , anterior coils of unc-8 ( gf ) and unc-3 mutants were characterized by locking into a static coiled posture ( Fig 6C ) . More detailed information can be obtained from focusing on specific families of coils . We defined a spool as any posture for which the product of the two leading amplitudes , a1·a2 , was larger than unity , i . e . , anterior and posterior curvatures were sufficiently high and in the same direction . The centroids of the 10 most populated clusters of postures that satisfied this condition spanned the observed range of loops typical of Ω-turns to compact spirals . The shaded areas in the left panel of Fig 6D represent the convex hulls of these centroids for wild-type animals and four coiler mutants . The surrounding postures were reconstructed from 26 of these centroids . The center of the panel , where a1·a2 was small , was populated by loops that could typically be observed during Ω-turns . As a1·a2 grew larger , we preferentially observed spirals in which the head was at the center in unc-8 ( gf ) and unc-122 mutants . In contrast , we observed a significant fraction of spirals in which the tail was at the center in egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants . Taken together with the dynamics of the amplitude , these data suggested that the two pairs of mutants preferentially formed spirals differently . Exaggerating an anterior bend prevented dorsoventral undulations and developed into a static head-centered spiral , perhaps through proprioceptive coupling , i . e . , the trigger that compels body regions to bend in the same direction as their anterior neighboring region after a short time delay during forward locomotion [36] . Reversing into an exaggerated posterior bend formed a tail-centered spiral that did not suppress dorsoventral bending and was more rapidly released . Assaying the propagation of body-bends concurrently with coiling ( described below ) supported this interpretation . PCA analysis of spools can also be used to assess the severity of a defect . Wild-type animals rarely exhibit postures for which a1·a2 >4 , but coilers do ( Fig 6D , right panel ) and this trend was not sensitive to the exact value of the threshold . Projecting the sub group of spools onto its own low-dimensional space could facilitate testing more detailed hypotheses . Intuitively , the resulting three leading principal components corresponded to nearly uniform curvature , tightening of anterior bending , and tightening of posterior bending ( S3 Fig ) . Similarly applying the condition a1·a2 < −4 would result in charactering number-8-like coils where anterior and posterior curvature have opposite signs . These results demonstrate that complex posture recognition can be integrated with existing analysis methods for large scale and unbiased studies of severe locomotion defects . Is the initiation of directed locomotion particularly favorable for coiling in certain mutant backgrounds ? To address this question , we assayed the temporal dynamics of locomotion upon entering and exiting a coiling event ( Fig 7 ) . In large part , wild-type coiling resulted from omega turns: acute turns composed of a reversal , an Ω-like posture , and forward locomotion in the new direction [37–41] . As a result , a rise in the propensity to reverse was observed shortly prior to coiling and high levels of forward locomotion were observed immediately following the coil ( Fig 7A ) . In the cases of egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants , the signature of a reversal-to-forward switch was detected immediately prior to entering an anterior coil ( Fig 7B and 7C , middle panels ) . Upon posterior coiling , these mutants exhibited the opposite behavioral switch ( Fig 7B and 7C , right panels ) . However , similar trends were not observed in other coiler mutants ( Fig 7D and 7E ) . As a complementary measure of the association of coiling with locomotion transitions , we measured the fraction of coiling events that occurred within 5 sec from the initiation of directed locomotion . The signature of wild-type Ω-turns could be clearly detected: a large fraction of all coils promptly followed the initiation of forward locomotion after a reversal ( Fig 8A ) . In coiler mutants , an exaggerated posterior body-bend upon a forward-to-reversal switch could increase the likelihood of coiling shortly following the initiation of the reversal . This trend ( and the opposite one for anterior coiling ) was displayed by egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants but not by other coilers ( Fig 8A ) . Our analysis typically identified brief periods of dwelling during transitions between forward and backward locomotion . Therefore , to visualize selected behavioral trends at the termination and initiation of directed locomotion , we aligned the data at the initiation and termination of short bouts of dwelling . Locomotion was then compared between two sub-categories of the full dataset: events in which , shortly following the onset of dwelling , anterior coiling was identified or no coiling was detected ( Fig 8B; additional examples shown in S4 Fig ) . Coiling upon switching by egl-30 mutants manifested as an exaggerated reversals peak prior to dwelling and elevated forward propensities following dwelling . Thus , in egl-30 ( gf ) and unc-77/nca-1 ( gf ) mutants , the dorsoventral bends that initiate directed locomotion may be more likely to exaggerate and result in coiling than those that follow . Are coiler phenotypes asymmetric with respect to the dorsoventral axis ? The deep head bend of an Ω- turn is known to be ventral ( Fig 9A ) [37–41] . However , ectopic deep bends could potentially arise from the misregulation of bending in either direction . Interestingly , posterior coiling of unc-77/nca-1 ( gf ) mutants was more likely when the tail bent dorsally ( Fig 9B ) . The asymmetry in the bending direction of the tail also manifested as higher dorsal ( as compared to ventral ) posterior curvature in the period leading to a coil ( Fig 9C ) . The NCA-1 leak channel was recently implicated in persistent motor circuit activity required for sustaining locomotion [42] . Curiously , the gain-of-function of UNC-77/NCA-1 was shown to eliminate some of the spontaneous activity in muscles ( miniature postsynaptic currents ) [30] . The asymmetric behavior of unc-77/nca-1 ( gf ) mutants can lead to hypotheses regarding the structure and function of the backward motor circuit . For instance , given the expression of unc-77/nca-1 in AVA premotor interneurons , AVA may be capable of asymmetrically activating dorsal and ventral motoneurons . Alternatively , unc-77/nca-1 may be asymmetrically expressed in motoneurons [43] or not expressed in AS neurons which innervate only dorsal muscles [2 , 30 , 44] . In the latter case , AS may play a role in maintaining dorsoventral balance . We note that the initiation of coiling is not generally restricted to the initiation of directed locomotion . To demonstrate this we examined animals carrying a gain-of-function mutation in the unc-8 gene , encoding a putative mechanosensory channel [31] or a loss-of-function mutation of unc-122 , affecting postsynaptic neuromuscular signaling [45] . Neither of these mutants exhibited the signature peaks associated with switching before coiling ( Figs 7D , 7E , 8D and 8E ) . Curiously , unc-8 ( gf ) was the only mutant we examined that exhibited significant anterior coiling while reversing , as evident by the unique rise of reversal probability prior to anterior coiling ( Fig 7D left and middle panels ) . These data indicate that the proposed statistical model can be used for testing detailed hypotheses regarding cellular and molecular locomotion mechanisms .
The standard approach for identifying C . elegans in a digitized image applies simple morphological operations and/or heuristically motivated processing steps [5] . Typically , a background subtraction step is followed by thresholding to obtain a binary image . The largest connected component in the binary image is identified as the animal . Next , morphological closing ( dilations followed by erosions ) or morphological hole filling is applied and a skeletonization algorithm computes the midline of the body . The head is distinguished from the tail either by manual inspection or by comparing the regions in vicinity of the end points of the midline . Typically , when imaging in “artificial dirt” chambers , the brighter region is associated with the head . Alternatively , the boundaries of the body in the binary image are determined by subtracting an eroded version of the image ( or an equivalent edge detection method ) . A spline can then be fitted to all boundary points and the end with the higher peak curvature is associated with the tail [10] . If visual inspection is feasible and the duration of the measurement is limited , manual detection of the head and information about the motion of the center of mass can be used to resolve situations where parts of the worm overlap [11–14] . Such approaches are limited in their ability to reliably detect complex ( non-self-avoiding ) postures based on a single frame . The approaches described in [11–13] have been applied to non-self-avoiding postures ( including some cases of self-crossing midlines ) and implemented commercially . They are based upon a geometric model for postures and a motion model for deformations of postures during locomotion . The posture in a given frame is assumed to be a small deformation of the posture in the preceding frame . Given this assumption , complex postures are resolved by tracing them back to simpler ones . These approaches require an initially resolved simple posture that sufficiently resembles the complex one . The simple posture is either provided manually [11] or assumed to be automatically attainable [12 , 13] . Once such an algorithm loses track of an animal it cannot autonomously recover , but may resume tracking given manual input [11] . These and similar approaches were not specifically designed to address severe phenotypes such as the prolonged continuous periods of coiling exhibited by egl-30 ( gf ) mutants . Correspondingly , in published datasets , they were strictly applied to short video sequences in which bouts of coiling were brief . Our formulation of the object recognition problem is qualitatively different: we introduce sparse visual features that enable single-frame detection as opposed to solely relying on the differences in brightness between the imaged animal and its background . In addition to minimizing error propagation and manual intervention , single-frame detection can be parallelized easily and applied efficiently to large datasets . An enhancement of the standard morphological methods is described in [46] , where the skeleton could be determined for omega or spiral shaped postures . In this work , sophisticated heuristics were used to locate and dissect instances of self-touching for certain coiled body configurations . However , this approach is limited to specific postures and cannot be easily generalized . In addition , a laterally coupled snake model was developed for accurate contour detection of coiled animals [47] . When faced with complex postures , this method requires initializations that are close to the correct posture and therefore cannot be used in high-throughput , automated , applications . Importantly , existing methods do not provide a measure of quality of detection , as they lack a cost function that allows comparison of candidate solutions in a meaningful way . A key advantage of a global generative statistical model is that it is principled: it enables to quantitatively assess the plausibility of the detected posture and can be naturally adapted to different experimental circumstances . An additional advantage of the proposed approach is its scalability . Analyzing a single frame at a time ( rather than relying on neighboring frames ) is an embarrassingly parallel problem , i . e . , one that requires no dependency or communication between the parallel tasks , and eliminates propagation of errors . We implemented our algorithm using open source , freely available tools and libraries that are virtually guaranteed to be available at any research-computing environment . Therefore , our implementation can seamlessly be incorporate in a “big data” workflow for the timely analysis of large volumes of data . In order to apply our approach to a different species it would be necessary to identify distinct visual features analogous to the edge formations described here . For this work it was sufficient to represent postures as a sequence of instantiation points but more sophisticated representations can be used instead . To summarize , we presented a computationally efficient method , which correctly detects the posture of C . elegans in a variety of complex cases where standard morphological operations are inadequate . If higher precision is required , our fine detection method can be extended to a more computationally expensive procedure , e . g . , an additional stage of further refinement . The presented analysis of coiler mutants demonstrates the flexibility and usability of this method for generating and testing detailed hypotheses .
C . elegans strains were maintained and grown according to standard protocols [1] . The following strains were used: wild-type strain N2 , CG21 egl-30 ( tg26 ) ; him-5 ( e1490 ) , DR1089 unc-77 ( e625 ) , CB15 unc-8 ( e15 ) , CB4870 unc-122 ( e2520 ) , CB151 unc-3 ( e151 ) , CB719 unc-1 ( e719 ) . Animals were grown at 20°C on standard NGM plates seeded with E . coli OP50 bacteria . Mid to late L4 individuals were sealed into individual “artificial dirt” chambers filled with an overnight OP50 culture concentrated tenfold and resuspended in NGM medium [48] . Animals were imaged at 10 frames per second at a 4 . 2x magnification for posture-based analysis using a CCD camera ( Prosilica GC2450 , Allied Vision Technologies , Stadtroda , Germany ) . Motion and quiescence were identified using previously described methods [49] . The proposed algorithm for identifying body features was implemented in Python ( performance-critical parts were programmed in Cython ) and integrated with our previously described suite of image analysis tools , called PyCelegans [9 , 49] . In brief , once we identified the body midline , head , and tail in each frame , each midline was divided into 20 equal intervals and the relative angles of all 18 next-nearest neighbor interval pairs ( corresponding to the curvature of the body ) were calculated . The dynamics of these 18 relative angles were used to identify quiescence and directed locomotion states . The propagation of body bends from anterior to posterior or vice versa was defined as forward or backward locomotion , respectively . Complete lack of motion was defined as quiescence . All other states were defined as dwelling . Although directional propagation of body bends corresponded well to centroid motion , directed locomotion could be scored even if the animal was slipping and the centroid was not propagating in the laboratory frame of reference . Data analysis was performed using custom Matlab scripts ( Mathworks Inc . , Natick MA ) . Our source code and documentation are publicly available at https://github . com/david-biron/pycelegans-2 . 0 . Data analysis was performed using custom Matlab scripts . For comparisons in summary statistics panels , significance was calculated using a one-way ANOVA test . Post-hoc correction for multiple comparisons was performed using the Bonferroni adjustment . For the purpose of performing principal component analysis ( PCA ) , the posture of the animal was represented using the same 18 relative angles between next-nearest neighbor intervals that were used for the analysis of locomotion . For the purpose of calculating the principal modes , approximately 5 , 000 anterior coiled frames and 5 , 000 posterior coiled frames ( or spooled , for S3 Fig ) were randomly picked from the full dataset of each of the six coiler mutants assayed . Thus , PCA modes were calculated using a total of 60 , 000 frames . K-means clustering was performed with a redundancy of 5 using k = 50 clusters for Fig 6 and k = 25 for the more restricted set of spools ( S3 Fig ) . Results were not sensitive to an exact choice of the number of clusters , k . | The roundworm Caenorhabditis elegans is a widely used model organism . Its locomotion , for instance , enables the study of genetic and cellular mechanisms that underlie behavior and may be broadly conserved . Characterizing C . elegans locomotion requires identifying its body posture and tracking how posture changes with time . Existing machine vision approaches have greatly aided this effort . However , they have been limited in cases where the body of the animal curved strongly such that one part of the animal rested or slid against another part . We present a method for automated detection of such complex body postures and its application to the analysis of locomotion . At the core of our method are progressively detailed statistical models of the shape of the animal . These models enable us to assess the probability that a given image contains a suggested posture . Our approach does not require manual initialization and can be readily parallelized for large-scale applications . We used our approach to analyze locomotion in mutants that severely exaggerate their body bends , called coilers . This approach can readily be extended to additional automated tracking tasks such as pairs of interacting roundworms or different organisms . | [
"Abstract",
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] | [] | 2015 | A Generative Statistical Algorithm for Automatic Detection of Complex Postures |
We previously showed the existence of selective pressure against protein aggregation by the enrichment of aggregation-opposing ‘gatekeeper’ residues at strategic places along the sequence of proteins . Here we analyzed the relationship between protein lifetime and protein aggregation by combining experimentally determined turnover rates , expression data , structural data and chaperone interaction data on a set of more than 500 proteins . We find that selective pressure on protein sequences against aggregation is not homogeneous but that short-living proteins on average have a higher aggregation propensity and fewer chaperone interactions than long-living proteins . We also find that short-living proteins are more often associated to deposition diseases . These findings suggest that the efficient degradation of high-turnover proteins is sufficient to preclude aggregation , but also that factors that inhibit proteasomal activity , such as physiological ageing , will primarily affect the aggregation of short-living proteins .
Biological networks are fine-tuned to respond to narrow changes in protein concentration . The ability of a cell to maintain metabolic and signal transduction fluxes is therefore highly dependent on a tight regulation of its proteostatic network [1] . The capacity of the protein quality control system to regulate protein folding and degradation erodes with age , resulting in increased protein aggregation and aggregation-associated diseases [2] , [3] . Which proteins first fall prey to misfolding is most likely a stochastic process that is modulated by both tissue-specific expression levels and environmental factors [4] . However , sensitivity to protein aggregation is also determined by intrinsic protein parameters such as the efficiency of the folding process [5] , thermodynamic stability [6] , [7] , the aggregation propensity of the protein sequence [8] , [9] and its ability to be recognized by the protein quality control system [10] . We previously showed that evolutionary forces shape protein sequences in order to minimize their aggregation propensity , by strategically placing aggregation-opposing gatekeeper residues along the sequence [11] , [12] . Although this insight has been confirmed by independent studies [13] , [14] , [15] , [16] , the extent to which selective pressures mould protein sequences is most likely not uniform , but determined by the biological context in which the protein functions [17] . For instance , it has been shown that proteins with high expression levels on average have a lower aggregation propensity than proteins with lower expression levels [18] . We reasoned that proteins with high turnover rate and thus short lifetime will have , on average , lower risk of misfolding than long-living proteins . Their respective sequences should therefore also experience different selective pressures against protein aggregation . Such evolutionary pressure might have resulted in different affinities towards molecular chaperones and different implications towards aggregation-related diseases . In order to determine the relationship between protein lifetime and protein aggregation we here combine experimental lifetime measured for 611 proteins [19] with the corresponding gene expression data in 532 healthy individuals . We also correlated experimental chaperone interaction data and structural information of these proteins to their aggregation propensity using TANGO [20] , an algorithm that accurately predicts the intrinsic aggregation propensity of protein sequences . This analysis resulted in two major observations: i ) short-living proteins on average are predicted to have longer and more severe aggregating regions than long-living proteins , and ii ) the evolutionary enrichment of aggregation breaking gatekeeper residues is less pronounced in short-living proteins , suggesting that they experience milder selective pressure to minimize aggregation . Further , we also found significantly less interactions between short-living proteins and molecular chaperones in the IntAct database [21] . Our results suggest that under normal circumstances , protein aggregation of short-living proteins is not problematic , and thus there is little evolutionary pressure to reduce the intrinsic aggregation propensity or optimize chaperone interaction . This would turn such proteins into the Achilles' heel of the proteome in conditions where proteasomal function is significantly reduced , such as is reported for normal human ageing [22] , [23] , [24] , [25] . In support of this hypothesis , we found that all but one of the proteins with experimentally determined turnover rates that are involved in a protein deposition disease belong to the fastest turnover rate group .
The current study focuses on short-stretch mediated protein aggregation , where specific segments of a polypeptide chain assemble into an intermolecular beta-sheet and thus nucleate aggregation . Since current knowledge in the field suggests that the short-stretch mediated protein aggregation covers the majority of disease-associated protein deposition , and no reliable prediction methods exist for alternative protein aggregation mechanisms , we feel justified to ignore alternative aggregation mechanisms such as 3D domain swapping and native protein aggregation . Like all current protein aggregation prediction algorithms , TANGO calculates intrinsic aggregation propensity of an input polypeptide sequence and returns short stretches predicted to have a high propensity to nucleate protein aggregation through the formation of intermolecular beta-sheets . These regions constitute the intrinsic aggregation propensity of the sequence in the absence of globular structure . Since these aggregation prone regions are nearly always part of the hydrophobic core when the protein resides in its native conformation , the aggregating stretches identified computationally need to become exposed by ( partial ) unfolding of the protein before they can actually nucleate protein aggregation . So , although three dimensional relationships that existed in the folded state are no longer relevant during assembly into an intermolecular beta-sheet , they are highly relevant to determine if a particular region is likely to become exposed in the first place . In order to estimate the likelihood that a given short polypeptide segment may become exposed by ( partial ) protein unfolding , we employ the FoldX force field , which calculates the contribution of each amino acid to the thermodynamic stability of the three dimensional structure of the protein , thus allowing to determine if an aggregation prone region is in a stable or less stable part of the structure . The statistical mechanics algorithm TANGO [20] was used to determine the aggregation-prone regions in the human proteins . This resulted in an aggregation propensity ( 0–100% ) for each residue , whereby an aggregating segment is defined as a continuous stretch of at least five consecutive residues , each with a TANGO score higher than 5% . The five positions before and after aggregation-prone regions are considered as “gatekeeping flanks” , with each P , R , K , E or D counting as gatekeepers [17] . No distinction was made between gatekeepers at the N or C terminus of the aggregating stretch . Our dataset was composed of 532 HG-U133_Plus_2 type microarray experiments extracted from GEO ( Gene Expression Omnibus ) [26] . Queries were carried out using GEOmetadb module from R [27] . The dataset is composed of cancer healthy control samples only . HG-U133_Plus_2 microarrays contains probe sets of 54675 human genes per chip . All 532 chips were preprocessed in one single block using robust multichip average ( RMA ) . RMA processing consists of three steps: background adjustment , quantile normalization and finally summarization . A list of common housekeeping genes ( EIF4G2 , RPL9 , SFR9 , GUK1 , H3F3A , RHOA , ACTB ) was used to confirm that the expression levels remain constant for the whole dataset . The dataset was divided into two subsets according to long-living and short-living proteins . Conversion of Affymetrix to Uniprot identifiers was done using Babelomics4 id converter [28] , [29] . Structures were selected according to the following criteria: ( 1 ) 100% sequence identity with the sequence of interest , ( 2 ) crystal structure , ( 3 ) resolution at least 3 Ä . All modeling was performed using the FoldX 2 . 8 force field and tool suite [30] , [31] . All structures were repaired using the RepairPDB command and homology models were constructed using the BuildModel command . The stability of the aggregation nucleating regions was extracted using the SequenceDetail command . Comparison of the distributions for each parameter tested in the analysis of short versus long-living proteins was performed using Mann-Whitney and Kolmogorov-Smirnov tests .
Yen et al . developed a global stability analysis , a high throughput approach for proteome-scale protein-turnover analysis , resulting in a protein stability index ( PSI ) for 8000 human proteins [19] . PSI scores ranges from 1 to 7 , with higher values indicating higher biological protein stability and thus slower protein turnover . To simplify the analysis , we used a low and a high cut-off value to eliminate proteins with intermediate lifetime , so that the data were split in two groups of short ( PSI ≤ 2 ) versus long-living ( PSI ≥ 5 ) proteins ( Text S1 ) . A number of characteristics of the aggregation propensity of these 611 proteins were determined using the TANGO algorithm [20]: i ) the average aggregation propensity of the protein ( total TANGO score normalized by protein length ) , ii ) the number of aggregating segments in the protein , iii ) the length of aggregating segments , and iv ) the aggregation propensity of each aggregating segment . The correlation with the experimentally determined biological lifetime of the protein was tested for each individual parameter and significant differences were found ( Text S1 ) : Short-living proteins display a higher average aggregation propensity ( Figure 1A ) , which is not caused by an increase in the average number of aggregating segments ( Figure 1B ) , but by an significant increase in their length ( Figure 1C ) and aggregation propensity ( Figure 1D ) . As previous studies have shown that long proteins on average have less effective aggregation-promoting regions than shorter proteins [32] and the average length of short and long-living proteins is respectively 263 and 357 amino acids , the aforementioned observations could also be due to the longer mean length of long-living proteins . In order to exclude this possibility , we repeated the analysis after the exclusion of proteins longer than 300 amino acids , and found that the difference in aggregation tendency between the two lifetime categories remains significant ( p<0 . 001 ) , showing that the observed difference in aggregation tendency is linked to the disparity in lifetime , and is independent of the difference in mean length of the proteins . This conclusion is confirmed by plotting the average aggregation tendency in function of the protein length for each lifetime category ( Figure 2A ) . In view of the idea introduced by Vendrusculo and co-workers that protein expression levels are tuned to the solubility limit of the protein [18] , we need to exclude that the difference in aggregation load in our data is simply due to a lower expression level for the fast turnover proteins . To address this , we employed publically available microarray data from the Gene Expression Omnibus ( GEO ) [33] , corresponding to 532 healthy individuals from 62 studies to compare expression levels of the proteins in our lifetime dataset . The density plot of the normalized expression levels for all proteins from the short lifetime and long lifetime groups reveals indeed a different composition of both groups in terms of expression levels ( Figure 2B ) . However , when we plot the length normalized aggregation score of the short and long-living proteins grouped per expression level ( Figure 2C ) , we see that the expression level is not the determining factor in the difference in aggregation propensity between fast and slow turnover proteins . These results suggest that proteins with a short biological lifetime undergo less evolutionary pressure to minimize the burden of aggregation . An alternative explanation for the lower sensitivity of fast turnover proteins to the evolutionary pressure against protein aggregation could be that these proteins possess native structures with inherently superior thermodynamic stability to those of proteins from the long lifetime group . Given the significant structural coverage of our dataset , i . e . there are high resolution crystallographic structures available for 127 proteins in our dataset of 611 ( Text S1 ) , we can address this question using a modeling approach . To do so we employed the FoldX force field [31] to calculate the thermodynamic stability of the aggregation nucleating regions predicted by TANGO in the corresponding crystal structures . We then plotted the average thermodynamic stability of the aggregating nucleating regions per bin of aggregation propensity according to TANGO ( Figure 3A ) . In this plot , we observe a clear correlation between the aggregation propensity of a polypeptide stretch and thermodynamic stability of the same region in the context of its native three-dimensional structure , so that sequences with the highest aggregation propensity form the most stable parts of the protein structure under native conditions , which is in accordance with previous observations [5] . Importantly , Figure 3A reveals no significant differences between proteins with a long or a short lifetime , showing that the difference in aggregation propensity between these groups is not due to fundamental differences in protein architecture or thermodynamic stability . It has been well established that evolutionary pressure against protein aggregation has resulted in the enrichment at the flanks of aggregation prone segments of gatekeeper residues , a term used to indicate amino acids that counteract aggregation [12] , [15] , [34] . This disruption of the aggregation prone stretches is achieved by a ) the repulsive effect of charge ( arginine , aspartate , glutamate ) , b ) the entropic penalty for burial ( arginine and lysine ) or c ) incompatibility with beta-structure conformation ( proline ) [34] . We analyzed the frequency of occurrence of gatekeeper residues in our short- and long-living protein datasets and found that the frequency of occurrence of gatekeeper residues shows a small but significant reduction in short-living proteins ( Figure 3B ) , which indicates that the introduction of gatekeepers as an evolutionary mechanism , to minimize aggregation is less pronounced in this set . This is consistent with the observation of longer aggregating stretches since they are less frequently interrupted by aggregation breaking residues , resulting also in a higher aggregation propensity of the stretches . A major component of the protein quality control system that evolved in all forms of cellular life to deal with the unavoidable burden of protein misfolding and aggregation is formed by the diverse families of molecular chaperones , which are a class of proteins that assist other proteins in ( re ) folding and disaggregation and eventually shuttle substrates to the degradation machinery [35] . In order to address the question if protein turnover rates influence the requirement of chaperone assistance of a protein , we searched the protein interaction database IntAct [21] ( release March 19 , 2010 ) for experimentally recorded interactions between proteins from our dataset and an extensive list of known human molecular chaperones ( listed in Text S1 ) . A total of 237 chaperone-binding proteins were identified , but experimentally determined protein stability was available for only 114 proteins . Based on Yen et al . , we divided this set of proteins into four categories according to their PSI turnover scores: short half-life ( PSI < 2 ) , medium half-life ( 2 ≤ PSI<3 ) , long half-life ( 3 ≤ PSI<4 ) and extra-long half-life ( PSI ≥ 4 ) [19] . For each category we calculated the enrichment of chaperone-binding proteins , where enrichment is defined as PSIN/PSIT – SUMN/SUMT . PSIx is the number of proteins in a given set x , belonging to a given PSI category and SUMx the total number of proteins in a given set x . X points to the total set ( T ) or the ( non- ) chaperone-binding proteins ( N ) . Comparison of the chaperone enrichment in short-living versus long-living proteins shows that in our limited dataset , proteins that interact with molecular chaperones are significantly enriched in the group of long-living proteins ( Figure 3C ) . Given we observed no fundamental differences in the thermodynamic stability or protein architecture between these groups ( see FoldX analysis above ) , this suggests that short-living proteins on average require less chaperone intervention than long-living proteins , consistent with the notion that their fast degradation rate is sufficient to protect against misfolding and aggregation . We investigated which of the proteins in our dataset are involved in a human disease associated with protein deposition and found 16 proteins with known PSI score ( Text S1 ) . Interestingly , all but one of these proteins belong to the category of short ( PSI < 2 ) or medium ( 2 ≤ PSI < 3 ) half-life . Although this analysis is not exhaustive , the data does suggest that the lack of evolutionary pressure to reduce aggregation in short-living proteins can backfire in circumstances were their turnover is altered .
Protein aggregation is triggered by short polypeptide stretches within a protein sequence that assemble into intermolecular beta-sheets when they become exposed to the solvent [8] , [36] , [37] ( Figure 4 ) . These aggregation nucleating regions can be predicted with good accuracy with biocomputational tools [20] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] and earlier work has shown that their occurrence is an inevitable consequence of the structural requirements of protein structure [52] . Globular protein architecture requires the tertiary packing of hydrophobic secondary structure elements to form a stable hydrophobic core . Unfortunately , these physicochemical parameters are also associated to a high probability for self-assembly of such secondary structure elements into β-aggregates [53] , [54] . Indeed , less than 10% of globular protein domains are devoid of aggregation propensity [12] . As a consequence of these overlapping but opposing forces that govern protein folding and aggregation , protein folding is generally a very inefficient process [55] , [56] . Moreover , aggregation is detrimental for the cell as misfolded proteins are inactive [57] and can acquire toxic gain-of-function [58] . Protein homeostasis is therefore tightly regulated by the protein quality control machinery of the cell . Given the high burden of protein aggregation on the proteome , and even if aggregation propensity cannot be avoided altogether , selective pressure to minimize the aggregation propensity of protein sequences is still to be expected . Indeed , it was found that aggregation-opposing residues are enriched at specific sites along the sequence of proteins [12] , [59] . These so-called aggregation-gatekeepers residues , consisting of prolines and charged amino acids , are systematically found at the flanks of aggregation-prone sequences stretches within proteins . Due to their β-breaking nature or charge they efficiently lower the aggregation propensity of hydrophobic stretches while at the same time preserving hydrophobic cores by their peripheral placement ( Figure 4 ) . Removal of gatekeepers increases aggregation and as a result gatekeeper mutations are three times more frequent in human disease mutants than in human polymorphisms [17] , [60] . Selective pressure against aggregation is not homogeneous . We previously showed that enrichment of gatekeeper residues is more pronounced at the flanks of strongly aggregating sequences [12] and it was also shown that aggregation propensity inversely correlates with gene expression [18] . In this study we employed the TANGO aggregation prediction tool [20] to compare the aggregation characteristics of proteins taken from the extremes of the protein lifetime distribution from the large scale data by Yen et al [19] . We observe a significantly higher aggregation propensity in proteins with a short lifetime than in proteins with a long lifetime . Analysis of gene expression data in 532 healthy individuals excluded the possibility that the observed difference in aggregation propensity arises from differences in gene expression levels between short-living and long-living proteins . Additionally the FoldX [31] analysis of the structures from both groups of proteins clearly show that this is not a result from a superior thermodynamic stability of short lifetime proteins , but rather from a genuinely higher aggregation propensity of their protein sequence . The higher aggregation propensity of short-living proteins does not originate from a higher number of aggregating regions , but rather from the higher average length and aggregation propensity of these regions , which can be traced back to a reduction in the amount of aggregation breaking gatekeeper residues . Hence , the reduced placement of gatekeepers in short-living proteins and the resulting higher average aggregation propensity , is evidence for the fact that proteins with a fast turnover rate experience less selective pressure to minimize aggregation than proteins with a longer biological lifetime . Moreover , a search of the IntAct database [21] revealed that there are significantly more recorded chaperone interactions for long-living proteins than short-living proteins . So , not only do short-living proteins experience milder selective pressure against aggregation , but at the same time they also interact less frequently with molecular chaperones or at least form less stable interactions of the type that can be recorded by current experimental techniques . Taken together , these data strongly suggest that the misfolding of short-living proteins is generally not affecting the fitness of the cell , as presumably the strong dependence of these proteins on proteasomal degradation suffices to avoid the accumulation of protein aggregates . On the other hand , it is known that the efficiency of the proteasomal system erodes as a result of physiological ageing [61] , [62] , [63] . Under these changing conditions , proteins with a higher aggregation propensity and lacking sufficient affinity for chaperones would form the Achilles' heel of the proteome and be among the most susceptible to aggregate . In this respect it is interesting to see that some of the fast turnover proteins from the dataset are indeed associated with human diseases with a protein deposition phenotype . | In order to carry out their biological function , proteins need to fold into well-defined three-dimensional structures . Protein aggregation is a process whereby proteins misfold into inactive and often toxic higher order structures , which is implied in about 30 human diseases such as Alzheimer's disease , Parkinson's disease and systemic amyloidosis . In earlier work it has been shown that although protein aggregation is an intrinsic property of polypeptide chains that cannot be entirely avoided , evolution has optimized protein sequences to minimize the risk of aggregation in a proteome . Here we show that this pressure is not uniform , but that proteins with a short lifetime have on average a higher aggregation propensity than long-living proteins . In addition , we show that high turnover proteins also make fewer interactions with chaperones . Taken together , these observations suggest that under normal physiological conditions the aggregation propensity of short-lived proteins does not represent a significant treat for the biochemistry of the cell . Presumably the strong dependence of these proteins on proteasomal degradation is sufficient to preclude the accumulation of aggregates . As proteasomal activity declines with age this would also explain why we observe a higher association of high turnover proteins with age-dependent aggregation-related diseases . | [
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] | 2011 | An Evolutionary Trade-Off between Protein Turnover Rate and Protein Aggregation Favors a Higher Aggregation Propensity in Fast Degrading Proteins |
Urogenital schistosomiasis caused by Schistosoma haematobium was endemic in Adasawase , Ghana in 2007 . Transmission was reported to be primarily through recreational water contact . We designed a water recreation area ( WRA ) to prevent transmission to school-aged children . The WRA features a concrete pool supplied by a borehole well and a gravity-driven rainwater collection system; it is 30 m2 and is split into shallow and deep sections to accommodate a variety of age groups . The WRA opened in 2009 and children were encouraged to use it for recreation as opposed to the local river . We screened children annually for S . haematobium eggs in their urine in 2008 , 2009 , and 2010 and established differences in infection rates before ( 2008–09 ) and after ( 2009–10 ) installation of the WRA . After each annual screening , children were treated with praziquantel and rescreened to confirm parasite clearance . Initial baseline testing in 2008 established that 105 of 247 ( 42 . 5% ) children were egg-positive . In 2009 , with drug treatment alone , the pre-WRA annual cumulative incidence of infection was 29 of 216 ( 13 . 4% ) . In 2010 , this incidence rate fell significantly ( p<0 . 001 , chi-squared ) to 9 of 245 ( 3 . 7% ) children after installation of the WRA . Logistic regression analysis was used to determine correlates of infection among the variables age , sex , distance between home and river , minutes observed at the river , low height-for-age , low weight-for-age , low Body Mass Index ( BMI ) -for-age , and previous infection status . The installation and use of a WRA is a feasible and highly effective means to reduce the incidence of schistosomiasis in school-aged children in a rural Ghanaian community . In conjunction with drug treatment and education , such an intervention can represent a significant step towards the control of schistosomiasis . The WRA should be tested in other water-rich endemic areas to determine whether infection prevalence can be substantially reduced .
Schistosomiasis is a neglected tropical disease caused by parasitic trematodes of the genus Schistosoma . In 2006 , Steinmann et al . estimated that globally , 207 million people live with schistosomiasis [1] , but later estimates by King suggest that the number is between 391 and 587 million people [2] . Morbidity may result from chronic or acute infection and may be independent of worm burden [3] . Hematuria is not exclusive to S . haematobium infection but it is estimated that in West Africa , over 15% of the population experiences hematuria at any given time [4] . Studies reviewed by Mbabazi et al . strongly point to S . haematobium infection as a risk factor for contracting HIV , particularly for women [5] . As described below , there are a variety of risk factors for contracting schistosomes , and correspondingly , a variety of options to control morbidity and transmission . Risk factors for S . haematobium infection tend to be location-specific; they may include age , sex , occupation , water contact practices , socioeconomic status , and distance to safe and unsafe water sources . Age and sex are two commonly studied infection risk factors . The prevalence of hematuria and S . haematobium eggs in urine tends to increase throughout childhood and peak between the ages of 10 and 20 as a function of increasing contact with infested water [6]–[10] . Decreases in worm burden after adolescence may be due to changes in immunity and/or behavior [10] . Males often have higher prevalences of infection and higher mean egg counts than do females [7] , [8] , but this is not always the case [11]–[13] . Sex-based differences in infection are thought to result from behavior differences . Previous infection with schistosomes is a complex risk factor and may predict likelihood of current infection . Previous infection may indicate behaviors that increase the subsequent risk of reinfection , but could also be associated with a relatively high likelihood of recent treatment with praziquantel , the drug of choice to kill schistosomes [7] . Finally , previous infection may correlate with acquired immunity [10] . Clothes washing , water collection , swimming/bathing , and fishing have all been identified as risk factors for schistosome infection [7] , [14] with varying results . For example , Hammad et al . , Handzel et al . , and Stothard et al . found correlations between water contact and infection [7] , [14] , [15]; other studies showed that proximity to contaminated surface water is a relevant factor [12] , [14] . In contrast , Satayathum et al . working in Kenya and Pereira et al . working in Brazil did not find correlations between water contact and infection [11] , [16] . In 1993 , the World Health Organization ( WHO ) stated that control of schistosomiasis should be accomplished within the context of the existing primary health care system , and that a long-term commitment ( 10 to 20 years ) to this goal is necessary [17] . Control programs can be broadly categorized into transmission control and/or morbidity control initiatives . Control options were recently reviewed by King [2] . Current control methods are the following: mass drug administration ( MDA ) ; water , sanitation and hygiene programs; education and behavior change programs; and occasionally , snail control . There is no single solution that is appropriate for every setting . Stothard et al . argue for the need to address S . haematobium transmission via improved access to clean water , education , and behavior change [15] . Satayathum et al . [11] determined that annual treatment of egg-positive school-aged children in Kenya could not reduce infection prevalence below 14% between 1984 and 1992 . After seven years of intensive health education in Senegal , knowledge of S . mansoni infection , transmission , symptoms , and treatment remained very low among both children and adults [18] . The authors concluded that community-driven control would be more effective than a vertical approach and behavior change may not occur when individuals lack access to infrastructure . In China , there is evidence that integrated control is highly effective in controlling S . japonicum [19] , [20] . Our goal was to assess S . haematobium infection rates in the absence and presence of a water recreation area ( WRA ) designed to reduce water contact and S . haematobium annual cumulative incidence . We focused on S . haematobium infections in Adasawase , Ghana where infection is typically transmitted via recreational contact with the Tini River . The location was selected based on a relatively high prevalence of S . haematobium infection as reported by the Chief of Adasawase in December 2007 . The main objective of our study was to test the hypothesis ( chi-squared analysis ) that the annual cumulative incidence of S . haematobium infection among a population of schoolchildren would decrease in the presence of a WRA . The study team was invited by the Chief of Adasawase to test this hypothesis by assessing annual cumulative incidence of S . haematobium infection before and after WRA installation .
The study protocol was approved by the Social , Behavioral , and Educational Research Institutional Review Board ( IRB ) of Tufts University and the IRB of the Noguchi Memorial Institute for Medical Research ( NMIMR ) in Accra , Ghana . The Chief of Adasawase and the head of each school provided written permission to conduct the study protocol . The Chief and the school heads communicated with parents and community members about the nature of the study . As part of this process , a number of public meetings were held by the Chief of Adasawase and the Council of Elders . Senior study team members were present at these meetings and answered questions about the study protocol as posed by parents , guardians , and potential participants . The IRBs of both Tufts University and NMIMR approved the study protocol , which employed verbal informed consent from parents/guardians , verbal informed consent from adult participants ( ≥18 years ) , and verbal assent from school-aged participants . A waiver of documentation of informed consent was approved by both IRBs . Only individuals <18 years whose parent/guardian provided verbal consent were enrolled in the study . This protocol was considered appropriate in a town where parents/guardians have historically expressed concern about signing formal paperwork for a non-invasive procedure ( e . g . , providing urine samples ) and where schistosomiasis prevalence is high but treatment options are few . Each study participant provided verbal assent ( or verbal consent for participants ≥18 years ) in the presence of several witnesses; this verbal assent was obtained prior to the collection of each urine sample and at each praziquantel treatment encounter administered by Ghana Health Services staff . The study described here was conducted over three years ( Figure 1 ) . In 2008 , infection prevalence for S . haematobium in Adasawase was quantified , all children were treated with praziquantel by Ghana Health Services as per WHO Guidelines [21] , and construction of the WRA began . The design , construction , and operation methods were all chosen for sustainability and ability to be replicated in other settings . The WRA is described in detail elsewhere [22] . Briefly , the WRA is a concrete pool fed by rainwater and hand-pumped groundwater; it is approximately 30 square meters and is divided into shallow and deep sections to meet the needs of children in a variety of age groups . In 2009 , ( re ) infection in the community was quantified in the absence of the WRA and children were again treated with praziquantel . Directly after the WRA was opened for public use in July 2009 , water contact at the local river was observed . In 2010 , reinfection was quantified after the WRA had been used for one year; egg-positive children were specifically targeted for treatment with praziquantel , but any child who wished to take praziquantel was treated [21] . For data analysis , study participants were separated into five cohorts based on age , school enrollment , screening status , treatment status , and whether previous infection status was known ( P . I . S . K . ) ( Table 1 ) . The 2008 cohort was chosen based on age , school enrollment , and participation in three screenings . The 2009 and 2010 cohorts were both chosen based on the following: age; school enrollment; participation in three or more screenings in the relevant year; treatment with praziquantel in the previous year; and negative S . haematobium status in the previous year at baseline . The 2009-P . I . S . K . and 2010-P . I . S . K . cohorts are made up of children with these same characteristics , in addition to the criteria that they were screened three or more times in the previous year and their previous infection status was known ( P . I . S . K . ) . Adasawase has a population of approximately 2 , 000 residents . S . haematobium infection was monitored in residents 8 to 22 years of age who were enrolled in one of three schools ( one junior high school , two primary schools ) in Adasawase as of June 2008 , June 2009 , and/or June 2010 . The number , percentage and age of children screened at least three times in any given year are shown in Table 2 . Not all of these children were previously treated with praziquantel and re-screened; thus , the number of study participants whose data were used in 2009 and 2010 is smaller than the number shown in Table 2 . S . haematobium infection prevalence was quantified in June of 2008 and annual cumulative incidence in June/July of 2009 and 2010 . Infection status was determined by urine filtration and subsequent microscopy for identification of S . haematobium eggs . Urine was collected in conical 50 mL tubes between 10:00 and 14:00 hours from children who were present at school . Schools were visited up to seven ( 2008 ) or nine ( 2009 , 2010 ) times to request samples from each study participant . Once a child provided three ( 2008 ) or four ( 2009 , 2010 ) samples on different days , ( s ) he was not asked for additional samples . We screened each child multiple times to improve the accuracy of prevalence and annual cumulative incidence estimates [23] . Data from children who provided at least three samples are presented here . A flow chart of the study design is available upon request . Urine samples were tested for S . haematobium eggs via filtration through Nucleopore membranes ( 25 mm diameter , 12 . 0 µm pore size; Sterlitech Corporation , Kent , Washington ) . Urine was shaken , drawn into a 10 mL syringe , and then discharged through a new Nucleopore membrane . Filtered urine was discarded . In this study , samples were tested either by extracting a 10 mL sub-sample of urine and filtering it , or by filtering the entire urine volume , which is a slightly more sensitive method . The first urine sample each year that a child submitted was tested by filtering a 10 mL sub-sample of urine . Membranes were removed from filter holders with forceps and placed egg-side-down on glass slides and examined under 100× magnification . For every sample , all S . haematobium eggs on each membrane were counted by the same experienced laboratory technician from the Noguchi Memorial Institute for Medical Research . To standardize results between 10 mL and full volume filtration methods , only urine samples with at least 1 egg/10 mL urine were considered positive in this study . Data were reduced to a binary score of positive or negative for S . haematobium eggs for analysis . A single community member from Adasawase was trained to directly observe behavior at the Tini River , the only recreational water contact site used by the community . The observer is a resident of the town and had established rapport with children , teachers , and parents in Adasawase . He answered questions about the study if asked and did not record information about anyone who did not wish to be observed ( no such requests were made ) . He observed the river from 6:00 to 18:00 hours for 14 days between July 5 and July 31 , 2009 . He also observed the river from 6:00 to 18:00 hours 7 days per week ( 84 hours/week ) from 1 August to 30 November 2009 . Use of the river after 18:00 hours was very rare ( data not collected ) . The following data were collected for each school-aged person who visited the river: name , age , school attended , school class , time of day , minutes in contact with river water , and activities performed ( swimming , washing/bathing , water collection , and washing of clothing or utensils ) . Each child's height ( centimeters ) and weight ( kilograms ) were measured . For height measurements , children stood barefoot against a stadiometer . In 2008 and 2009 , height was recorded to the nearest 0 . 10 cm; in 2010 , height was recorded to the nearest 0 . 50 cm . A digital scale ( 2008 , 2009 ) or mechanical scale ( 2010 ) was used to record the child's weight to the nearest 0 . 1 kg . Children wore school uniforms when they were measured . Body mass index was calculated as BMI = weight ( kg ) /height ( m2 ) . A child's status as having low height-for-age and/or low BMI-for-age was determined based on the “Simplified Field Tables” from the WHO [24] , [25] . A child's status as having low weight-for-age was determined based on the Growth Charts from the United States Centers for Disease Control and Prevention [26] . The birth days and months of children were unknown because the children were not able to self-report this information . Birth years were obtained from school records . The cutoff point ( inclusive ) for low height-for-age was -2 standard deviations ( SD ) for the child's age in years plus 6 months . The cutoff point ( inclusive ) for low weight-for-age was the fifth percentile for the child's age in years plus 6 months . The cutoff point for low BMI-for-age was the third percentile for the child's age in years plus 6 months , inclusive . In July 2010 , a spatial plan of Adasawase was created to determine the locations of homes of children enrolled in the study . One team member from Adasawase located houses , explained the study to participants , obtained verbal consent to participate in the study , and asked questions about school-aged individuals in each home . The other team member used a handheld global positioning system ( GPS ) unit ( Garmin GPS 60 Portable Navigator , Garmin , Ltd . ) to record the latitude and longitude coordinates of homes and well-known town landmarks; he also recorded data in a field notebook . Household members were verbally asked to provide the name , age , sex , grade level , and school of each school-aged child in the household . These data were recorded and manually matched to the child's parasitological and demographic data . When an exact match of information was not found , the relevant household was revisited and follow-up questions were asked to rectify any discrepancies . GIS layers were constructed by digitizing satellite imagery against the latitude and longitude coordinates of landmarks collected with the handheld GPS unit . Once the satellite image was georectified ( World Geodetic System 1984 , 30N ) , walking paths , roads , surface water , and points of interests were manually digitized from the image . House locations were imported into ArcGIS ( version 9 . 3 . 1 ) from the handheld GPS unit . The objective was to determine the locations of houses with respect to the Tini River . In the analysis of the spatial data , several simplifying assumptions were made . As a measure of distance , we employed the linear distance between homes and the main swimming point in Tini River , as opposed to the walking distance . The distance between a child's home and the Tini River was calculated in meters; the data were then reduced to an ordinal number corresponding to 1 = close ( <500 m ) , 2 = medium distance ( ≥500 m and ≤1 , 000 m ) , and 3 = far ( >1 , 000 m ) . These cutoff points were assigned by the research team based on the size of the community . In addition , we assumed that children reported to live at a particular home in 2010 resided in that same home in 2008 and 2009 . Logistic regression ( LR ) can be used for prediction [27] , [28] , hypothesis testing , or the determination of the statistical significance of covariates [7] , [27] . Here , LR was used to assess the significance of covariates . Parasitological ( hematuria and egg data ) , behavioral , anthropometric , and spatial data were double-entered into SPSS 14 . 0 ( SPSS Inc . , Chicago , IL ) . Three separate univariable analyses of potential risk factors for infection with S . haematobium were conducted to identify risk factors that were likely to be significant in multivariable models ( results not shown ) . The dichotomous outcome variable in each case is S . haematobium infection status in 2008 , 2009 , and 2010 . After univariable analysis , potential predictor variables with respect to multivariable LR were identified based on whether or not they were significant ( p<0 . 05 ) or marginally significant ( 0 . 05≤p<0 . 10 ) . Potential covariates were tested via LR analysis to determine whether or not their association with infection status was statistically significant . For 2008 infection status , variables tested included the following: age ( in years ) , sex , the distance between a child's home and the Tini River ( in meters ) , and the number of minutes a child was observed to be in contact with the river in 2009 . These same risk factors were considered for 2009 , in addition to the variable ‘previous infection status’ ( either positive or negative ) . Note that observation of behavior in 2009 took place after children were screened in 2008 and 2009; thus , behavior in 2009 is used as a proxy for behavior that occurred prior to the 2008 and 2009 screenings . A final model for each LR analysis was chosen once all variables in the model were either statistically significant or biologically plausible and marginally significant .
Spatial data can be collected and used to characterize patterns of S . haematobium infection , water contact behavior , and environmental factors within communities . Because risk factors for infection vary among and within communities , additional studies are needed to document the spatial heterogeneity of schistosome infection and to better characterize significant risk factors other than climate and terrain [29] , [30] . For example , Clennon et al . mapped home and water contact locations in Kenya via very high resolution ( 1 to 4 m2 ) remotely-sensed imagery and concluded that children under 6 who live near water contact sites may have more exposure to contaminated water and thus may develop immunity to reinfection earlier than children who live farther away [31] . We created a spatial plan of Adasawase using GIS data collected in 2010 ( see reference 22 for figure ) . The home locations for 71 out of 254 children in the 2008 cohort were available in 2010 . The home locations for 86 out of 220 children were available for the 2009 cohort , and for 117 out of 246 children in the 2010 cohort . The home locations for the rest of the children in each cohort remain unknown; these children were not reported to be living at any of the households visited by the study team , which may indicate that they live outside the town , they have moved away from the town , the individual reporting house resident names did not consider them a ‘resident’ , or the name given in the household is not the name the child uses in school . Potential risk factors for developing urogenital schistosomiasis were evaluated via LR analysis by Hammad et al . , Nsowah-Nuamah et al . , and Clennon et al . [7] , [32] , [33] . Here , a univariable analysis of risk factors was conducted ( not shown ) and then LR analysis was performed . Prior to the intervention ( 2009 model only ) , variables that were associated ( p<0 . 05 ) with infection include: the distance between a child's home and the Tini River , the number of minutes observed using the river , and previous infection status . Post-intervention in 2010 , none of the variables remained significant ( p<0 . 05 ) as predictors of infection status . Children who were egg-positive for S . haematobium in any given year were more likely than their sex-matched peers to ( a ) use the river and ( b ) use the river for longer periods ( Table 4 ) . The number of “contacts per person” refers to the number of different occasions on which a person used the river . Directly observed behavior in 2009 was considered a proxy for behavior in 2008 , 2009 , and 2010 . A logistic regression model was developed for the dichotomous outcome variable S . haematobium infection status in 2008 . Variables considered in the model include: age , sex , distance between home and the Tini River , minutes observed at the river , low height-for-age , low weight-for-age , and low BMI-for-age . Sex and minutes observed at the river were significant in the model ( Table 5 ) ; age is of marginal significance but is retained in the model for comparison with other studies . The distance between a child's home and the Tini River may have been a relevant factor for infection , but the sample size for this variable was relatively small ( n = 71 ) and it was not significant in the final model . The model correctly identified 81 . 7% of negative children and 33 . 3% of positive children , indicating that while relevant , not all variability in infection status is captured by these explanatory factors . The final model was chosen based on the significance of each explanatory variable , on biological plausibility , and on a non-significant Hosmer and Lemeshow goodness-of-fit test ( p = 0 . 385 ) . The model shows that for every one additional year of age , risk of infection increases by a factor of 1 . 112 ( 95% CI: 1 . 002–1 . 235 ) ; males were nearly 1 . 9 times more likely than females to be infected ( 95% CI: 1 . 108–3 . 198 ) ; and for every additional hour spent in contact with the Tini River , risk of infection increases by 1 . 349 ( 95% CI: 1 . 062–1 . 613 ) . Two different logistic regression models were developed for the dichotomous outcome variable “S . haematobium infection status in 2009” . In the first model ( Table 6 ) , variables considered include: age , sex , distance between home and the Tini River , minutes observed at the river , low height-for-age , low weight-for-age , low BMI-for-age , and 2008 infection status . To assess the significance of the variable ‘2008 infection status’ , only data from children in the group 2009-P . I . S . K . ( Table 1 ) were considered ( n = 133 ) . The number of minutes observed at the river ( OR = 1 . 006 , 95% CI: 1 . 002–1 . 010 ) and 2008 infection status ( OR = 9 . 422 , 95% CI: 1 . 932–45 . 938 ) were the only variables significantly associated with S . haematobium infection in 2009 . The model correctly identified 99 . 1% of negative children and 38 . 9% of positive children ( Hosmer and Lemeshow = 0 . 691 ) . The final model was chosen based on the significance of each explanatory variable and on biological plausibility . A second logistic regression model was developed for the dichotomous outcome variable “S . haematobium infection status in 2009” that did not consider 2008 infection status as an explanatory variable ( Table 7 ) . In the second model , variables considered include: age , sex , distance between home and the Tini River , minutes observed at the river , low height-for-age , low weight-for-age , and low BMI-for-age . The number of minutes observed at the river was the only factor significantly associated with infection in 2009 ( OR = 1 . 007 , 95% CI: 1 . 004–1 . 011 ) . Sex was not significant ( p = 0 . 564 ) , but was retained in the model for comparison . The model correctly identified 98 . 9% of negative children and 31 . 0% of positive children ( Hosmer and Lemeshow = 0 . 108 ) . The final model was chosen based on the significance of each explanatory variable and on biological plausibility . Nine children were infected with S . haematobium following construction of the WRA . The annual cumulative incidence rate ( 3 . 7% ) in the presence ( 2009 to 2010 ) of the WRA was significantly lower than the annual cumulative incidence rate ( 13 . 4% ) in the absence ( 2008 to 2009 ) of the WRA ( χ2 = 14 . 44 , df = 1 , p<0 . 001 ) . Because of the small number of infected children in 2010 , a well-powered logistic regression analysis to determine risk factors was not feasible . Instead , the potential risk factors associated with infection are presented for each child ( Table 8 ) . Of the infected children , 7 of 9 were male; all were between the ages of 11 and 15 years; nearly half ( 4/9 ) had a history of previous S . haematobium infection; and 8 of 9 children had at least one potential indicator of malnutrition ( low height-for-age , low weight-for-age , or low BMI-for-age ) . These risk factors suggest demographic and behavioral characteristics that may be associated with infection . However , it is not possible to conclusively state why each child was infected . In this study , the S . haematobium infection rate decreased significantly in the presence of the WRA . It is possible that infection decreased for a reason not related to the WRA , although to our knowledge , no other relevant community-wide changes took place during the course of the study . It is highly unlikely that individuals received treatment for schistosome infection outside of the praziquantel distribution through our study; praziquantel was difficult to locate in the Atiwa District health clinics near Adasawase at the time of our study , and was occasionally scarce in Accra , the capital of Ghana . The WRA should be tested further in other communities as behavior change may be location-specific . The study shows a biologically-relevant and statistically-significant decrease in S . haematobium annual cumulative incidence in a community after installation of a WRA; this decrease was not achieved via MDA alone in the year prior to installing the WRA . | Urogenital schistosomiasis is a disease caused by the parasite Schistosoma haematobium; it is often characterized by bloody urine and tends to disproportionately affect school-aged children in rural tropical regions . The parasite is transmitted via skin contact with surface water that is contaminated by human waste . The disease was endemic in Adasawase , a rural Ghanaian community , in 2007 . Transmission occurred mainly through recreational water contact . We collaborated with community members to design a water recreation area ( WRA ) featuring a concrete pool supplied by a borehole well and a rainwater collection system . We opened the pool in 2009 and local officials encouraged children to use the WRA for recreation . We screened local children annually ( 2008 , 2009 , 2010 ) for S . haematobium infection . After each screening , children were treated with praziquantel and rescreened . Baseline testing in 2008 established that at least 105 of 247 ( 42 . 5% ) children were infected . In 2009 , 29 of 216 ( 13 . 4% ) children were infected , reflecting annual cumulative incidence . In 2010 , a significantly smaller percentage of children ( 9 of 245 , 3 . 7% ) were infected . We conclude that the WRA effectively reduced infection in Adasawase , and that it should be tested in other water-rich endemic areas . | [
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"control",... | 2012 | Effective Control of Schistosoma haematobium Infection in a Ghanaian Community following Installation of a Water Recreation Area |
Infection is a complex and dynamic process involving a population of invading microbes , the host and its responses , aimed at controlling the situation . Depending on the purpose and level of organization , infection at the organism level can be described by a process as simple as a coin toss , or as complex as a multi-factorial dynamic model; the former , for instance , may be adequate as a component of a population model , while the latter is necessary for a thorough description of the process beginning with a challenge with an infectious inoculum up to establishment or elimination of the pathogen . Experimental readouts in the laboratory are often static , snapshots of the process , assayed under some convenient experimental condition , and therefore cannot comprehensively describe the system . Different from the discrete treatment of infection in population models , or the descriptive summarized accounts of typical lab experiments , in this manuscript , infection is treated as a dynamic process dependent on the initial conditions of the infectious challenge , viral growth , and the host response along time . Here , experimental data is generated for multiple doses of type 1 dengue virus , and pathogen levels are recorded at different points in time for two populations of mosquitoes: either carrying endosymbiont bacteria Wolbachia or not . A dynamic microbe/host-response mathematical model is used to describe pathogen growth in the face of a host response like the immune system , and to infer model parameters for the two populations of insects , revealing a slight—but potentially important—protection conferred by the symbiont .
Infection is a complex and dynamic process that starts with a host coming in contact with pathogens , and ends with the latter either being eliminated by the former or becoming established inside it . A comprehensive analysis of host invasion by a microorganism requires a thorough description of the host biology , such as physical compartments and barriers , important tissues and organs , and immune responses , as well as the microbial processes , and the interaction of these many components [1]; missing parts in this description limit the thorough understanding of infection . Due to constraints in time , resources , as well as analysis tools , any study must simplify its scope . Experimental assays typically rely on static measurements such as pathogen level at some specific time point , as well as restrictive laboratory conditions like a typical challenge dose [2] . While these traditional approaches can be useful to determine the effect of a large perturbation to the host-microbe system—such as gene knockouts or different pathogen strains—they are limited to a snapshot of the process under arbitrary conditions . On the theoretical side , mathematical models of within-host pathogen dynamics are often disconnected from data , or use convenience data samples [3] . Because of that , the findings from modeling studies are only rarely comparable to those of more traditional experimental approaches . Despite having lagged the establishment of population transmission—or between-host—mathematical models by several decades [4] , quantitative descriptions of pathogen proliferation along time within-host have considerable history [5 , 6] . Typically these take the form of a few coupled differential equations , and include simple constant-rate pathogen growth , with immune response-dependent pathogen death , and immunity described as either induced by the pathogen [7] , constitutive [8] , or both . Most models assume deterministic increase in pathogen load after entering the host , elimination being possible only by chance; less common are models that deterministically predict bistable outcomes in the form of either establishment or elimination [9] . Describing infection dynamically beyond a purely theoretical construct therefore requires specific time course data on the components described by the mathematical model; ideally , the data and its interpretations should also relate to the bulk of experimental research in host-pathogen infection experiments , and to the existing theoretical models available . This work does not purport to , on its own , change and unify disparate fields , but instead it is a complete attempt at design , execution , and model-based analysis of a large experiment consisting of multiple initial dose challenges and time points that describe a range of progressions along time and outcomes of infection , from elimination to establishment . The data set produced illustrates how looking at any one dose and time point can only give a limited glimpse into the process of infection . Given the patterns visible in the data , a bistable model is found to be suitable to the broad features observed: it is able to describe increase in microbe levels up to establishment of systemic infection , as well as decrease and elimination of the pathogen . Aedes aegypti mosquito hosts were infected with serotype 1 of dengue virus ( DENV-1 ) previously circulating in the city of Rio de Janeiro , Brazil . The choice of the less tractable DENV-1 is a deliberate one , considering the small literature availability , and also the fact that experiments are mainly conducted with DENV-2 , due to its ease of cultivation in C6/36 mosquito-cell culture . This bias further restricts knowledge about dengue more generally , and perpetuates many knowledge gaps in the difference between dengue serotypes . Besides using a system relevant to human health for a novel analysis , we infect a population of mosquitoes carrying a strain of the bacterium Wolbachia , a maternally inherited symbiont introduced as means of controlling dengue as well as Zika and chikungunya viruses [10 , 11] . We fit the model to data of mosquito populations either carrying the symbiont Wolbachia or not , and compare the dynamic profiles and parameters estimated . Because the data set and model take into account both time and dose dimensions , the results are more general than those for typical laboratory conditions . In the light of the mathematical model and its inferred parameters , Wolbachia is shown to protect the mosquitoes from dengue virus infection , given the reduced time course profile of infection associated to increased recruiting and longer-lived host response . We discuss how these results compare to past experiments and what they bring to future ones , the limitations we acknowledge in this particular work and how they can be overcome in future similar efforts , as well as the implications for the study of infection more generally and in other systems , particularly different dengue virus serotypes .
Around 1000 mosquito larvae were reared in plastic trays with approximately 1 . 5 liters of dechlorinated water and 0 . 9 g of Tetramin Tropical Flakes® added every two days . Adult Ae . aegypti were maintained at 25 ± 3°C and relative humidity of 80 ± 5% for about 2-3 days for mating , with a sugar solution of 10% ad libitum . Two groups were used , wMelBR ( formed by backcrossing Brazilian wild males with Wolbachia infected Australian females , as showed on [12] and wMelTET ( obtained from treating wMelBR mosquitoes with the antibiotic Tetracycline by three consecutive generations , healing Wolbachia in these insects ) . Thus , the two groups have strong genetic similarities regarding their background . Dengue virus infection was performed with DENV-1 samples recently isolated from a patient in Rio de Janeiro and stored at −80°C . DENV-1 was initially amplified to a 108 TCID50/mL and later passed through a ten-fold serial dilution , producing five different titers , from 108 to 104 TCID50/mL . The experimental design , therefore included 6 different challenge doses and 3 time points for both Wolbachia-carrying and Wolbachia-free groups . The numbers of mosquitoes assayed by qPCR for each of the 36 experimental conditions are shown in a supplementary Table A in S1 Text . For the purpose of computing statistical correlations between dengue and Wolbachia titers , only conditions with no fewer than four detectable pairs of data points were used to avoid obtaining artificially high correlations due to lack of data points . For inference purposes , conditions with six or fewer mosquitoes and non-zero titers were not used . To produce the final data set used for all analyses hereafter the levels of DENV-1 were normalized by dividing by the mosquito Ribosomal Protein S17 ( RPS ) gene . The viral titers being relative to the mosquito gene , the lowest value in the data set was set to unity , and all others were adjusted accordingly and rounded to the closest integer values; this scaling neither affects the relative titers nor the subsequent analyses . The data set for viral levels in the wMelTET group is shown in the foreground of Fig 1 , with the viral titers for the wMelBR shown in light-shaded color for comparison . For ease of visualization , the data set for viral levels in the wMelBR group is shown in supplementary Fig A in S1 Text with this group in the foreground instead . For the wMelBR group , the symbiont levels were computed relative to the same house keeping gene used as a standard for the viral titer data; otherwise the procedure was the same as for the wMelTET group . The wMelTET group was used as a negative control , and every sample had undetectable qPCR levels of the symbiont , as expected . The Wolbachia levels are shown in Fig 2 . The mathematical model used in this work is a slightly modified version from the model proposed by Pujol et al . [9] . Most within-host models found in the literature describe microbe reproduction as a constant rate , resulting in exponential growth in the absence of any other process; most also treat microbe killing as an immunity-dependent process , resulting in non-linear terms with both immune response and microbe density interacting [4–8] . These features are also present in this model . Other than that , descriptions often diverge in what is the origin of the immune response . Models may assume it is constitutive , induced , or both [4] . Because these descriptions generally do not refer to any specific immune pathway , and it is more of a mathematical construct , we henceforth describe this component of the model with the more generic term host response . These features are included in our model , which is described by two differential equations , one for the pathogens , one for host response ( with terms for both constitutive and induced processes ) , as shown by the system of Eq 1 . In this model r is the growth rate of pathogens; growth is self-limiting due to the negative quadratic term , −kP2 . Additional decreases in pathogen numbers are governed by a non-linear term , −δPPR , indicating an increased rate of destruction of pathogen units when there is a host response R—the intensity of this response is governed by its own differential equation . The host response can be described by pathogen-independent components , α , a constant rate of recruitment of the response minus a linear death rate , −γR , as well as pathogen-dependent components , λP , describing the rate of recruitment of the response in the presence of pathogens minus a non-linear term , −δRPR , representing the pathogen-induced destruction , use , or wear of the host response . The difference between this formulation and that of Pujol et al . [9] is a logistic-like growth profile induced by the quadratic term; in the absence of a host response , pathogens follow a logistic growth , growing initially at nearly exponential rate and saturating as the population reaches carrying capacity . The same is true for a small response incapable of eliminating the pathogen; in that case growth is a little slower but at high levels it is limited mainly by the quadratic decrease resulting in a stable level of pathogens and response , as opposed to unlimited growth [9] . The importance of this feature to explain specific features of our data set , as well as the implication for the possible mathematical solutions of the system are discussed in the results section . Correlations were computed between virus and Wolbachia , with the data stratified by dose and time as well as with the entire aggregated data set , to assess quantitative relationships between naturally changing symbiont levels and the observed viral levels . Bayesian inference of the parameters for model ( 1 ) was performed using a Markov Chain Monte Carlo implementation in the Python programming language [14] . A poisson distribution of errors was used to compute the likelihood of the parameters given the data . Convergence was assessed by stability of the chains , and replicate chains were run to make sure the same approximate values were obtained regardless of starting point of the Markov chain [15] . Burn-in was performed by discarding the initial samples; the first half was assumed to be enough given the total lengths of the chains and trace of the likelihood values . Besides the parameters described above , the initial condition parameter P 0 h i g h and the dilution “dosefold” parameter were also estimated—because each dose is diluted equally from the previous concentration , the two parameters define all initial conditions for all challenge doses by successively dividing the highest dose by the dilution value . Most parameters were kept fixed between the wMelTET and wMelBR groups , as described in the results section . The λ and δR , as well as the initial condition P0 and dilution parameters are allowed to vary between the two groups . These choices are detailed in the discussion section . Uniform priors were used on a wide range of positive values; a gamma-distributed prior is used for the dilution parameter , since it is known that a tenfold dilution was done for each lower dose . To reduce uncertainty , a gamma prior is also used for the growth and initial condition parameters; to that end a simple linear least-squares regression is computed for the highest dose logarithmic values , where the slope of straight line would give the exponential growth rate , and the intercept the initial condition—those values are used as the mean of the gamma distribution . In addition to the main analysis a generalized linear model is fitted to the data to assess significance of the variables in the experimental design ( see supplementary material ) .
Broad patterns can be observed directly from Fig 1: the two lower doses have essentially zero infection , with only a couple of data points at very low levels . The intermediate dose has already a higher proportion of infected individuals , but still at low levels . The higher doses show a clear trend of increasing pathogen levels along time , with the exception of the second highest dose at the last time point ( 107 TCID50 ) ; however , the unexpected pattern may be an effect of the low number of data points for that condition . Given our criteria for number of data points per condition described in the methods , this time/dose condition is not used for the model-based inference . In any case , in the next section we discuss more sophisticated methods that could be used in the future to deal with issues such as low number of data points for any one condition without any ad hoc treatment . One very important feature of the data is a number of data points far from the mean , which could represent samples from a long-tailed or bimodal distribution . This is especially visible in later time points of the second highest dose , but also on the highest challenge dose . The feature could be explained by bistability in the viral dynamics , stochasticity , or the combination of both , and it is further discussed later under the model predictions with and without stochasticity . Given that DENV and wMelBR titers were measured for each individual mosquito , the correlation between virus and Wolbachia can be computed , and are shown in Fig 3 ( separate panels are shown per dose and time point in Fig B in S1 Text ) . There is a significant ( p = 0 . 05 ) but not high ( R2 = 0 . 25 ) positive correlation for the whole data set . We also compute the correlations for each time point and dose combination individually , as long as there were more than 3 non-zero data points in the condition tested . Some correlations were somewhat higher , but for the most part not significant at the 5% level , and were all positive . These are shown in Table 1 . Given these results , Wolbachia is therefore treated not as a quantitative variable , but as a present/absent factor . There are three mathematical solutions to the system of differential Eq ( 1 ) ; the simplest being the pathogen-free solution ( P free * = 0 ) , where constitutive recruitment and constant-rate elimination of immunity results in a stable equilibrium R free * = α / γ . The other two solutions represent a stable establishment of pathogens , i . e . a systemic , persistent infection , and an unstable equilibrium that is of interest mainly to determine under which conditions the system will be tipped one way or the other . All three solutions are shown below: ( ( Pfree*=0;Rfree*=αγ ) , ( Psystemic*=rδR−λδP−γk+ ( λδP+γk−rδR ) 2−4kδR ( αδP−γr ) 2kδR; Rsystemic*=rδR+λδP+γk− ( λδP+γk−rδR ) 2−4kδR ( αδP−γr ) 2δPδR ) , ( P^=rδR−λδP−γk− ( λδP+γk−rδR ) 2−4kδR ( αδP−γr ) 2kδR; R^=rδR+λδP+γk+ ( λδP+γk−rδR ) 2−4kδR ( αδP−γr ) 2δPδR ) ) The bistability in the steady states of the model can produce the bimodal distribution of the later viral titers , observed in the data , as long as there are differences in the initial conditions of infection . Although the injected doses are controlled , it cannot be excluded that experimental or biological variation in the host or virus initial conditions explains observed bimodal outcomes in an otherwise adequate deterministic model . Alternatively , a stochastic implementation of the model acknowledges noise in the processes along the entire trajectory and gives a non-zero probability of observing a bimodal distribution even for identical initial conditions . Numerical simulation of the system illustrates not only the equilibria , but the dynamic trajectory of the pathogens towards either establishment or elimination , and is shown in Fig 4 . At selected time points the levels of pathogens can be sampled from the simulation , generating a pseudo-data set similar in structure to a real data set . With a somewhat arbitrary set of parameters it can be seen that some of the broad features of our real data could be reproduced by a stochastic simulation of the model ( Fig 4A ) , and that some of it could be captured by a deterministic approximation ( Fig 4B ) . In the absence of the quadratic term the system has only one stable equilibrium , elimination of the pathogen ( identical to the one shown above ) , and one unstable equilibrium [9]; if pathogens manage to grow beyond control of the response their growth is unbounded . For real data sets , pathogen levels are expected not to increase indefinitely , as is observed for our data set , therefore our attempt to explain the data set with this model does include the logistic-like term . This forward approach gives us the output of the model given a set of parameters , but does not give anything beyond qualitative interpretations of any data set; in the next section we use the reverse approach with the Aedes-DENV-Wolbachia data set to infer the parameter values given our experimental data and compare the two populations of mosquitoes . The dynamic host-parasite model ( 1 ) , described in the previous section , was fitted to the experimental data , as described previously . The results of the inference are shown in Fig 5; data points in the figure are a superposition of all panels in Fig 1 , with the wMelTET group data following the same color code , and the wMelBR in green . The lines are the model prediction with the associated confidence intervals in a lighter shade . The dynamic profile inferred ( Fig 5A ) shows not only lower initial pathogen levels , but generally lower titers along time for the population with Wolbachia . The highest two doses result in establishment of infection , although the increase is slower for the hosts carrying the symbiont . For the middle dose viruses are expected to persist at low levels and decay towards zero after around 10 days in the wMelTET hosts , while in the Wolbachia-carrying hosts they drop below detection before day 1 . The two lowest doses for both the wMelTET and wMelBR groups start from very low titers and rapidly decrease to even lower levels , making viral titers essentially zero for the entire time course . Besides the dynamic profiles inferred , the values of individual parameters are shown in the lower panel ( Fig 5B ) , breaking down which processes are responsible for the inferred dynamics . The initial condition parameter P 0 ( h i g h ) is greater for the wMelTET group , and the dilution parameter is greater for the wMelBR group , meaning estimated initial inoculum for the highest dose is lower for the latter group , and they are progressively smaller for lower doses—differences in the presumably equal initial inoculum are discussed in the next section . The additional parameters that differ between the two groups are λ , the pathogen-induced recruitment of the response , and δR , the rate of destruction of the response once in contact with the pathogen . These parameters are greater and smaller , respectively , for the Wolbachia-carrying group; therefore , as interpreted by the model-based inference , the wMelBR group recruits a response faster than the wMelTET group , and this response wears out more slowly , i . e . persists longer once recruited .
Laboratory experiments are usually designed to maximize the success of infection by challenging susceptible mosquitoes with a single high viral dose , masking the range of possible outcomes of the host-pathogen interaction ( in our system Ae . aegypti and DENV plus Wolbachia ) . Rather than using static readouts that only provide a snapshot of this process , the results shown here are a product of a multi-factorial experimental design . The interpretation of these results requires a quantitative framework that describes the initial infectious dose of the pathogen and considers the viral growth and the host response throughout time [9] . Performing a multiple-dose infectious challenge assay with 3 time points and 5 different DENV-1 dilutions on Ae . aegypti females—with the symbiont bacterium Wolbachia assumed to be a discrete variable that is either present or absent—revealed a mild although potentially important protection against the virus , conferred by the symbiont along time . To ascertain the importance of these findings , future experiments should emulate more natural viral challenge routes , and assess infection in specific host organs , such as the salivary glands given their role in transmission . The intensity of pathogen transmission is largely influenced by the ability of mosquitoes to become infected by and transmit arboviruses . Ae . aegypti traits that are components of this vectorial capacity , such as susceptibility to DENV and the duration of the extrinsic incubation period ( EIP ) , are likely to produce significant effects on the epidemiological trend during outbreaks [16 , 17] . Therefore , a thorough quantitative description of the process is necessary to assess the impact of interventions on the vector population , and nevertheless excessive experimental simplification may preclude a useful understanding of the system beyond qualitative assessments or outright speculation . The process of infection is extremely structured . Soon after the ingestion of an infected blood meal from a vertebrate host , viral particles are detected on Ae . aegypti midgut , where digestion takes place [18–20] . It is thought that some arboviruses can induce the activity of particular proteases to help disassemble the basal lamina surrounding the midgut and corollary disseminate to mosquito haemocoel [21] . The midgut infection barrier is an important bottleneck of viral replication inside the host [1]; by performing mosquito infection using intrathoracic injections , we allowed DENV to bypass it entirely , so that the control of viral load is due to other components of the host response , immunological or otherwise . Therefore , a component of vectorial competence is artificially removed in this experiment; extrapolations based on our data must take that into consideration because it should not fully represent a natural route of infection [22] . It is worth noting that a more natural oral infection should also result in an observable bistable pattern if an appropriate range of doses is used; however , the variation associated to feeding infected blood to mosquitoes , even in controlled laboratory conditions , as well as the additional biological steps to a systemic infection would lead to increased variation that may obscure the observed bistability . Bypassing the midgut allows greater control of the pathogen dose received by each mosquito , and clearly shows the possibility of two opposite outcomes of either elimination of the pathogen , clearly seen for lower dilutions ( 104 and 105 ) , or establishment , observed when the females were inoculated with the higher doses ( 107 and 108 TCID50 ) . Intuitively , it is expected that DENV dynamics inside an Ae . aegypti host is dependent on the initial amount of viral particles the mosquito has ingested during the infective blood meal . From the raw data at the assayed data points it can be seen that wMelBR mosquitoes infected with high DENV doses generally have lower pathogen loads at specific time points , supporting the observation that Wolbachia has a protective effect . The viral growth can also be empirically observed to be slower in the wMelBR group , which is supported by the model-inferred lower profile of pathogen levels in that group . On the other hand , the effect of Wolbachia on infection was less pronounced on the two lowest DENV doses since the virus titers rapidly decreased and remained close to zero for the entire time course for both groups . Stochasticity is likely to play an important role on the observed infection outcomes . Duneau et al . [23] observe variation in bacterial levels along the course of infection , and interpret a bimodality in infection outcome under a two-piece model where each of the two outcome groups is assigned different trajectories after some time when the host can still control infection . While the model described by the authors is also deterministic , variation in the initial phase of infection is found to predict which hosts are able to reach a tipping point that would determine whether an increasing or decreasing function describes infection levels from them on . While bacteria and viruses will have many specificities to their invasion of a host , some broad features of the data observed by Duneau et al . [23] may be shared between different types of pathogens . We too interpret that variation in the initial phase , or conditions of infection may determine the final outcome , although under our model bistability is an emerging property of the host-pathogen interactions . Stochastic versions of this and other models will be able to further accommodate unexplained variation , as shown by our forward simulations , and incorporating it into inference could significantly improve model-based analyses [24] . That the observed effect of Wolbachia depends on the dose does not mean that it cannot be explained by a single , unified model . Dose-response models , for instance , can explain the expected proportion for a binary ( infected/uninfected ) outcome as the effect of a single susceptibility distribution [25]; similarly , the model proposed here explains viral levels along time and ultimately opposite outcomes as a function of dose and time for a single set of parameters . As inferred by the model , the observed differences are a consequence of differences in the initial pathogen loads and host responses . The predictions about host response parameters are themselves hypotheses , both qualitative and quantitative , which can be compared to observations of the host immune response like the expression of specific pathways [26] . This is an interesting future perspective that has nevertheless not been explored in this work . Reproducing all the complexity of the real world in a controlled laboratory study is unrealistic , but we do make the distinction between not considering discrete categorical variables , as opposed to fixing variables in a continuous scale [27] . An example of the former is a single viral serotype or genotype which can cause an epidemic alone , and a factor like Wolbachia is normally absent in the mosquito population . On the other hand mosquitoes will invariably ingest blood from infected hosts with a potentially great range of DENV titers [28] , artificially fixing it is an example of the latter . This study aims at incorporating variation in quantitative dimensions that are inescapable in any infection , although it does not exhaust all continuous or otherwise extensive number of variables that are likely to be important in the process . Notably , mosquito-virus interactions are related to the heterogeneity of DENV ( serotypes , and at a finer scale , genotypes ) and mosquito populations with their genetic background and environmental factors [29 , 30]; these are not explored beyond the inbred population used for this study . The model-based inferred DENV-1 infection profiles reveal otherwise obscure differences between Ae . aegypti mosquitoes and their Wolbachia-carrying counterparts , and the underlying parameters that determine the difference . These results corroborate the reported effect of Wolbachia [10] in a broad sense , but are more comprehensive , and include observed and/or predicted differences for specific conditions of previous studies . Therefore , previous results can only be interpreted as particular cases of this study , not the other way around . Analogously , the effect of other factors , when assessed , should be interpreted under a complete picture of their infection profiles , as opposed to looking at a single time point or challenge dose . Ignoring these dimensions could be especially counterproductive when comparing susceptibility to or infectivity of discrete serotypes , for instance . The work presented here shows the combination of a dynamic model with a multiple dose challenge design that allows interpretation of a comprehensive data set beyond discrete factors or pairwise comparisons , and allows more concrete hypotheses about the biology to be tested . Improvement of the mathematical and statistical framework , as well as inclusion of more detailed biological processes in the model description could further refine the interpretation of infection . | Infection is usually assayed as a static observation of a pathogen within a host; it is , nevertheless , a dynamic process that cannot be described from a single time point and arbitrary conditions . Results based on the usual methods are a snapshot of a convenient laboratory condition; a more comprehensive data set is required to describe the entire process of infection from inoculation of the host with a microorganism to establishment of a systemic infection , or elimination of the threat by the host . We design an experiment that takes into account increasing pathogen challenges to a mosquito host and viral levels along time; we use a dynamic mathematical model to analyze the resulting data set . The entire framework is used to compare susceptibility to dengue virus of Aedes aegypti mosquitoes either carrying the Wolbachia symbiont or not . Instead of a simple pairwise comparison , we are able to compare infection profiles and parameters associated to host immune processes in this insect-symbiont-virus system . | [
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... | 2018 | Model-based inference from multiple dose, time course data reveals Wolbachia effects on infection profiles of type 1 dengue virus in Aedes aegypti |
Despite the availability of effective interventions and public recognition of the severity of the problem , rabies continues to suffer neglect by programme planners in India and other low and middle income countries . We investigate whether this state of ‘policy impasse’ is due to , at least in part , the research community not catering to the information needs of the policy makers . Our objective was to review the research output on rabies from India and examine its alignment with national policy priorities . A systematic literature review of all rabies research articles published from India between 2001 and 2011 was conducted . The distribution of conducted research was compared to the findings of an earlier research prioritization exercise . It was found that a total of 93 research articles were published from India since 2001 , out of which 61% consisted of laboratory based studies focussing on rabies virus . Animals were the least studied group , comprising only 8% of the research output . One third of the articles were published in three journals focussing on vaccines and infectious disease epidemiology and the top 4 institutions ( 2 each from the animal and human health sectors ) collectively produced 49% of the national research output . Biomedical research related to development of new interventions dominated the total output as opposed to the identified priority domains of socio-politic-economic research , basic epidemiological research and research to improve existing interventions . The paper highlights the gaps between rabies research and policy needs , and makes the case for developing a strategic research agenda that focusses on rabies control as an expected outcome .
South Asian countries contribute to more than half of the global burden of rabies [1] , [2] . However , in spite of the long-standing nature of the problem , and despite the presence of effective intervention strategies [3] for rabies control , rabies continues to pose a major public health challenge to program planners in the region and elsewhere . Most South Asian countries still retain ad hoc approaches and have not been able to develop sustainable , population-level rabies control strategies , such as routine availability of post exposure prophylaxis in humans , dog immunization and dog population control [4]–[6] . As demonstrated in Africa , doubts persist among some experts as well as policy makers in low resource settings regarding the technical and operational challenges of rabies control [7] . Concerns related to burden and distribution of rabies as well as cost effectiveness and practicality of the interventions persist among opinion makers even in the face of proven intervention strategies across multiple settings [7] . We propose that this state of ‘policy impasse’ is contributed by the fact , at least in part , that the research community has not catered to the information needs of the policy makers . This phenomenon is not exclusive to rabies . In fact , research to implementation gap has been reported in many other health domains [8] where the mismatch between the outputs from researchers and policy makers' information needs have been described as a key barrier to bridging this gap [9] . India is a major contributor to the global rabies burden , being responsible for 17 , 000–20 , 000 of the 55 , 000–70 , 000 deaths that modelling approaches have suggested to occur globally each year [1] , [2] . In addition , the country has strong institutional capacity for research in medical , veterinary medicine and laboratory sciences . An earlier research prioritization exercise systematically identified priority research options required for prevention and control of zoonoses in India over the next five years ( 2010–15 ) and incorporated the perspectives of a diverse group of stakeholders [10] . Rabies was also specifically identified as a priority zoonosis for India . The exercise found that the identified priority research options highlighted the importance of ‘actionable policy-relevant research’ for the prevention and control of zoonoses in India . The priorities cut across diseases , disciplines , and sectors and focussed more on policy relevant research than research for development of newer biomedical interventions . In this paper , we build upon the findings of the earlier study to systematically review the rabies research output from India and examine its alignment with policy priorities of the country . This review is intended to serve as a case study highlighting the research – policy gap related to rabies in low and middle income countries ( LMICs ) .
The study was designed as a review of rabies-related research published from Indian institutions from 2001 to 2011 as indexed in the PubMed database . PubMed was selected for the search as it is among the most accessible , standardized and extensive sources of life sciences literature in India , covering research publications in veterinary sciences , public health and molecular biology . The search was restricted to Indian institutions publishing rabies research since 2001 so as to ascertain the national research capacity and its alignment with national policy needs as reflected in the prioritisation exercise referred to earlier [10] . We aimed to employ an inclusive search strategy to ensure maximum coverage of original research related to rabies from India . The following search terms were used: “rabies”[MeSH Terms] OR “rabies”[All Fields] ) AND india[Affiliation] AND ( “2001/01/01”[PDat] : “2011/31/12”[PDat] . All original research articles related to rabies published from an Indian institution were included in the review . Articles not related to rabies as an important focus area of the study , case studies , literature reviews , opinion pieces and meeting reports were excluded . The review assessed the concordance between conducted research and policy priorities . Given the topical nature of policy agendas , the review was confined to research conducted in the last eleven years so that these could be contrasted with contemporary policy priorities . A total of 138 articles related to rabies were identified to have been published from India in the last eleven years through PubMed . An initial screening of the records resulted in the exclusion of one PubMed reference to an erratum . Subsequently , the remaining 137 articles were reviewed by two researchers for inclusion in the final database using the criteria described above . Any conflicts in the process were resolved through mutual discussions or consultation with a third researcher . Once the list of articles was finalized , their abstracts were reviewed for extracting metadata on publishing journal , setting of research and institutional affiliation of researchers . The articles were then categorized into research categories used in an earlier research prioritization exercise for zoonoses prevention and control: Instruments of Health Research ( IHR ) and Research Factorials [10] . While the IHR [11] aimed to assess the actionable nature of the findings expected from the research question , the research factorial categories [12] sought to assess the involvement of different sectors in the research question . A listing of these categorizations is mentioned in Table 1 . Detailed descriptions of the research categorizations have been included as a supporting file ( File S1 ) . The process of categorization was carried out primarily by one researcher and a sample of categorizations was reviewed by the second researcher . Any confusions relating to the categorizations or conflict between the ratings of the two researchers were highlighted and resolved through mutual discussion with a third researcher . The proportionate distribution of conducted research into IHRs and the research factorial categories was then compared to their distribution in priority research options identified earlier by national experts and policy makers [10] .
Journals focussing on animal health accounted for only 8% of the publications . Most of the articles were published in broad-based or human centric journals ( 48% and 43% , respectively ) . The 93 identified articles were published through 50 different journals . However , the top three journals accounted for 30% of all the published articles . These journals were Vaccine , Human Vaccines and International Journal of Infectious Diseases . Institutions having Ministry of Health & Family Welfare as the nodal ministry dominated rabies research output , accounting for 57% of identified articles . The veterinary sector followed with 27% , and other institutions contributed 14% of publications . The top two institutions from the human and animal health sectors together accounted for half the total research output . These were National Institute of Mental Health & Neuro Sciences ( NIMHANS ) , Kempegowda Institute of Medical Sciences ( KIMS ) , Indian Veterinary Research Institute ( IVRI ) and Indian Immunologicals Limited ( IIL ) . We identified a total of 29 institutions from human , veterinary and other sectors that have worked on rabies research in the last eleven years . While half ( 14 ) the institutions were from the health sector , one third ( 9 ) were from the veterinary sector . The category of other institutions ( 6 ) included those from the scientific institutions and the vaccine industry . The vast majority of published articles ( 58% ) related to the rabies virus and a third ( 34% ) were human-focussed . Only a minority of articles focussed on dogs ( 7% ) and other animals ( 1% ) . The predominantly bio-medical focus of rabies research was also borne out by a categorisation of settings in which the reported research took place . While 61% of research articles described laboratory based work , 27% of articles related to clinic based research . Only 12% of research articles related to community based research settings . This trend was more pronounced for the veterinary sector where 23 out of a total of 25 articles related to laboratory based research . In contrast , research in the human sector was almost evenly divided between clinical and laboratory research . As described in Table 1 and Figure 2 , a large proportion of rabies research related to basic science research for the development of new interventions . Most of the remaining research options related to epidemiologic research . Less than 10% of conducted research related to improving existing interventions or for research related to health policy and systems . Adopting a different lens of research factorials ( Figure 3 ) , we found that the research conducted so far was almost entirely focussed upon genetic and biological factors ( 86% ) and some social , political and economic research ( 14% ) .
Research output in terms of number of articles was fairly regular over an eleven year period , averaging 8 . 5 research articles per year . However , given the fact that India contributes less than 5% of global research output on rabies yet contains half the disease burden , the quantum of research output does not appear to be in keeping with either the disease burden in India or its institutional research capacity . Research priorities were clearly skewed towards a bio-medical disease paradigm , with pathogen-based research driving the research agenda . Laboratory -based and clinical research focussing on the virus and its disease manifestations appeared to be more popular than risk research , ecological studies , health services research , operations research , economic evaluations and health systems research . As demonstrated by other researchers , this phenomenon is not limited to rabies and is a reflection of limited focus on public health research in India [13] , [16] , [17] and globally [18]–[20] . The distribution pattern of conducted research topics on rabies appeared to be in direct contrast with the research options identified by national experts and policy makers in an earlier study [10] . ( The list of priority research options related to rabies control in India can be found in File S3 . ) As described in Figure 2 , the priority research options for all zoonoses ( n = 103 ) as well as priority research options specifically for rabies ( n = 10 ) had a much more balanced distribution of IHRs and research factorials than what was found among the conducted research . Research for development of new interventions was least favoured among priority research options , while it was the most represented research option among the review articles . Similarly , as depicted in Figure 3 , the distribution pattern of research factors in the review articles was inverse of what was found in the priority research options . The priority research options also focussed upon ecological , physical and environmental factors that were totally absent from the conducted research . While the importance of strengthening basic science research in the long run cannot be disputed , it needs to be understood that program managers and policy makers operate on shorter time frames than researchers . They need more actionable information from the research community , a role that can easily be fulfilled by the public health and veterinary community conversant with the practical challenges of mounting rabies intervention strategies . It is important to situate the findings from our review into the larger context of policy challenges facing rabies control in India and internationally . In spite of repeated attempts , efforts to create a national rabies control program in many LMICs , including India , have not been successful because of challenges in conceptualising a programmatic structure for a multisectoral effort . A national consultation of rabies researchers , program managers and policy makers organised recently in Chennai , India , reviewed the policy landscape of rabies control in India and recognised the fact that rabies-related policy making has largely been conducted in isolation , with little contribution from local research [21] . Inadequate interaction and communication between the research and policy-making communities is caused and exacerbated by the lack of collaborative platforms , differences in perspectives , and institutional barriers . Researchers are often unaware about the information needs of policy makers , while policy makers face limitations in preparing evidence-informed policies . We describe some of the key policy challenges facing rabies planners in India and a sample of indicative knowledge gaps relating to these issues in Table 2 . The identified knowledge gaps are of immediate relevance to policy makers , and filling these gaps can lead to the development of national implementation framework for rabies control in India . Unfortunately , we were unable to find much conducted research that could help answer these questions . While rabies research in India might not be completely reflective of global priorities , we have used it as an illustrative case study to highlight points that can be used to inform a larger discussion on prioritisation for rabies research globally . Researchers have reported similar research-policy disconnect in rabies control in other Asian and African countries . Their concerns relate to the absence of political commitment for rabies control from decision makers as a result of a perceived lack of conclusive information on disease burden and cost effectiveness of existing interventions among others [4] , [5] , [7] . In order to overcome this stalemate and ensure progressive action towards rabies control globally , we propose the development of a strategic research agenda at national and regional levels focussing on rabies control among affected populations as an expected outcome . Such a research agenda would help the planners evolve a unified vision of rabies control involving a closer interaction of different disciplines ( epidemiology , economics , life sciences and sociology , among others ) , sectors ( human , animal and environment ) as well as functions ( researchers , practitioners , policy planners , donor representatives ) . Existing frameworks on national research systems [22] and zoonotic research [23] can be used to inform the development of such a strategic research agenda for combating rabies at the national and regional levels . The policy relevance of conducted research can increase only when the close relationships between policy , program and research functions are recognized and when both research generators as well as research users are equally invested in such an exercise . Development of such a research agenda should , therefore , involve all stakeholder communities for a series of exercises going beyond defining the research needs of a specific population group . While the stewardship and the larger vision will need to come from the policy makers and nodal agencies with the mandate to lead such efforts , the researcher and program manager communities will need to be mobilised to advocate for the development and implementation of such an agenda . As a first step , periodic research prioritization exercises will play a necessary role in aligning research output to the public health needs of the community . Recent initiatives on research prioritization have demonstrated the importance of increasing the policy relevance of conducted research across specific areas [11] , institutions [24] and national research systems [25] . The research agenda will also need to include other mechanisms to increase knowledge translation [26] processes . An indicative list of mechanisms for promoting research-policy interactions include the following: creating knowledge networks , establishing partnerships and allowing mutual exchange of personnel between research , training and implementing organizations , increasing emphasis on evidence based decision making , creating information clearing house , etc [9] , [27] . Possible limitations in the study design that may affect the robustness of its results and the generalizability of its conclusions are listed below . First , we restricted our search to PubMed because of ease of search and the database' coverage of multiple sectors . Although “grey literature” and un-indexed papers were not included as a result of this strategy , PubMed has the largest coverage of all life sciences journals , which ensures that we have captured the majority of literature on rabies research . Second , the corresponding author affiliation was the only way to capture national affiliations . It is possible that resident researchers would have conducted policy-relevant research in collaboration with non-Indian institutions , but we did not include this work as it was not seen to be contributing to the capacity of national institutional research . Third , we have referred to an earlier priority setting exercise by our team that can be used as a comparison with the conducted research . We would have liked to include further measures for validating our conclusions , but to our knowledge , we are not aware of other systematic priority setting exercises in zoonoses in this region . The purpose of this paper was to highlight the need for strategic planning of rabies research and to identify key issues that should be considered in the process using the example of India . The exact processes involved and the identification of precise criteria for research prioritizations will have to be informed by the local context . Rabies research globally has generated a lot of ‘actionable’ evidence related to rabies control . Yet rabies control efforts continue to be neglected in many LMICs . We use the example of rabies research in India to demonstrate the fact that the research community has not been able to sufficiently address the concerns of policymakers . While the rabies research output in India is neither reflective of its share of the disease burden nor its institutional capacity , rabies research conducted in India has the potential to influence the rabies agenda nationally as well as in many LMIC countries if more policy relevant research is conducted . The Planning Commission , Government of India has identified rabies as a priority zoonosis in India that will be targeted through a set of focussed strategies [28] . However , there is no strong evidence base to appropriately inform this well-intentioned strategy . There is an urgent need to address this research-policy gap by developing a strategic research agenda for rabies control at the national and regional levels . Our observations on rabies research in India can be used as a predictor of similar challenges in other LMICs . Therefore , we contend that program priorities should be an important factor in systematically shaping research agendas related to rabies in India and other endemic countries . | Rabies is among the most widely spread zoonoses ( diseases that are naturally transmitted between vertebrate animals and humans ) in humans in most Asian , African and Latin American countries . Even though researchers have demonstrated effectiveness of strategies to control rabies at the population level , such as post exposure prophylaxis in humans and animal birth control and immunization among dogs , are well known , policy makers in most countries are hesitant to implement these strategies . This paper examines the disconnect that prevents the translation of scientific research outputs into effective policies . We contrasted the type of research papers published on rabies from India in the last eleven years with a previously identified set of priority research options . We found that most published research articles related to biomedical research focussing on development of new interventions . This was in contrast to policy and systems-related research and research to improve the performance of existing interventions that were identified as priority research options for India earlier . The findings of our study highlight the importance of moving beyond a purely researcher-driven agenda and suggest the need to promote research that has a vision of rabies control in the near future . | [
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"science",
... | 2012 | Moving from Rabies Research to Rabies Control: Lessons from India |
Bacteria frequently lose biosynthetic genes , thus making them dependent on an environmental uptake of the corresponding metabolite . Despite the ubiquity of this ‘genome streamlining’ , it is generally unclear whether the concomitant loss of biosynthetic functions is favored by natural selection or rather caused by random genetic drift . Here we demonstrate experimentally that a loss of metabolic functions is strongly selected for when the corresponding metabolites can be derived from the environment . Serially propagating replicate populations of the bacterium Escherichia coli in amino acid-containing environments revealed that auxotrophic genotypes rapidly evolved in less than 2 , 000 generations in almost all replicate populations . Moreover , auxotrophs also evolved in environments lacking amino acids–yet to a much lesser extent . Loss of these biosynthetic functions was due to mutations in both structural and regulatory genes . In competition experiments performed in the presence of amino acids , auxotrophic mutants gained a significant fitness advantage over the evolutionary ancestor , suggesting their emergence was selectively favored . Interestingly , auxotrophic mutants derived amino acids not only via an environmental uptake , but also by cross-feeding from coexisting strains . Our results show that adaptive fitness benefits can favor biosynthetic loss-of-function mutants and drive the establishment of intricate metabolic interactions within microbial communities .
Bacterial genomes are highly dynamic in terms of both size and composition [1] . The extensive variation in gene repertoires that characterizes prokaryotic genomes can be caused by genome expansion via horizontal gene transfer and gene duplication or , alternatively , contraction due to gene loss . Interestingly , comparative analyses have provided evidence that gene loss may in fact be quantitatively more important for determining the size of prokaryotic genomes than the gain of new genetic information [1–3] . Indeed , as sequencing technologies improve , more and more microorganisms are being discovered that feature tremendously small genomes [4]; some of which are even smaller than the suggested minimal genome size for cellular life of ~300 kb [5] . Analyzing the genetic content of these reduced genomes revealed—besides a lack of dispensable elements [6]—also the elimination of seemingly essential biosynthetic functions . For example , reconstructing metabolic networks from sequence data to predict the phenotype of the focal organism unraveled that the majority of bacterial genomes analyzed lacked the biosynthetic capability to produce several essential building block metabolites such as amino acids , vitamins , or even nucleobases [7–10] . Surprisingly , the list of genotypes that cannot produce certain metabolites autonomously ( hereafter: auxotrophic genotypes ) does not only include host-associated bacteria such as pathogens [11] or endosymbionts [12–14] , which potentially obtain the required metabolites from their host’s cytoplasm , but also free-living bacteria such as Prochlorococcus and Pelagibacter [15 , 16] that are known to mainly inhabit nutrient-poor environments . The ubiquity of biosynthetic loss-of-function mutations in bacteria that inhabit ecologically disparate environments begs an explanation: Which evolutionary mechanisms have favored a loss of biosynthetic genes over metabolic autonomy in these bacteria ? Two main hypotheses have been put forward to explain these striking observations . First , genetic drift may drive gene loss in bacteria that are obligately associated with eukaryotic hosts . These bacteria experience nutrient-rich and rather constant environmental conditions [17] and frequently undergo reductions in population sizes during host-to-host transmission . As a consequence of these periodic population bottlenecks , the effects of drift may override those of selection [2] . A lack or a drastically reduced frequency of recombination may further accelerate the fixation of non-beneficial or deleterious mutations [12] . This hypothesis is mainly supported by evidence stemming from comparative genomic analyses [18–20] . In addition , selection experiments , in which bacterial populations were repeatedly subjected to single-cell bottlenecks , resulted in bacteria with strikingly reduced genomes [21] . The second main hypothesis that has been proposed to account for the ‘streamlining’ of bacterial genomes is that natural selection favors loss-of-function mutants in environments , in which the gene is no longer required [2 , 22–24] . This line of reasoning has been mainly applied to bacteria with free-living lifestyles or those that face nutrient-poor conditions . The large effective population size bacteria experience in these environments likely increases the efficacy of selection . For instance , it has been previously shown that advantageous mutations occur frequently in experimentally evolved bacterial populations of large effective population sizes [25] . As a consequence , loss-of-function mutations or gene deletions that increase a cell’s fitness are more likely to fix in the population [2] . Adaptive benefits of losing genes may stem , for example , from an increased cellular economization [26] or a saving of production costs , when certain metabolites can be derived from the environment [8 , 23] . Indeed , previous studies that compared the Darwinian fitness of engineered mutants lacking the ability to biosynthesize certain metabolites to non-mutated wild type cells revealed that metabolic auxotrophies can be beneficial , when the required biosynthetic product is sufficiently available in the environment [7 , 8 , 22 , 23] . Furthermore , fitness-increasing deletion mutations have also been observed in bacterial populations adapting to environments , in which the lost functions were not required for survival [27 , 28] . Even though these studies suggest that selection can possibly account for the commonly observed loss of biosynthetic genes from bacterial genomes , the frequencies with which these mutations arise in nutrient-containing environments as well as the fitness effects they exert on the corresponding mutants remain generally unclear . Therefore , direct experimental evidence for natural selection driving the loss of biosynthetic functions , and thus facilitating a metabolic adaptation to the current nutrient environment , is lacking . To unravel whether fitness advantages can indeed drive the loss of biosynthetic functions from bacterial genomes in nutrient-containing environments , we serially propagated eight replicate populations of the initially metabolically autonomous ( hereafter: prototrophic ) bacterium Escherichia coli in amino acid-replete ( hereafter: AA regime ) or -deficient environments ( hereafter: non-AA regime ) . After 2 , 000 generations of evolution in the presence of 20 amino acids , 75% of the experimental populations evolved amino acid auxotrophies–on average for 10 amino acids per strain . Surprisingly , auxotrophic mutants also evolved in the non-amino acid environment , albeit at lower frequencies than in the amino acid-containing environment . The evolution of metabolic auxotrophies was adaptive when amino acids were present in the environment and–surprisingly–also when other co-occurring genotypes could provide the required amino acids . A genomic analysis of derived auxotrophic genotypes revealed that distinct genetic changes in both structural and regulatory genes caused the adaptive loss or deactivation of biosynthetic functions . Our analysis indicates that adaptive advantages can drive the evolution of metabolic auxotrophies in bacteria and thus foster their obligate dependency on the biotic and abiotic environment .
To determine how the growth environment can affect the evolution of adapting bacterial populations , eight replicate populations of Escherichia coli were serially propagated by daily transfer of 1 , 000 cells in minimal medium that did or did not contain all of 20 different amino acids ( AAs ) . Quantifying the cell density that each population achieved at different time points of the evolution experiment as the number of colony forming units ( CFUs ) ml-1 indicated that the mere presence of AAs already benefitted the ancestral genotype as indicated by a significantly increased productivity relative to AA-deficient conditions ( independent samples t-test: P<0 . 05 , n = 8 , Fig 1A ) . This initial pattern remained consistent as these populations were further propagated , resulting in a significantly increased slope of the populations that evolved in the presence of AAs as compared to populations that were selected in unsupplemented minimal medium ( independent sample t-test: P<0 . 05 , n = 8 , Fig 1A ) . In other words , the presence of nutrients significantly increased the rate of increase in cell density . Given that the size of a bacterial population affects its rate of adaptation via influencing the size of fitness advantage of fixing beneficial mutations [29] , reaching an increased cell density in the presence of AAs likely sped up adaptation in these evolving populations . To determine whether the increased cell density of populations that evolved in the presence of AAs was indeed due to the increased availability of nutrients , the growth rates ( μmax h-1 ) the focal populations achieved in the presence ( i . e . the selection regime ) of AAs was subtracted from the growth rates the same populations achieved under AA-deficient conditions and the percent change in growth rates was calculated . The resulting value ( percent change ) was significantly greater than zero for 4 of 8 of the ancestral populations ( false discovery rate ( FDR ) -corrected paired samples t-test: P<0 . 05 , n = 4 , Fig 1B ) , indicating that the exponential growth of the evolutionary ancestor was inhibited in the presence of AAs . However , over the course of evolution , an omission of amino acids from the test medium resulted in a decline of growth rates for most of the replicate populations to the point that at 2 , 000 generations , seven out of eight populations showed significantly reduced growth rates in the absence of AAs ( FDR-corrected paired samples t-test: P<0 . 05 , n = 4 , Fig 1B ) . In summary , analyzing the evolution experiment on a population level revealed that the rate , with which populations of E . coli increased in cell density , was increased in the presence of AAs . Moreover , the growth of AA-evolved populations became increasingly dependent on the presence of AAs over the course of the evolution experiment . One possibility to explain the AA-dependent increase in the productivity of populations that evolved in AA-containing environments could be the emergence of AA auxotrophic genotypes that benefitted from utilizing environmentally available AAs . These strains would be unable to grow in the absence of AAs , yet can grow when AAs are present [8] . To determine whether and to which extent auxotrophic genotypes evolved in both selection regimes , 1 , 000 colonies of each replicate population from different evolutionary time points were screened for the presence of auxotrophic genotypes . After 0 , 250 , and 500 generations , no auxotrophic CFU was detected in any of the populations that evolved in the presence of AAs ( Fig 2A ) . However , when populations from later time points were considered , 50% ( 1 , 000 generations ) , 25% ( 1 , 500 gens . ) , and 75% ( 2 , 000 gens . ) of the AA-evolved populations contained auxotrophic genotypes ( Fig 2A ) . The proportion of auxotrophic genotypes detected in these populations ranged between 0 . 8% and 2 . 5% after 1 , 000 generations , between 5 . 7% and 20% after 1 , 500 generations , and between 0 . 6% to 7 . 5% after 2 , 000 generations of evolution in the AA-containing environment ( Fig 2A ) . Intriguingly , three of the eight populations that evolved without an external supply of AAs also featured AA auxotrophic strains ( Fig 2B ) : Replicate population 3 , which had evolved for 500 generations contained nearly 2% of auxotrophic genotypes , while replicate populations 7 and 8 comprised approximately 1% of auxotrophic strains after 1 , 500 generations of evolution ( Fig 2B ) . Detecting auxotrophic genotypes in populations that evolved in the absence of AAs is surprising and suggests that these loss-of-function mutants likely obtained the AAs they required for growth from the coexisting prototrophic genotypes , which were present in high frequencies . A striking pattern that arose in both selection regimes was the dynamics that characterized the emergence of auxotrophic genotypes . Even though the number of auxotrophic genotypes generally increased over evolutionary time , their distribution and abundance within replicate populations showed a high degree of fluctuation around the detection limit of 1 , 000 cells ( Fig 2 ) . For instance , replicate population 1 , which has evolved in the presence of AAs , contained 5 . 7% auxotrophic strains after 1 , 500 generations , but no auxotrophic strain was detectable after 2 , 000 generations . However , given that a frequency change of 1% corresponds to at least 104 auxotrophic cells , the observed fluctuations are significant on a population-level . Moreover , the fact that their frequency rose to detectable levels ( ≥104 cells ) implies these mutants were likely selectively favored . Analyzing the number of different metabolic auxotrophies that were found on a genotype-level revealed that strains isolated from the AA-containing environment were generally impaired in the biosynthesis of more than four different AAs simultaneously , while auxotrophs that evolved in the AA-deficient environment depended on average on only one or two different AAs ( Pearson’s chi square test: P<0 . 05 , n = 483 ( AA regime ) and 37 ( non-AA regime ) , Fig 2C and 2D; S1 Table ) . Some of the strains , which have been isolated after 2 , 000 generations of evolution in the AA-containing environment , even required all 20 AAs for growth ( Fig 2C ) . Auxotrophic types that emerged in individual populations after 2 , 000 generations of adapting to an AA-containing environment showed a striking congruence in the identity of amino acid auxotrophies that evolved in these populations ( S1 Fig ) , suggesting adaptive advantages likely drove this pattern . Taken together , these results demonstrate that AA auxotrophies evolved in both selection regimes , yet to a significantly larger extent when AAs were present in the environment . The finding that the frequency of auxotrophic strains increased under both conditions to detectable levels implies that these loss-of-function mutants were likely selectively favored . To identify whether the rapid emergence and spread of auxotrophic genotypes was driven by selective advantages that resulted from the loss of biosynthetic functions , auxotrophic and prototrophic genotypes that evolved in either the absence or presence of all AAs were individually competed against their common evolutionary ancestor . Determining competitive fitness in this way revealed that auxotrophic strains , which evolved in the presence of AAs , were significantly fitter than their evolutionary ancestor when all 20 AAs were present in the environment ( i . e . the selection regime ) ( FDR-corrected independent samples t-test: P<0 . 05 , n = 4 for each of 28 the auxotrophic strains in S1 Table , Fig 3A ) . However , when AAs were omitted , these auxotrophic genotypes were significantly less fit than the ancestral genotype ( FDR-corrected independent samples t-test: P>0 . 05 , n = 4 for each of the 28 auxotrophic strains isolated from the AA regime , S1 Table , Fig 3A ) . This observation implies that auxotrophic genotypes increased in frequency , because they gained an adaptive advantage when AAs were present in the environment . In contrast , the evolutionary success of derived prototrophs was independent of an environmental availability of AAs , as indicated by the finding that their fitness was significantly increased over ancestral levels independent of whether or not AAs were present in the environment ( FDR-corrected paired samples t-test: P>0 . 05 , n = 4 for each of the 28 prototrophic strains in that coexisted with the auxotrophic strains , S1 Table , Fig 3A ) . A qualitatively similar picture emerged when the derived strains of the non-AA regime were analyzed: Supplementing AAs to the growth medium resulted in an increased fitness of auxotrophic genotypes relative to their prototrophic ancestor ( FDR-corrected independent sample t-test: P<0 . 05 , n = 4 for 3 derived auxotrophic strains , Fig 3B ) . However , under unsupplemented conditions ( i . e . the selection regime ) , auxotrophs were less fit than the ancestor ( FDR-corrected independent samples t-test: P<0 . 05 , n = 4 for 3 derived auxotrophic strains , Fig 3B ) . In line with the above findings , prototrophic genotypes isolated from the derived non-AA populations gained a significant fitness advantage over their ancestor in the absence of AAs ( i . e . the selection regime ) ( FDR-corrected independent samples t-tests: P<0 . 05 , n = 4 for 3 derived prototrophic strains , Fig 3B ) that was quantitatively comparable to the advantage AA-evolved prototrophs gained under the same conditions ( Fig 3 ) . Taken together , these findings imply that an environmental availability of AAs favored mutants that have lost the ability to autonomously biosynthesize certain AAs . In contrast , the fitness advantage gained by prototrophic genotypes was independent of the presence of AAs in the environment . Two findings of the abovementioned experiments beg an explanation . First , AA auxotrophies evolved even when no AAs were present in the environment ( Fig 2B ) . Second , even though AA-evolved auxotrophs were less fit than the ancestor when no AAs were present in the environment ( FDR-corrected independent samples t-test: P<0 . 05 , n = 4 for 28 derived auxotrophic strains , Fig 3A ) , these genotypes still grew to detectable frequencies in the corresponding fitness assays . From where did these auxotrophic genotypes obtain the AAs they needed to grow ? A likely source could be the prototrophs that coexisted with the auxotrophic genotypes in the two abovementioned experiments . To test this possibility , auxotrophic genotypes , which were phenotypically dissimilar based on their metabolic auxotrophies and isolated after 2 , 000 generations ( S1 Table ) , were grown in monoculture , together with the prototroph they coevolved with , or the evolutionary ancestor ( both cocultures: 1:1 ratio ) . This test was performed in either the absence or presence of AAs and the fitness of auxotrophs ( i . e . their Malthusian parameter ) was determined . As expected , auxotrophs were unable to grow when no AAs were present in the environment , yet grew when all AAs were supplemented to the growth medium . This held true for all auxotrophs analyzed from both selection regimes ( Fig 4A and 4B ) . However , coculturing auxotrophic genotypes in the absence of AAs together with the prototrophic strain they had coevolved with , resulted in fitness levels of auxotrophs that were statistically indistinguishable to the levels they have reached in monoculture in the presence of AAs ( one-way ANOVA followed by a LSD post-hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic strains ( AA regime ) , n = 4 for 3 derived auxotrophic strains ( non-AA regime ) , Fig 4 ) indicating that auxotrophs derived amino acids from the cocultured prototrophs . Supplementing these cocultures with additional AAs further increased the fitness of AA-evolved auxotrophs over the levels they reached under unsupplemented conditions ( one-way ANOVA followed by a LSD post-hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic strains , Fig 4A ) , while the growth of non-AA-evolved auxotrophs did not change upon AA supplementation ( one-way ANOVA followed by a LSD post-hoc test: P>0 . 05 , n = for 3 derived auxotrophic strains , Fig 4B ) . Interestingly , when the focal auxotrophs were cocultured with the evolutionary ancestor and not the coevolved prototrophs , auxotrophs isolated from the AA regime were generally less fit as compared to the situation when the coevolved prototroph was present ( one-way ANOVA followed by a LSD post-hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic strains , Fig 4A ) . Still , AA supplementation significantly enhanced the fitness of AA-evolved auxotrophs in coculture with the ancestor over the fitness reached in unsupplemented medium ( one-way ANOVA followed by a LSD post-hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic strains , Fig 4A ) . This pattern is consistent with a coevolutionary change between derived auxotrophs and prototrophs that is absent when the ancestral prototroph is the interaction partner . In contrast , for auxotrophs that evolved in the non-AA regime , it did not make a difference whether the coevolved prototrophs or the evolutionary ancestor was present when the coculture was exposed to unsupplemented minimal medium ( one-way ANOVA followed by a LSD post-hoc test: P>0 . 05 , n = 4 for 3 derived auxotrophic strains , Fig 4B ) . However , when AAs were provided to these cocultures , the auxotrophs reached the highest fitness levels of all experimental conditions analyzed ( one-way ANOVA followed by a LSD post-hoc test: P>0 . 05 , n = 4 for 3 derived auxotrophic strains , Fig 4B ) . Together , these observations suggest that either the auxotrophs’ ability to derive AAs from the coexisting prototrophs increased over time or , alternatively , prototrophic cells increased their amino acid production levels . In either way , these results demonstrate that evolved auxotrophs utilized the AAs that were available in the growth environment as well as those they could obtain from other , coexisting strains . Which ecological mechanism maintained the evolved genotypic diversity ( i . e . both auxotrophic and prototrophic genotypes ) in the evolution experiment ? A likely possibility that has been previously identified to be key for maintaining synthetically engineered cross-feeding genotypes that reciprocally exchange essential AAs is negative frequency-dependent selection [30] . To determine whether the same mechanism also stabilized our naturally-evolved genotypes , the ability of derived auxotrophs and prototrophs to invade a population of the respective other strain when rare was determined in the absence and presence of AAs in the environment . Under all conditions tested , the initially rare type ( i . e . auxotroph or prototroph , initial ratio: 1:100 ) was able to invade a resident population of the respective other strain as evidenced by its significantly increased selection coefficients ( one-sample t-test: P<0 . 05 , n = 4 for 6 derived auxotrophic or prototrophic strains for the AA regime , Fig 4C and 4E ) . Even following the fate of both types over 75 bacterial generations revealed for both auxotrophs and prototrophs a pronounced fitness advantage when rare that steeply declined as the population-level proportion of the focal type increased ( linear regression , P<0 . 05 , n = 4 for 6 auxotrophs and prototrophs from the AA regime and n = 4 for 3 auxotrophs and prototrophs from the non-AA regime , Fig 4D and 4F ) . Interestingly , this pattern was independent of whether or not AAs were supplied to the medium . In other words , the above findings corroborated the hypothesis that AA cross-feeding between prototrophic donor cells and auxotrophic recipients is maintained by negative frequency-dependent selection and that the frequency of both types oscillates around a stable equilibrium point . To determine whether different growth strategies of the two coevolved genotypes could cause the observed negative frequency-dependent selection , monocultures of the AA-evolved genotypes and the evolutionary ancestor were grown in the presence of AAs and their growth kinetic parameters were compared . This experiment revealed that the derived auxotrophic and prototrophic strains grew significantly faster and achieved a higher cell density than the ancestral strain ( one-way ANOVA followed by a LSD post hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic or prototrophic strains and the ancestor , S2A Fig ) . Moreover , the growth of auxotrophic strains was characterized by a significantly shorter lag phase ( one-way ANOVA followed by a LSD post hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic or prototrophic strains and the ancestor , S2A Fig ) and an earlier onset of the stationary phase than was the case for both derived and ancestral prototrophic genotypes ( one-way ANOVA followed by a LSD post hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic or prototrophic strains and the ancestor , S2B Fig ) , indicating that auxotrophic strains likely utilized environmentally available AAs until this pool was depleted . In contrast , prototrophic genotypes remained much longer in the exponential growth phase than auxotrophs ( one-way ANOVA followed by a LSD post hoc test: P<0 . 05 , n = 4 for 6 derived auxotrophic or prototrophic strains and the ancestor , S2C Fig ) , suggesting that their growth was limited by the carbon source rather than the amount of AAs present in the environment . Taken together , these observations suggest that due to their inability to autonomously produce certain amino acids , auxotrophs first utilized the pool of environmentally available AAs before they exploited the AAs produced by other , coexisting strains . This bi-phasic growth pattern together with their obligate dependency on coexisting prototrophs likely maintained auxotrophs by negative frequency-dependent selection . To unravel the genetic basis of the metabolic auxotrophies that emerged in the course of the evolution experiment , genomes of 8 auxotrophs and 6 prototrophs from different replicate populations , which had evolved for up to 2 , 000 generations in the AA regime ( Fig 5A and 5B , 6 auxotrophs and 4 prototrophs , S3 Table ) and up to 1 , 500 generations in non-AA regime ( Fig 5C and 5D , 2 auxotrophs and 2 prototrophs , S3 Table ) , were sequenced and their genome sequence compared to that of the evolutionary ancestor . This analysis revealed that derived auxotrophic mutants carried significantly more mutations in their genome than the coevolved prototrophs ( Pearson’s chi square test: P<0 . 05 , n≥6 ) . Moreover , both types had very few mutations in common ( Fig 5E , S2 Table ) , confirming that co-occurring auxotrophic and prototrophic cells represented genetically distinct subpopulations . Next , we focused on those mutations that arose exclusively in genomes of auxotrophic genotypes with the aim to functionally link them to their auxotrophic phenotypes . Interestingly , auxotrophs isolated from the two selection regimes differed starkly in the mutations present in their genomes ( Fig 5E ) . However , very few of these mutations showed an obvious involvement in AA metabolism . The two auxotrophs isolated from the non-AA regime carried identical SNPs in the rpoB gene , which encodes the β-subunit of RNA polymerase ( RNAP , b3987 , ECK3978 ) . Mutations in this gene are known to result in a specific down-regulation of genes involved in AA biosynthesis [31] . Indeed , both of these genotypes were auxotrophic for the same set of AAs: lysine and tryptophan . In contrast , auxotrophies that arose in the AA-containing environment were caused by completely different mechanisms , including the loss of regulatory and structural genes ( Fig 5A and 5E , S2 and S3 Tables ) . One auxotroph 2-AA-AT ( population R2 ) lost approximately 13 kb from its genome ( Fig 5A and 5E , S2 and S3 Tables ) due to the deletion of a large fragment that comprised 14 genes . These included the genes encoding the BaeSR two-component regulatory system , loss of which has been previously implicated in the down-regulation of multiple regulatory and AA biosynthesis-associated proteins [32] . Another auxotroph 4-AA-AT ( population R5 , see S3 Table for description of genotypes ) carried a non-sense mutation , which inactivated sspA , a gene that encodes the stringent starvation protein A ( Fig 5A and 5E , S2 and S3 Tables ) . This protein is known to be involved in the regulation of AA biosynthesis and mutants lacking this gene have been shown to lose viability under arginine-limiting conditions [33] . The only case , in which a biosynthetic gene was actually lost from the genome was in case of an auxotroph 6-AA-AT that has been isolated from population 6 . This mutant had lost a 13 kb region from its genome comprising 20 genes , which included proA and proB , two genes that are essentially involved in the biosynthesis of the AA proline [34] . However , for most of the observed mutational changes , it remained unclear whether or not a functional link to the cells’ amino acid metabolism existed . To address this issue , we first investigated whether the mutations unique to auxotrophic genotypes inactivated gene functions using the PROVEAN algorithm ( S2 Table ) [35] . This analysis revealed auxotrophs isolated from both selection regimes bore significantly more loss of function mutations in their genome than the cognate prototrophic types ( Fig 5F , Pearson’s chi square test: P<0 . 05 , n≥45 ) . Next , to determine if the predicted loss-of-function mutations ( S2 Table ) can negatively affect growth of the corresponding mutants when AA are lacking , each of the 13 mutations that were unique to 6 auxotrophic genotypes ( Genotypes: 2-AA-AT , 3-AA-AT , 5-AA-AT , 6-AA-AT , 8-AA-AT , and 8-NA-AT ) were transferred into the ancestral background of the prototrophic WT and the growth of the resulting mutants was analyzed in the absence of AAs . Almost half of these reconstructed mutants ( 46% ) did not show detectable growth in the presence of AAs ( one sample t-test , P<0 . 05 , n = 8 for each of the 6 auxotrophs tested , S3 Fig ) , suggesting that these alleles likely caused the observed auxotrophic phenotypes . In summary , this analysis revealed that genomes of evolved auxotrophic genotypes carried significantly more mutations than their prototrophic counterparts and that mutations identified in auxotrophs were more likely to cause a loss-of-function than the ones detected in prototrophic genotypes . To determine if auxotrophy-causing mutations increase bacterial fitness in AA-containing environments and were thus selected for , the derived auxotrophic genotypes whose genome sequence has been analyzed ( S3 Table ) as well as the mutants , in which the identified mutations have been reconstructed ( S4 Table ) , were individually competed against the evolutionary ancestor in the presence of AAs . This included genotypes 2-AA-AT , 3-AA-AT , 5-AA-AT , 6-AA-AT , and 8-AA-AT that evolved in the AA regime , 8-NA-AT that evolved in the non-AA regime , as well as the corresponding 13 mutants that each carried one the reconstructed mutations . Interestingly , 4 of the 7 auxotrophy-causing mutations identified ( i . e . insF1- mdtB , sspA , stpA , and ykfC-proB ) significantly increased fitness of the corresponding mutants in the presence of AAs relative to the ancestral strain ( independent sample t-test: P<0 . 05 , Fig 6 ) , while one mutant ( yhdW ) showed a trend in this direction ( independent sample t-test: P = 0 . 094 , n = 8 , Fig 6 ) . Fitness values of the other mutants that carried the three remaining auxotrophy-causing mutations were statistically indistinguishable from the levels of the evolutionary ancestor ( independent samples t-test , P<0 . 08 , n = 8 for each of the mutants tested , Fig 6 ) . One of these mutations ( in rpoB ) originated from the auxotroph 8-NA-AT that evolved in the absence of AAs . Interestingly , two of the six mutations that did not cause a metabolic auxotrophy ( i . e . uspC/flhD and yqiB ) also gave rise to a fitness advantage in the corresponding mutants when AAs were present in the environment . Taken together , finding that most of the mutations that resulted in amino acid auxotrophies also increased bacterial fitness in the presence of AAs corroborated that indeed auxotrophy-causing mutations were selectively favored in AA-containing environments .
Why are metabolic auxotrophies so common in natural microbial communities ? Hypothesizing that adaptive benefits may account for the frequently observed loss of metabolic functions , our evolution experiment revealed that prototrophic Escherichia coli cells rapidly evolved metabolic auxotrophies when adapting to environments that contained all of 20 different AAs . Interestingly , also serial propagation in AA-free environments resulted in the emergence of genotypes that had a lost the ability to autonomously produce some amino acids , yet the number of auxotrophies per strain , the number of auxotrophic strains per population , and the number of populations containing auxotrophs was significantly lower relative to populations that evolved under AA-replete conditions . In line with prior expectations , auxotrophic genotypes that evolved in AA-containing environments gained an adaptive advantage over their evolutionary ancestor , yet the observed fitness benefit was contingent on the presence of AAs in the environment . Surprisingly , evolved auxotrophs also derived amino acids from coexisting prototrophic cells and this interaction was stabilized by negative frequency-dependent selection . Multiple genetic routes lead to the inactivation of AA biosynthetic abilities , including mutations in both regulatory and structural genes . Moreover , reconstructing all mutations identified in auxotrophic genotypes in the ancestral WT background revealed that most auxotrophy-causing mutations that were considered , resulted in an increased fitness of the corresponding mutant in AA-containing environments , thus strongly suggesting that they were selectively favored under the experimental conditions . A main outcome of the evolution experiment was that adaptive benefits drove the rapid loss of biosynthetic functions when the focal metabolites were sufficiently present in the cell-external environment . These findings are in line with previous analyses , which revealed a significant fitness advantage synthetically engineered , auxotrophic mutants gained over competing prototrophic types when AAs were sufficiently present in the environment [7 , 8 , 23 , 30] . What could explain these adaptive benefits ? One explanation could be that the loss of biosynthetic functions in the presence of metabolites in the environment results in energetic savings for a cell that might be due to the saving of protein production costs [7 , 8 , 23 , 36] . Alternatively , a regulatory or metabolic rewiring of cells could provide auxotrophs with a growth advantage in AA-deficient conditions—for example by changing fluxes through the cells’ metabolic networks [37] . Another explanation could be that auxotrophic cells do not only use amino acids as building block metabolites , but also catabolize AAs . This could explain the advantage auxotrophic genotypes gain relative to prototrophic cells , which autonomously produce the AAs they require for growth . Which of these mechanisms causes the fitness increase of auxotrophic genotypes in AA-containing environments , however , remains unknown and should be subject to further investigation . A prediction that follows from our observations is that metabolic auxotrophies should rapidly evolve whenever bacteria are cultivated in AA-rich media or inhabit environments with increased AA-availabilities [8 , 38 , 39] . Indeed , metabolic auxotrophies have been repeatedly reported to arise in laboratory-based evolution experiments [11 , 40 , 41] or have been detected in natural microbial communities [7–10 , 26 , 30 , 42–44] . In our experiment , derived auxotrophs always coexisted together with metabolically autonomous prototrophs . A strikingly similar pattern has been previously observed in populations of Pseudomonas aeruginosa that adapted to the lungs of cystic fibrosis ( CF ) patients: both prototrophic and auxotrophic strains have been isolated from the AA-rich mucus that fills the lungs of these CF patients [44 , 45] . Independent of whether or not AAs were present in the selective environment , auxotrophs that evolved in our evolution experiment always obtained AAs also from other community members such as the coexisting prototrophs . Two mechanisms are conceivable how auxotrophs obtained the AAs they required for growth: metabolites might be exchanged among genotypes via diffusion through the cell-external environment [46–48] or , alternatively , in a contact-dependent manner [49 , 50] . Recently it has been described that auxotrophic cells of E . coli can produce so-called ‘nanotubes’ to directly obtain cytoplasmic AAs from other bacterial cells [50] . These structures likely minimize the costs to the AA-producing cell by reducing the loss of AAs to the cell-external environment . Thus , the formation of nanotubes of AA-starved bacteria might be interpreted as a strategy to survive under AA-limiting conditions . Such a scenario could explain the evolution of auxotrophic genotypes in the non-AA regime . A contact-dependent exchange mechanism might also have allowed growth of auxotrophic genotypes after depletion of amino acids in the cell-external environment . Analyzing the genomes of derived mutants unveiled a diverse spectrum of mutations that caused the observed phenotypes . The finding that auxotrophic genotypes bore on average significantly more loss-of-function mutations than the cognate prototrophs strongly suggests the adaptive loss of functions resulted in the observed auxotrophies ( Fig 5D ) . In contrast to expectations , deactivation of amino acid biosynthetic pathways via a deletion of the corresponding structural genes was much less common than the loss of regulatory elements with putative roles in AA metabolism ( Fig 5 , S2 Table ) . Interestingly , auxotrophies that evolved in the non-AA regime were most likely due to mutations that down-regulated the expression levels of AA biosynthetic genes , while most auxotrophies that evolved in the AA-containing environment were caused by a complete loss of enzyme-coding regions or an inactivation of the corresponding regulatory elements . This pattern likely mirrors differences in the two selective regimes . While the environment that did not contain AAs penalized any newly evolved auxotroph , whose metabolic deficiency could not be compensated by any of the prototrophic types present , the AA-replete condition likely permitted many more different auxotrophs to increase in frequency . Indeed , the only auxotrophs that could be detected in the lines that evolved under AA-free conditions had lost the ability to produce leucine , lysine , and tryptophan , which incur relatively low metabolic costs [51] and are thus cheaper for the corresponding prototrophs to produce . In contrast , in the AA-replete environment , many more auxotrophic mutants evolved , with all replicate populations displaying a core set of common auxotrophies ( S1 Fig ) . Since both epistatic interactions among mutations [7] and metabolic costs to produce the corresponding amino acids [8] determine the fitness consequences of a biosynthetic loss-of-function mutation in E . coli , the observation of such strikingly parallel changes likely reflects selective constraints acting on the evolved populations . Fitness consequences resulting from the individually reconstructed , auxotrophy-causing mutations were in a majority of cases different from the fitness levels the derived auxotrophic strain achieved ( Fig 6 ) . Given that all evolved genomes analyzed contained multiple mutations , the fact that one individual mutation could not in all cases explain the fitness of the whole organism likely resulted from epistatic interactions among mutations that have been previously shown to strongly affect the fitness of auxotrophic genotypes [7] . This means that those auxotrophy-causing mutations that did not cause an increased fitness in the reconstructed mutant may have been adaptive in the genetic background in which the mutation arose . In addition , also an accumulation of multiple beneficial mutations in the same genetic background could explain why reconstructing the auxotrophy-causing mutation in the ancestral background was not in all cases sufficient to reconstitute the fitness level of the derived genotype , from which this mutation originated . Unfortunately , due to a lack of clear evolutionary lineages , these hypotheses escape an experimental validation . Interestingly , some of the mutations that did not cause an AA auxotrophy were found to be neutral ( 1 case ) or even deleterious ( 2 cases ) ( Fig 6 ) . These mutations likely hitchhiked on another adaptive mutation that arose in the focal genotype–a phenomenon that has been observed previously in other selection experiments [25 , 52] . Theoretical predictions of metabolic auxotrophies in otherwise uncharacterized bacterial genomes have been largely based on whether or not a given biosynthetic pathway exists in the focal organism [7–9] . Due to a lack of understanding of the underlying regulatory networks , these approaches usually neglect the multifarious genetic routes that can possibly cause metabolic auxotrophies . Consequently , previously published estimates that only consider the absence or presence of biosynthetic genes [7–9] likely underestimate the true number of auxotrophic prokaryotes in nature dramatically . Given that the fitness advantage multiply auxotrophic bacteria gain are strongly affected by epistatic interactions among the auxotrophy-causing mutations [7] , mutationally-induced regulatory changes could represent an effective bypass of this evolutionary constraint . The adaptive loss of metabolic capabilities and the emergent dependence on other co-occurring strains as observed in this study have significant ramifications for the evolution of bacterial genomes . A striking pattern that emerges when genomes of multiple different bacterial clones are sequenced that have coexisted together for extended time periods , is not only the frequent loss of many biosynthetic functions from their genomes , but often also a high degree of metabolic complementarity on the genomic level . Examples involve both free-living bacterial communities [53] and consortia of endosymbiotic bacteria , whose metabolite production is intricately interwoven between their eukaryotic host [12 , 54 , 55] and other coevolving bacteria [14 , 56 , 57] . In the latter case , loss of biosynthetic functions has been suggested to arise as a consequence of drift resulting from periodic bottlenecks leading to low population sizes . Empirical evidence , however , suggests that the bottleneck size experienced by bacterial symbionts during transfer between insect hosts is usually around 103 CFUs [58 , 59] . This population size is strikingly similar to the number of bacterial cells that were serially passaged in our evolution experiment . Assuming the cytoplasm of host cells is as nutrient-rich as the medium used in this study , the evolution of metabolic complementarities in insect endosymbionts could be selectively favored as well . Given the ease , with which metabolic auxotrophies evolve , thereby rendering the resulting mutants dependent on other coexisting organisms , it is conceivable how this event can set the stage for a coevolutionary race , in which the interacting partners may further benefit from losing additional metabolic functions . This race will most likely favor those loss-of-function mutants , which are fitter than other competing genotypes given the presence of a donor that can sufficiently compensate for their deficiencies . This ‘black-queen’-like process [46 , 48] can then lead to coadaptations on both sides . Indeed , our observation that the AA-evolved auxotrophs grew significantly better when cocultured with the derived prototrophs than their evolutionary ancestor supports this interpretation ( Fig 4A ) . In the long-run , this process should lead to metabolic networks that are intricately interconnected between multiple different bacterial genotypes . Ultimately , the findings of our study may provide a plausible explanation of why most bacterial species known are difficult to cultivate under laboratory conditions [60 , 61]: they have most likely adapted to the nutritional biotic and abiotic environment they encountered in nature , which complicates a reproduction of these conditions in the laboratory .
The isogenic ancestor of the evolution experiment was Escherichia coli BW25113 Ara- or Ara+ . The Ara+/- phenotypic marker ( i . e . presence/ absence of the araA gene [7 , 8] ) was used to phenotypically discriminate strains ( S3 Table ) ( e . g . ancestral and derived strain ) by red-white differentiation . When growing on tryptone agar ( TA ) containing 10% arabinose and 0 . 5% tri-phenyl tetrazolium chloride ( TTC ) [62] , Ara- colonies release basic end products , while Ara+ colonies release acidic end products . This causes a color change of the pH-dependent TTC dye [62] , giving rise to white ( Ara- ) and red colonies ( Ara+ ) . Unless stated otherwise , liquid minimal medium for Azospirillum brasillense ( MMAB ) [63] with 0 . 5% fructose as carbon source and without biotin was used for cultivating bacteria in all experiments . For some experiments , all 20 AAs ( +AA regime ) were supplemented to the MMAB medium—each at a concentration of 100 μM . When solid MMAB medium was used , 1 . 5% agar was supplemented to the liquid minimal medium . For all experimental assays , strains and populations were first precultured in the conditions they evolved in before the experimental assay was inoculated ( 1:100 dilution ) . All precultures and growth assays were performed at 30°C with shaking at 220 rpm for 18 or 24 hours , respectively . All experiments involving monocultures were initiated using ~105 cells ml-1 of the focal strain or population and cocultures were inoculated with ~105 cells ml-1 of each strain or population . Eight independent lineages were founded from four isogenic clones of either E . coli BW25113 ( Ara- ) [64] or E . coli BW25113 ( Ara+ ) [8] . The eight lineages were serially propagated for 2 , 000 generations by daily transfers into 1 ml of fresh MMAB medium containing a mixture of all 20 amino acids ( i . e . AA regime ) or 1 ml of unsupplemented MMAB medium ( i . e . non-AA regime ) . Each day , 1 μl of culture medium was transferred to 999 μl of fresh medium ( 1:1 , 000 dilution ) . The transfer volume contained ~103 cells and the population expanded to ~106 cells after 24 hours of growth , resulting in ~10 generations ever day ( generations = [ ( log number of cells after 24 h ) — ( log number of cells initially present ) ]/ 0 . 301 ) . To ensure the size of the population bottleneck was consistent during the course of the experiment , the number of colony forming units ( CFUs ) was determined in regular intervals ( i . e . every 150 generations ) and the dilutions adjusted accordingly . Evolving populations were frozen at -80°C at 250 , 500 , 1 , 000 , 1 , 500 , and 2 , 000 generations along with 50% glycerol for subsequent experiments . Productivity of the evolving populations was measured by determining the number of CFUs ml-1 on Lysogeny Broth ( LB ) agar plates at 15 , 180 , 450 , 750 , 870 , 1 , 170 , 1 , 365 , 1 , 590 , 1 , 695 , 1 , 920 , and 1 , 995 generations . Growth kinetic parameters like the maximal growth rate ( μmax h-1 ) and the maximum optical density at 600 nm reached during 24 h ( ODmax ) were determined for populations and clones isolated from different time points over the course of the evolution experiment . For this , frozen samples were revived by inoculating 10 μl into 1 ml of the corresponding medium as described above . Growth assays were performed in 50 μl of medium in a 384 micro-well plate ( Greiner , Germany ) and growth kinetics were monitored in a Tecan Infinite Pro Microplate reader ( Tecan , Austria ) by recording the OD every eight minutes for 24 h at 30°C with shaking at 2 . 5 Hz in the interim . Calculating the percent change between the maximal growth rates that a genotype achieved during 24 h of growth in the presence of AAs ( 20 AAs each at a concentration of 100 μM , μmax h-1+AA ) from the values reached under AA-free conditions ( μmax h-1-AA ) provided a quantitative measure to compare the growth of a specific population under both conditions . This was calculated using the formula: Percent change = {[ ( μmax h−1−AA−μmax h−1+AA ) x 100 ]/μmax h−1+AA} −100 Positive values indicate the population growths better in the absence of AAs , while negative values point to a growth-stimulating effect of AAs . This experiment has been performed with the ancestral as well as the eight derived populations after 250 , 500 , 1 , 000 , 1 , 500 , and 2 , 000 generations of evolution in the absence and presence of AAs . Each comparison has been replicated 4 times . The emergence of auxotrophic mutants in the evolving populations was determined by resuscitating and preculturing frozen samples from generations 250 , 500 , 1 , 000 , 1 , 500 , and 2 , 000 of both selection regimes . These cultures were then serially diluted such that each population contained ~103 CFUs ml-1 and plated on MMAB agar plates that contained all AAs . 1 , 000 colonies from each population of the two regimes were then inoculated onto a fresh MMAB agar plate that contained all AAs . The colonies were then replicated on MMAB agar plates without any AAs using a 96-pin replicator to identify colonies that were unable to grow . Any colony that failed to grow on MMAB without AAs was deemed auxotrophic . The colonies identified in this way were then replica-plated on MMAB plates without AAs and 20 different MMAB ‘drop-out’ media—each containing a different combination of 19 AAs , leaving out one specific AA [65] . This approach allowed determining specific AA-auxotrophies of the focal strains . Strains that were unable to grow on MMAB without AA supplementation yet could grow on all 20 drop-out media , were deemed ‘non-assigned’ ( NA ) . The fitness of each auxotrophic or prototrophic type that has been isolated after 1 , 000 , 1 , 500 , and 2 , 000 generations of growth in the AA regime and after 500 and 1 , 500 generations in the non-AA regime was determined in competition experiments against the evolutionary ancestor . For this , ~105 cells of the derived clone as well as of the evolutionary ancestor that was carrying the respective other Arabinose utilization marker ( i . e . Ara+ versus Ara- and vice versa ) were precultured and subsequently co-inoculated into 1 ml of MMAB with or without AAs . The relative fitness of strains from the AA regime was determined in the absence or presence of AAs , while the strains from the non-AA regime were only analyzed in the absence of AAs . The number of CFUs ml-1 was determined at the onset ( i . e . 0 h ) and at the end of the coculture period ( i . e . after 24 h ) by plating on TA agar plates and the Malthusian fitness parameter was calculated as described previously [62] . Relative fitness measurements of each individually isolated genotype were replicated 8 times . A similar approach was used to determine the fitness effects of each of the 13 mutations that have been transferred to the WT background . The reconstructed strains that each carried a single mutation ( Ara- ) were competed against the evolutionary ancestor ( Ara+ ) in MMAB with AAs . The relative fitness of this comparison was determined as described above . To determine to which extent the growth of derived auxotrophs depended on the availability of AAs in the environment and/ or the presence of a cocultured prototroph , six auxotrophic genotypes that have been isolated after 2 , 000 generations of evolution in the AA regime or two isolated after 500 and 1 , 500 generations from the non-AA regime were precultured in AA-supplemented MMAB medium . The evolutionary ancestor or the corresponding prototrophs , which have been isolated from the same population and the same time points as the auxotroph , were similarly precultured in MMAB medium with or without AA-supplementation , depending on the medium of the main experiment . ~105 CFUs of the focal auxotrophic mutant were inoculated into 1 ml MMAB with or without AAs . The same experimental set-up was repeated three times: ( i ) with the auxotrophs grown in monoculture , or by co-inoculating ~105 CFUs per ml of ( ii ) a coevolved prototrophic strain , or ( iii ) the evolutionary ancestor . Each experimental treatment was replicated 4-times . All of these cultures were incubated for 24 h . The number of CFUs ml-1 was determined for each strain at the start ( 0 h ) and the end of the experiment ( 24 h ) . Since the derived auxotrophic and prototrophic types in each coculture carried the same Ara marker ( because they descend from the same ancestor ) , both types were distinguished by plating on an unsupplemented MMAB plate and LB plates . In this way , the number of CFUs ml-1 of prototrophic or ancestral strains ( i . e . CFUs on the MMAB plate ) and CFUs ml-1 of the auxotroph ( i . e . CFUs on the LB plate minus CFUs on the MMAB plate ) were determined and the Malthusian parameter of the auxotrophic strain calculated as described [62] . To determine the frequency-dependence of the interaction between the evolving auxotrophic and prototrophic genotypes , cultures of auxotrophs and the corresponding prototrophs that have been isolated after 2 , 000 generations of evolution in the AA regime ( n = 6 ) or after 500 ( n = 1 ) and 1 , 500 generations ( n = 2 ) of growth in the non-AA regime were precultured . Subsequently , cocultures between both partners were inoculated together at different initial frequencies ( i . e . 1:100 or 100:1 ) , such that the total initial cell density of the coculture was 105 CFUs ml-1 . Each experimental treatment was replicated 4-times and cocultures were propagated for 75 generations . This was done by transferring 1 μl of culture medium to 999 μl of fresh medium ( 1:1 , 000 dilution ) every 24 h , resulting in a transfer of ~103 CFUs ( i . e . 15 bacterial generations ) per day , akin to the conditions of the evolution experiment . The prototrophic or ancestral strains that derived from the same population were plated and distinguished as described above at the onset and after every transfer cycle . The number of CFUs ml-1 was determined for both types and the selection coefficients of the invading ( rare ) type at the end of every transfer cycle was calculated as described [66] . To understand the genomic basis of the observed phenotypic auxotrophies in the evolving populations , representative auxotrophic and prototrophic strains from both selection regimes were selected for genome sequencing . From the AA-supplemented environment , six auxotrophs and four coevolved prototrophs were selected from generation 2 , 000 . In addition , two auxotrophs from generation 1 , 500 and two coevolved prototrophic isolates from the unsupplemented regime were also selected for sequencing . Genomic DNA was extracted after these strains have been grown in LB medium for 24 h using the Epicentre MasterPure DNA extraction kit ( Biozym Scientific , Germany ) . Quality control and library preparation ( TruSeq , Illumina ) was performed by the Max Planck Genome Centre Cologne , Germany ( http://mpgc . mpipz . mpg . de/home/ ) and sequencing was performed on the Ilumina HiSeq2500 platform . The resulting raw Illumina sequences were aligned to the published reference genome of Escherichia coli BW25113 ( CP009273_1 ) [67] using the breseq pipeline and mutations were thus identified [68 , 69] . The effect of single nucleotide polymorphisms ( SNPs ) on gene function was determined using the PROVEAN algorithm [35] , which predicts whether a given SNP has a neutral or deleterious effect on the protein activity of the gene product it encodes . Whole gene deletions were by default classified as being deleterious for gene function . The 13 mutations that were unique to 6 evolved auxotrophs ( 2-AA-AT , 3-AA-AT , 5-AA-AT , 6-AA-AT , 8-AA-AT , and 8-NA-AT; see S3 Table ) were individually moved into the E . coli BW25113 strain using the Scarless Cas9 Assisted Recombineering ( no-SCAR ) -system described previously [70] . The plasmids pCas9cr4 ( accession number: 62655 ) and pKDsgRNA-ack ( accession number 62654 ) for the protocol were obtained from Addgene . Briefly , the WT BW 25113 was sequentially transformed with the pCas9cr4 plasmid and then with the plasmid containing the guide RNA ( sgRNA ) pKDsgRNA-X , where X is specific sequence of the sgRNA targeting the genomic region to be mutated . Protospacer targeting sequences of the sgRNA ( 20 bp ) were designed using the CRISPR-ERA online tool ( http://crispr-era . stanford . edu/ ) . Strains bearing the pCas9cr4 and pKD-sgRNA-X plasmids were then transformed with oligonucleotides containing the mutation of interest ( obtained from Integrated DNA Technologies , Germany ) and recombination initiated by inducing λ-Red recombinase and Cas9 . Transformants were cured of the cloning plasmids by incubating the colonies at 37°C . Subsequently , the focal genomic region of the resulting strains was amplified by PCR and Sanger-sequenced to verify whether they carried the mutation of interest . Normal distribution of data was assessed using the Kolmogorov-Smirnov test . Homogeneity of variances was determined by applying Levene’s test and variances were considered to be homogeneous when P>0 . 05 . Independent sample t-tests were employed to compare fitness and growth rates of populations evolved under the two regimes , fitness of auxotrophic or prototrophic strains relative to the evolutionary ancestor , and optical density of the reconstructed mutants relative to uninoculated medium . Paired samples t-tests were used to compare growth rates of AA-evolved populations in the presence or absence of amino acids . The cell density of replicate populations over evolutionary time was fitted by exponential fits and the slopes of the fitted lines were compared by employing independent sample t-tests [71–73] . One-way ANOVAs followed by LSD post-hoc tests were used to statistically compare Malthusian parameters in the metabolic dependence experiments as well as the competitive fitness of reconstructed mutants and evolved auxotrophs . The statistical relationship between initial frequencies of the two genotypes and the selection coefficients of the initially rare genotype was tested using linear regression analysis . One sample t-tests determined if selection coefficients of the auxotrophic genotypes were significantly different from 0 . P-values were corrected for multiple testing by applying the false discovery rate ( FDR ) procedure of Benjamini et al . [74] . | Bacteria frequently lose seemingly essential genes from their genomes that are required to autonomously biosynthesize building block metabolites such as amino acids . It is generally unclear whether these losses are due to chance events in small populations or favored by selection , because loss-of-function mutants may save production cost when utilizing metabolites from the environment . We discovered that populations of Escherichia coli that evolved in amino acid-replete environments rapidly lost the ability to autonomously produce several amino acids , which was beneficial when amino acids were present in the environment . Interestingly , these mutants derived amino acids not just from the growth medium , but also from other , co-occurring strains . Our findings show that nutrient-containing environments drive the loss of biosynthetic genes from bacterial genomes and facilitate the establishment of metabolic cross-feeding interactions among bacteria . | [
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"genetics... | 2016 | Experimental Evolution of Metabolic Dependency in Bacteria |
Quinacrine is a potent antiprion compound in cell culture models of prion disease but has failed to show efficacy in animal bioassays and human clinical trials . Previous studies demonstrated that quinacrine inefficiently penetrates the blood-brain barrier ( BBB ) , which could contribute to its lack of efficacy in vivo . As quinacrine is known to be a substrate for P-glycoprotein multi-drug resistance ( MDR ) transporters , we circumvented its poor BBB permeability by utilizing MDR0/0 mice that are deficient in mdr1a and mdr1b genes . Mice treated with 40 mg/kg/day of quinacrine accumulated up to 100 µM of quinacrine in their brains without acute toxicity . PrPSc levels in the brains of prion-inoculated MDR0/0 mice diminished upon the initiation of quinacrine treatment . However , this reduction was transient and PrPSc levels recovered despite the continuous administration of quinacrine . Treatment with quinacrine did not prolong the survival times of prion-inoculated , wild-type or MDR0/0 mice compared to untreated mice . A similar phenomenon was observed in cultured differentiated prion-infected neuroblastoma cells: PrPSc levels initially decreased after quinacrine treatment then rapidly recovered after 3 d of continuous treatment . Biochemical characterization of PrPSc that persisted in the brains of quinacrine-treated mice had a lower conformational stability and different immunoaffinities compared to that found in the brains of untreated controls . These physical properties were not maintained upon passage in MDR0/0 mice . From these data , we propose that quinacrine eliminates a specific subset of PrPSc conformers , resulting in the survival of drug-resistant prion conformations . Transient accumulation of this drug-resistant prion population provides a possible explanation for the lack of in vivo efficacy of quinacrine and other antiprion drugs .
Prion diseases are a class of rare , neurodegenerative disorders that include Creutzfeldt-Jakob disease ( CJD ) and kuru in humans , BSE in cattle , and scrapie in sheep [1] . They are uniformly fatal and follow a rapid clinical course after an extended asymptomatic incubation period . Prion diseases are caused by an unconventional transmissible pathogen called a prion that is devoid of nucleic acids [2] . Prions are composed entirely of an abnormally folded conformer of the prion protein and replicate by catalyzing the conversion of the endogenous , cellular prion protein ( PrPC ) into an aberrantly folded conformation ( PrPSc ) [1] , [3] , [4] , [5] . Although PrPSc accumulation has been demonstrated to be the primary pathogenic event in prion disease , the exact molecular and cellular mechanisms of its formation and the ensuing neurodegeneration remain largely unknown . To date , no therapeutic agent against prion diseases has been identified . Numerous compounds have been found to have antiprion activity in cell culture models of prion disease , including pentosan polysulfate , dextran sulfate , HPA-23 , Congo red , suramin , dendritic polyamines and quinacrine , among others; for a comprehensive review , see [6] . However , none of these compounds have been shown to be broadly effective against a range of prion strains in animal models when administered at a post-symptomatic clinical stage , and none have been shown to prolong the disease course in human clinical trials . Among the antiprion compounds , quinacrine seemed to be the most promising for immediate application in the treatment of prion disease because it has been used for decades as an antimalarial drug [7] . In two independent studies , incubation of persistently prion-infected neuroblastoma cells with quinacrine induced the clearance of protease-resistant PrPSc [8] , [9] . A subsequent study using bis-acridine compounds , which are comprised of two acridine ring scaffolds connected by a linker , demonstrated improved potency [10] . Despite its efficacy in cell-culture models , quinacrine administration in vivo has not appeared to be promising . Collins and colleagues reported that the incubation time of mice intracerebrally inoculated with prions and subsequently treated with quinacrine via gavage feeding did not differ from that of untreated mice [11] . In a second study , intraperitoneally administered quinacrine failed to extend the survival time of prion-infected mice [12] . In human clinical studies , quinacrine has had mixed success . In some patients , treatment with quinacrine transiently altered the clinical course of disease , whereas in others it failed to improve the clinical outcome [13] ( Geschwind et al . , unpublished results ) . In wild-type mice , quinacrine inefficiently penetrates the blood-brain barrier ( BBB ) . After an oral dose of 40 mg/kg/day , quinacrine accumulates in the brain at concentrations of less than 1 µM ( [14]; Ahn et al . , manuscript in preparation; this study ) . The quinacrine concentrations needed for half-maximal depletion of PrPSc ( EC50 ) in cell culture were reported to be ∼1 µM and ∼7 µM for extracellular and intracellular accumulation , respectively [9] , [15] . Thus , the failure of quinacrine in vivo seemed likely to result from its insufficient accumulation in the brain . The finding that the P-glycoprotein transporter is involved in the efflux of quinacrine across the BBB [14] , [16] opened another avenue of investigation . Pgp is encoded by two genes in humans ( MDR1[ABCB1] and MDR2[ABCB4] ) and three genes in rodents ( mdr1a , mdr1b and mdr2 ) [17] . Pgp is a member of a large family of ATP-binding cassette ( ABC ) transporters , 12 of which have been implicated in some form of drug resistance [17] , [18] . Pgp encoded by MDR1 in humans , and mdr1a and mdr1b in mice , confers the vast majority of observed multidrug resistance . Hereafter in this paper , “Pgp” will refer to the proteins encoded by this class of MDR genes ( there is no current evidence that mdr2 is involved in quinacrine efflux ) . Given that quinacrine is a substrate for Pgp , we tested whether deletion of the mdr genes ( knockout MDR1a/1b ( −/− , −/− ) , denoted as MDR0/0 ) would improve the pharmacological profile of quinacrine and enhance its in vivo efficacy . Here , we report that quinacrine can accumulate in the brains of orally treated MDR0/0 mice at concentrations exceeding 100 µM . Despite attaining these high concentrations in the brain , quinacrine failed to extend the survival times of prion-inoculated MDR0/0 mice . The results suggest that the failure of quinacrine in vivo cannot be attributed solely to its pharmacokinetic properties . Quinacrine treatment transiently reduced PrPSc levels in the brains of mice , but PrPSc levels recovered during the course of treatment . Parallel results in cell culture suggest that continuous quinacrine treatment leads to the selective survival of drug-resistant prion conformations . However , these resistant conformations do not stably propagate in the absence of quinacrine .
We evaluated whether quinacrine treatment would extend the survival time of prion-infected mice of three genetic backgrounds: CD1 ( wild-type outbred ) , FVB ( wild-type inbred ) and MDR0/0 . Mice were inoculated intracerebrally with 30 µL of 1% brain homogenate containing the Rocky Mountain Laboratory ( RML ) , rodent-passaged , scrapie prion strain . An oral regimen of 40 mg/kg/day of quinacrine was initiated at selected time-points [0 , 30 , 60 , 70 , 80 , 95 , and 105 d postinoculation ( dpi ) ] and continued for different time intervals ( 10 , 20 , 30 , 50 , 60 d , or remaining lifetime of the mouse ) . The mice were monitored for neurological dysfunction and sacrificed upon the onset of prion disease . At least 9 mice were allowed to reach the endpoint of disease for each arm of the experiment and used to determine the mean incubation period ( Table 1 ) . For some arms , several mice were sacrificed during the course of the experiment in order to measure quinacrine uptake , PrPSc levels , and neuropathologic changes ( see below ) . Treated mice will be referred to as S[a-b] or S[a>] , for which “S” is the mouse strain , “a” indicates the initiation of quinacrine in dpi , “b” indicates the cessation of treatment in dpi , and “>” indicates lifelong treatment . We did not observe obvious signs of acute toxicity resulting from quinacrine treatment in any of the experimental arms . Quinacrine treatment did not substantially alter the progression of disease ( Table 1 ) . Short-term quinacrine treatment ( ≤30 d ) resulted in statistically significant increases in the incubation periods ( P<0 . 01 ) for five experimental arms ( Table 1 ) . However , the magnitudes of these effects were small and all mice developed neurologic dysfunction . Together , our data indicate that quinacrine is largely inefficacious in all prion-inoculated mice observed . We assessed the accumulation of quinacrine in the brains , kidneys , livers and spleens of FVB[60–90] and MDR0/0[60–90] mice at 75 and 90 dpi by quantitative LC/MS/MS and comparison to known standards ( Fig . 1 ) . Three mice were analyzed for each measurement . At 75 dpi , the concentration of quinacrine in the brains of FVB and MDR0/0 mice were ∼1 µM and ∼100 µM , respectively . Quinacrine concentrations were also greater in all other tissues of MDR0/0 mice compared to FVB mice . Similar trends were observed in tissues obtained at 90 dpi . In treated MDR0/0 mice , quinacrine was distributed throughout the brain and was readily detected in the hippocampus , cortex , thalamus , cerebellum , and brainstem ( Ahn et al . , manuscript in preparation ) . In parallel , we examined neuropathologic changes in prion-infected MDR0/0[60–90] mice and untreated controls at 75 and 90 dpi ( Fig . 2 ) . The brains of infected MDR0/0 mice showed mild , focal astrocytic gliosis in the thalamus and hippocampus before quinacrine was administered , which was also observed with 15 d of quinacrine treatment ( 75 dpi ) . In comparison , the brains of untreated mice at 75 dpi showed moderate to severe astrocytic gliosis in the thalamus and hippocampus , and mild astrocytic gliosis in the cortex . At 90 dpi , astrocytic gliosis in the brains of treated and untreated mice showed subtle differences . At the endpoint of disease , astrocytic gliosis and spongiform degeneration were similar for both treated and untreated MDR0/0 mice . Together , these data suggest that quinacrine has a slight effect on neuropathologic changes upon initial administration , but does not halt widespread neurodegeneration during the course of disease . We analyzed the kinetics of PrPSc accumulation during the incubation period of MDR0/0[60–90] , MDR0/0[0>] , and untreated mice . Two or three animals were sacrificed at 60 , 75 , 90 and 111 dpi , and the accumulation of protease-resistant PrPSc in the brain was analyzed by Western immunoblotting ( Fig . 3A ) and quantitative ELISA ( Fig . 3B ) following proteinase K ( PK ) digestion . Additionally , PrPSc levels were measured in MDR[60–90] mice at 7 other time-points ( 64 , 69 , 75 , 78 , 81 , 84 , and 88 dpi ) . At 60 dpi , MDR0/0[0>] mice had lower PrPSc levels compared to untreated controls . At 75 dpi , PrPSc levels were lower in both MDR0/0[0>] and MDR0/0[60–90] mice compared to untreated controls . However , this difference was not evident at later time-points even with continuous quinacrine administration ( MDR[0>] mice ) . In the absence of quinacrine treatment , PrPSc was readily detectable at 60 dpi . With quinacrine treatment , PrPSc signals were faint but detectable beginning at 75 dpi , a delay of 15 d compared with no treatment . In MDR0/0[60–90] mice , PrPSc was abundant before treatment was initiated ( 60 dpi ) , but barely detectable at 69 dpi , indicating that 9 d of quinacrine treatment reduced PrPSc in the brain . These data indicate that quinacrine can both delay the formation and induce the clearance of PrPSc . However , in both cases , PrPSc eventually accumulated in the brains of quinacrine-treated animals until they showed neurologic signs of illness . The transient reduction of PrPSc was not observed in FVB[60–90] mice ( data not shown ) , indicating that accumulation of quinacrine to high levels in the brain is required for prion reduction . While quinacrine did not halt the accumulation of PrPSc in the brains of infected mice , we asked if whether quinacrine treatment altered the biochemical characteristics of PrPSc . We prepared 10% brain homogenates of inoculated , untreated MDR and chronically treated MDR[0>] mice at the endpoint of disease . We exposed the samples to increasing concentrations of guanidinium hydrochloride ( GdnHCl ) for 1 h followed by PK digestion , then quantified the levels of protease-resistant PrPSc by ELISA ( Table 2 ) . As an additional control , we also analyzed untreated brains spiked with 100 µM of quinacrine . PrPSc in the brains of quinacrine-treated mice was slightly less stable than in the brains of untreated controls . With 1 . 3 M GdnHCl , a significantly greater fraction of quinacrine-exposed PrPSc was denatured compared to quinacrine-naive PrPSc ( n = 3; P<0 . 01 ) . We also analyzed PrPSc in these brain homogenates using another method , the conformation-dependent immunoassay ( CDI ) [19] . This assay measures the differential ability of PrPSc conformations to bind anti-PrP antibodies by measuring the bound ratio between native and denatured samples following partial purification by phosphotungstate ( PTA ) . The CDI measurement for PrPSc purified from quinacrine-treated brains was substantially different from untreatedbrains spiked with quinacrine ( n = 3; P<0 . 01 , Table 2 ) . Together , these findings suggest that quinacrine exposure altered the physical properties of PrPSc accumulating in MDR[0>] mice . We next determined whether the physically altered , quinacrine-treated PrPSc could cause disease upon transmission to MDR0/0 mice . Brain homogenates from infected , untreated MDR0/0 and chronically treated MDR[0>] mice at the endpoint of disease were inoculated into MDR0/0 mice . The mice were monitored for neurological dysfunction and sacrificed upon the onset of prion disease . The mean incubation periods for MDR0/0 mice infected with untreated and quinacrine-treated inocula were 112±3 and 116±1 days , respectively ( n = 8 ) . Analyses of PrPSc in 10% brain homogenates of these ill mice , using GdnHCl denaturation and CDI as described above , showed no significant differences in the stabilities and CDI measurements ( Table 3 ) , suggesting that the physically altered , quinacrine-induced PrPSc is not a stably propagating strain . We next asked if quinacrine-resistant PrPSc could be formed in cell culture . The re-appearance of drug-resistant PrPSc upon continuous addition of quinacrine to prion-infected N2a cell lines ( ScN2a ) has not been previously observed . Past experiments were conducted in continuously dividing cultures . Here , we examined the kinetics of PrPSc clearance in infected cells after differentiation by exposure to sodium butyrate or dibutyryl cAMP [20] , [21] ( Fig . 4 ) to exclude the effect of cell division on prion levels [22] . PrP-overexpressing ScN2a-cl3 cells were plated at 70% confluency in the presence of 10 mM sodium butyrate or 5 mM dibutyryl cAMP . The cultures were either left untreated ( -Qa ) , or exposed to 1 µM quinacrine beginning at day 0 ( Qa[0>] ) or day 4 ( Qa[4>] ) . In a control experiment , cells were cultured in the absence of differentiating agents and treated with 1 µM quinacrine beginning at day 0 . After each day , cells were lysed and the relative amount of PK-resistant PrPSc was measured by ELISA . In dividing ScN2a-cl3 cultures , quinacrine rapidly cleared PrPSc and suppressed its accumulation for at least 7 d ( Fig . 4A ) . In sodium butyrate–treated cultures , PrPSc levels were transiently reduced by exposure to quinacrine , but increased after 2 d of treatment ( Fig . 4C ) . The observed PrPSc re-accumulation in ScN2a-cl3 cells is similar to that observed in treated MDR0/0 mice and suggests that PrPSc becomes resistant to the effects of quinacrine upon continuous treatment . Using similar treatment , drug resistance does not develop for an alternative antiprion compound , the polyamidoamine ( PAMAM ) G4 [23] ( Fig . 4A , C ) . Quinacrine proved similarly ineffective when infected cells were division-arrested by dibutyryl cAMP ( Fig . 4F ) . This effect of dibutyryl cAMP is not dependent on activation of the PKA pathway , as the addition of forskolin does not result in a rebound in PrPSc levels ( Fig . 4A ) . In differentiated cells exposed to sodium butyrate for 4 d prior to quinacrine administration , PrPSc levels only decreased after exposure to quinacrine , indicating that observed changes resulted from quinacrine and not from sodium butyrate exposure ( Fig . 4D ) . PrPSc slowly increased in untreated , differentiated cells to levels similar to an untreated confluent culture ( Fig . 4B , 4E ) . Unlike the in vivo experiments , the PrPSc that accumulated in division-arrested cells after the addition of quinacrine did not have a significantly altered conformational stability ( data not shown ) . Thus , PrPSc can become quinacrine-resistant without a change in conformational stability as detected by melting of the protein structure with GdnHCl . Upon addition of sodium butyrate and dibutyryl cAMP , N2a cells became differentiated and could not be further passaged . Instead , in an attempt to propagate quinacrine-treated PrPSc , we tried to infect N2a cells with lysates from treated cells ( data not shown ) but were unable to achieve infection . However , lysates from control untreated , infected cells were able to infect N2a cells . We also were unsuccessful at infecting N2a cells with homogenate from the brains of quinacrine-treated mice ( data not shown ) . The difference in infectivity between treated and untreated samples might indicate a conformational change in PrPSc induced by quinacrine . However , given that infection of cell lines with prions is a stochastic phenomenon , we cannot rule out the possibility that the difference in infectivity was caused by chance .
Quinacrine is a potent antiprion drug in culture but has failed to slow the course of disease in CJD patients and experimental mouse models of prion disease . It is unclear whether this translational gap is due primarily to quinacrine's pharmacokinetic or pharmacodynamic properties in vivo . As a minimum requirement for in vivo efficacy , quinacrine must accumulate in the brain at concentrations that exceed its in vitro effective concentration . In chronically treated wild-type mice , the highest nontoxic tolerable dose of quinacrine is 40 mg/kg/day ( Ahn et al . , manuscript in preparation ) . At this dose , quinacrine accumulates in the brains of FVB mice at a concentration of ∼1 µM . In continuously dividing , ScN2a cells , the EC50 of quinacrine is also ∼1 µM [9] . However , this represents the nominal effective concentration of quinacrine added to the culture media . When N2a cells are exposed to quinacrine , the intracellular concentration of quinacrine is typically 30 to 50 times higher than its extracellular concentration [15] ( Ghaemmaghami et al . , unpublished results ) . Thus , the actual EC50 of quinacrine at its site of action may be much greater than its reported EC50 value of 1 µM . We therefore reasoned that the efficacy of quinacrine in vivo might be improved by increasing its steady-state accumulation in the brain to levels approaching its intracellular effective concentration . Because the efflux of quinacrine from the brain is governed by Pgp , an mdr-associated protein [14] , [16] , active transport can be inhibited by the deletion of the mdr1a and mdr1b genes ( MDR0/0 ) or the co-administration of Pgp inhibitors [18] . Here , we orally administered quinacrine to MDR0/0 mice and achieved drug levels in the brain that were nearly two orders of magnitude higher than those in FVB mice . This level of accumulation far exceeds the intracellular EC50 of quinacrine in culture . Despite its excess accumulation , quinacrine again failed to extend the survival time of prion-inoculated mice . The results indicate that quinacrine's lack of efficacy in vivo is not solely due to its pharmacokinetic properties and may represent a pharmacodynamic failure . The data are consistent with a previous study reporting that continuous intraventricular administration into the cerebral ventricle does not improve the efficacy of quinacrine [24] . A recent study reported a decrease in the expression of cerebovascular Pgp in a CJD patient , suggesting a possible role for Pgp in prion pathogenesis [25] . However , we did not observe a significant difference between the incubation periods of inoculated , untreated MDR0/0 and wild-type mice . These results suggest that Pgp alone does not significantly influence the accumulation of PrPSc . Kinetic analysis of PrPSc levels following drug administration provided insight into the basis of quinacrine's failure in vivo . In the brains of prion-inoculated MDR0/0 mice , PrPSc levels diminished upon the initiation of quinacrine treatment , remained low for several days , but gradually increased despite the continuous presence of the drug . There are two potential explanations for this trend: ( 1 ) target tissues , such as the brain , alter their responsiveness to quinacrine or ( 2 ) prions become resistant to the actions of the drug . There is precedence for the former phenomenon . It has been shown that during chronic administration , various drugs and steroid hormones lose efficacy by inducing the expression of efflux transformers and cytochrome P450 [26] , [27] , [28] . However , we did not observe a measurable decrease in brain quinacrine levels during the course of treatment ( Fig . 1; Ahn et al . , manuscript in preparation ) . Therefore , we propose that chronic quinacrine treatment resulted in the formation of drug-resistant PrPSc conformations that survived the initial treatment . A similar linkage between limited efficacy and the formation of drug-resistant prions was cited for amyloidophilic compounds [29] . The following two observations are consistent with this hypothesis . First , we observed that PrPSc accumulating in the brains of chronically treated mice had a lower conformational stability compared to that of untreated controls , suggesting that quinacrine induced a structural change within the prion population . Second , we were able to induce the formation of quinacrine-resistant prions in cell culture , indicating that drug resistance can be induced outside the context of the central nervous system . Interestingly , the formation of quinacrine-resistant prions is only observed in division-arrested cultured cells . The steady-state concentration of PrPSc in a cell is established by the balance between its rates of formation and clearance . As has been noted before [22] , [30] , the apparent clearance rate for PrPSc in a dividing cell is the sum of the rate of catabolism and the rate of cell division . Thus , the apparent rate of clearance for a given prion conformation is likely to be slower in stationary cells compared to dividing cells . In a dividing cell , the process of cell division artificially enhances the rate of clearance and prevents the accumulation of PrPSc . Therefore , the probability that a partially resistant conformation survives quinacrine treatment is increased in stationary cells . These observations suggest that conducting drug screens in stationary cells may be more likely to identify antiprion compounds that prove effective in vivo . Increasing evidence shows that prion-infected tissues harbor multiple , distinct PrPSc conformations [31] , [32] , [33] , [34] , [35] , [36] . Thus , in an infected brain , each prion strain may have a different degree of susceptibility to the actions of quinacrine . In this context , the administration of quinacrine could provide selective pressure for the selection of drug-resistant conformations . Indeed , the PrPSc rebound observed in this study is reminiscent of the failure of antiviral drugs caused by selection of drug-resistant variants . However , unlike resistant viral strains , the quinacrine-resistant conformation formed here was not able to propagate in the absence of the drug and thus cannot be considered a stably propagating strain . In the absence of quinacrine , the initial physical properties of the prion population are rapidly re-established . In viral therapy , the administration of drug cocktails , combining compounds with differing modes of action , reduces the probability of virologic failure . Ultimately , the co-administration of multiple antiprion compounds may be required in order to avoid the formation of resistant prion conformations . Additionally , targeting PrPC , the endogenous substrate shared by all prion strains , is likely to identify compounds that have broad specificity and are uniformly effective against all strains .
Quinacrine dihydrochloride , PAMAM G4 , forskolin and sodium butyrate were purchased from Sigma-Aldrich ( St . Louis , MO ) . N2a cells were obtained from American Tissue Culture Collection . Minimal essential medium ( MEM ) with Earle's salts; Dulbecco's Modified Eagle Medium ( DMEM ) High Glucose 1× with 4 . 5 g/L D-glucose and L-glutamine and without sodium pyruvate; cell dissociation buffer; fetal bovine serum ( FBS ) ; Geneticin ( 50 mg/mL ) ; penicillin-streptomycin ( 10 , 000 units/mL and 10 , 000 µg/mL , respectively ) ; and GlutaMAX were purchased from Gibco . Dithiothreitol ( DTT; 0 . 5 M 10× ) ; 4× loading buffer; and proteinase K ( PK ) were purchased from Invitrogen . Complete protease inhibitor ( PI ) cocktail tablets were from Roche Diagnostics . Western blotting detection reagents 1 and 2 were from GE Healthcare . Anti-PrP antibodies Fab D18 [37] and Fab D13 conjugated to horseradish peroxidase ( HRP ) [38] were prepared as previously described . All protocols were approved by the University of California San Francisco Animal Care and Use Committee . Approximately five-week-old male and female wild-type ( FVB and CD1 ) and MDR0/0 mice were purchased from Charles River ( Wilmington , MA ) and Taconic ( Germantown , NY ) , respectively . Mice were inoculated intracerebrally with 30 µL of 1% brain homogenate containing the RML prion strain . Treatment consisted of 40 mg/kg/day of quinacrine in a chocolate-flavored liquid diet [39] administered beginning at 0 , 30 , and 60 dpi for MDR0/0 mice; 60 dpi for FVB mice; and 70 , 80 , 95 , and 105 dpi for CD1 mice . The initiation of quinacrine treatment varied for each mouse strain to reflect the relative incubation period of the respective mouse line . At 75 and 90 dpi , 2–3 mice were euthanized . The left half-brain was snap frozen on powdered dry ice for quinacrine extraction and biochemical analysis; the right half-brain was fixed for pathological analysis . Brain homogenates ( 10% w/v ) were prepared using a Precellys 24 homogenizer ( Bertin Technologies , Montigny-le-Bretonneux , France ) in pure distilled water ( Invitrogen , San Diego , CA ) and were aliquoted and stored at −80°C . Animals were observed every day for signs of neurologic disease . Mice were diagnosed with prion disease when they exhibited three or more of the following neurological symptoms: ataxia , circling , depression , and blank stare . Upon diagnosis , mice were killed and their brains collected as described above for analysis . Quinacrine was extracted from brain samples and its concentration was measured by LC/MS/MS as described previously [14] ( Ahn et al . , manuscript in preparation ) . Briefly , working solution containing 10 µg/mL of quinacrine was diluted with 10% tissue homogenates from untreated , control animals to make 500 µl of 1 µg/mL of standard 1 solution for each tissue type . The standard 1 solution was then serially diluted two-fold . Nine standard solutions ( 200 µl each ) and a blank solution ( 0 µg/mL ) were frozen at −80°C . A total of 400 µl of acetonitrile containing the internal standard ( 50 ng/mL of chlorophenamine ) was added to 200 µl of 10% tissue homogenates or standard solutions . The samples were vortexed vigorously twice for 1 min and centrifuged at 16 , 000 g for 5 min . LC/MS/MS system consisted of Shimadzu LC-10 AD pumps , a Waters Intelligent Sample Processor 717 Plus autosampler , and a Micromass Quattro LC Ultima triple quadruple tandem mass spectrometer . The mass spectrometer was set to electrospray ionization in the positive-ion mode . Quinacrine and its metabolites , O-demethylated quinacrine ( M1 ) and mono-desethyl quinacrine ( M2 ) , were monitored by multiple-reaction monitoring ( MRM ) at 400 . 5>142 . 2 m/z for QA , 384 . 5>142 . 2 m/z for M1 , 372 . 2>114 . 2 m/z for M2 , and 277 . 2>142 . 2 m/z for internal standard ( chlorphenamine ) . The column was a Betasil C18 column ( 50×4 . 6 mm ) from Hypersil-Keystone and the mobile phase consisted of CH3OH/H2O/trifluoroacetic acid ( 45∶55∶0 . 05 ) with 1 mM ammonium formate . The flow rate was 0 . 8 mL/min . Ten percent brain homogenates ( 100 µL ) were diluted with 1 mL lysis buffer and digested with 20 µl of 1 mg/mL PK at 37°C for 1 h . PMSF ( final concentration of 1 mM ) was added to stop the digestion . Sample volumes of 200 µl were used for ELISA analysis and of 800 µl for Western blot analysis . ELISA plates were prepared as described previously [22] . Triplicate samples ( 200 µL each ) were transferred to a 96-well PCR plate and 50 µl of 6 M GdnHCl ( Pierce , Rockford , IL ) was added to each sample . Samples were heated to 85°C for 15 min and 5 µl of samples were added onto an ELISA plate containing 245 µl of 1% BSA per well . Samples were incubated at 4°C overnight . The next day , ELISA plates were washed 3 times with TBST buffer ( 10 mM Tris/HCl , pH 8 . 0; 150 mM NaCl; 0 . 5% Tween-20 ) . After washes , 100 µl of D13-HRP antibody in 1% BSA ( 1∶1000 dilution ) was added to each well and plates were incubated at 37°C for 1 h . Subsequently , plates were washed 7 times with TBST and 100 µl of 2 , 2′-azino-bis ( 3-ethylbenzthiazoline-6-sulfonic acid ( ABTS ) was added to each well . After developing for 15 to 20 min , plates were read using a SpectraMax Plus microplate reader running SoftMaxPro ( Molecular Devices , Sunnyvale , CA ) . In each plate , standard ladders of recombinant mouse PrP and uninoculated brain samples were placed as controls . For Western blot analysis , PrPSc pellets from 800 µl of PK-digested samples were collected and resuspended with lysis buffer . SDS sample running buffer and reducing reagent ( Invitrogen ) were added . Samples were heated to 95°C for 5 min and run in a 4–12% Tris-glycine SDS gel ( Invitrogen ) . The gel was transferred to PVDF membrane using an iBlot ( Invitrogen ) and the membrane was blocked with 5% milk for 30 min . The membranes were subsequently incubated overnight with D13-HRP antibody and washed 3 times with TBST for 5 min before developing with ECL reagent . Brain homogenates ( 10 µL ) were incubated with GdnHCl , in a range of 0 to 2 M , at 22°C for 2 h . The samples were subsequently diluted with lysis buffer to a final concentration of 0 . 4 M of GdnHCl . PK was added at a final concentration of 50 µg/mL and the samples were incubated at 37°C for 1 h . PMSF ( final concentration of 1 mM ) was added to stop PK digestion . The samples were subsequently analyzed by ELISA as described above . Each sample was divided into two aliquots , precipitated by the addition of 1% PTA and resuspended in lysis buffer . The first aliquot was untreated and designated native; the second aliquot was mixed to a final concentration of 4 M GdnHCI , heated for 20 min at 80°C , and designated denatured . Both samples were diluted 20-fold with lysis buffer containing protease inhibitors ( 5 mM PMSF; aprotinin and leupeptin , 4 g/ml each ) . Then , 200 µl volumes were loaded on 96-well polystyrene plates that were previously coated with the mAb D18 . The plates were blocked overnight at 18–22°C in 0 . 2 M phosphate buffer , pH 7 . 2 , and then blocked with TBST , pH 7 . 2 , containing 0 . 25% BSA ( w/v ) and 6% Sorbitol ( w/v ) , and 0 . 03% ( w/v ) NaN3 . The plates were washed 3× with TBS , pH 7 . 8 , containing 0 . 05% ( v/v ) of Tween 20 , then incubated at room temperature for 2 h . HRP-labeled D13 anti-PrP antibody was added and incubated as described [33] . The plates were developed by the addition of ABTS ( 2 , 2′-azino-di 3-ethylbenzthiazoline-6-sulfonate ) after seven washing steps with TBST ( Tris-buffered saline Tween-20 ) and the signals were measured . After normalization , the ratios of antibody binding to denatured versus native aliquots were calculated . After mice were euthanized , their right half-brains were fixed in 10% formalin for a minimum of 3 d . Fixed brains were processed and embedded in paraffin , and 8-µm sections were cut from four representative brain regions: cortex , cerebellum , hippocampus and thalamus . Slides were deparaffinized and endogenous peroxidases blocked with 3% H2O2 in methanol for 20 min . The slides were washed 3 times for 5 min using PBS with 0 . 1% Tween 20 ( PBST ) with 3 buffer changes . The glial fibrillary acidic protein ( GFAP ) target antigen does not require retrieval for antibody recognition ( rabbit polyclonal anti-GFAP , Dako ) . Nonspecific antibody binding was blocked with 5% normal goat serum ( NGS ) in PBST for 30 min , then the slides were incubated with the primary antibody at 1∶1000 in PBST at 4°C overnight . After washing , the sections were incubated with a biotinylated goat anti-rabbit secondary antibody ( Pierce ) at 1∶1000 in PBST with 5% NGS for 1 h at room temperature . After additional washing , the Vector ABC kit was used per the manufacturer's instructions . Sections were washed and developed with the Vector DAB kit for 3 min and washed again . Tween 20 was not present in the third wash . The sections were counterstained for 10 s in hematoxylin ( Fisher ) , taken through graded alcohols to xylene , and coverslipped using Permount ( Fisher ) . Images were taken at 20× and 40× magnifications using the SpotFlex camera and program on the Leica DM-IRB microscope . The PrP-overexpressing N2a-cl3 cell line was created by stably transfecting N2a cells with the pSPOX . neo vector expressing full-length mouse PrP under the control of the hCMV promoter with DOTAP liposomal transfection reagent ( Roche ) . Stably transfected lines were cloned by serial dilution and 15 individual clones were assayed for PrP expression level by Western immunoblotting . The N2a-cl3 clone overexpresses PrP at ∼6× the levels of untransfected N2a cells . N2a-cl3 cells were infected with RML prions as previously described [40] and subcloned to produce ScN2a-cl3 cells . ScN2a-cl3 cells were maintained at 37°C in MEM supplemented with 10% FBS and 1% GlutaMAX ( Invitrogen ) in 100-mm plates and fed with fresh media every 2 d . Approximately 70% confluent cells were plated and 1 µM quinacrine or 10 µg/mL PAMAM G4 was added in the presence of 10 mM sodium butyrate or 5 mM dibutyryl cAMP for stationary conditions . Dividing cells were plated at 10% confluency in the presence of 1 µM quinacrine , 10 µg/mL PAMAM G4 and/or 1 µM forskolin ( and absence of sodium butyrate or dibutyryl cAMP ) and split 1∶10 when they become confluent . At the end of each day for 7 d , cells were washed with PBS and harvested using cell dissociation buffer ( Invitrogen ) . Cells were lysed with lysis buffer ( 100 mM Tris/HCl , pH 8 . 0; 150 mM NaCl; 0 . 5% NP-40; 0 . 5% sodium deoxycholate ) and protein concentrations were measured using a bicinchoninic acid protein assay kit ( Fisher , Rockford , IL ) . Protein extracts were normalized to 1 mg/mL total protein with lysis buffer prior to PK digestion and ELISA analysis . | Prion diseases belong to the class of neurodegenerative disorders that include Alzheimer , Parkinson and Huntington diseases . In each of these disorders , a specific protein in the brain changes shape and accumulates , leading to neuronal loss and damage . These diseases are uniformly fatal after a period of neurodegeneration , dementia and motor dysfunction . Quinacrine , an antimalarial drug , is able to eliminate prions from dividing cells in culture , yet is ineffective in diseased mice and human patients . Here , we provide an explanation for this failure . Our data indicate that the administration of quinacrine results in the proliferation of drug-resistant prions . This insight will enable us to develop more effective antiprion therapeutics in the future . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/prion",
"diseases",
"pharmacology/drug",
"resistance",
"pathology/neuropathology",
"neurological",
"disorders/prion",
"diseases"
] | 2009 | Continuous Quinacrine Treatment Results in the Formation of Drug-Resistant Prions |
Flight speed is expected to increase with mass and wing loading among flying animals and aircraft for fundamental aerodynamic reasons . Assuming geometrical and dynamical similarity , cruising flight speed is predicted to vary as ( body mass ) 1/6 and ( wing loading ) 1/2 among bird species . To test these scaling rules and the general importance of mass and wing loading for bird flight speeds , we used tracking radar to measure flapping flight speeds of individuals or flocks of migrating birds visually identified to species as well as their altitude and winds at the altitudes where the birds were flying . Equivalent airspeeds ( airspeeds corrected to sea level air density , Ue ) of 138 species , ranging 0 . 01–10 kg in mass , were analysed in relation to biometry and phylogeny . Scaling exponents in relation to mass and wing loading were significantly smaller than predicted ( about 0 . 12 and 0 . 32 , respectively , with similar results for analyses based on species and independent phylogenetic contrasts ) . These low scaling exponents may be the result of evolutionary restrictions on bird flight-speed range , counteracting too slow flight speeds among species with low wing loading and too fast speeds among species with high wing loading . This compression of speed range is partly attained through geometric differences , with aspect ratio showing a positive relationship with body mass and wing loading , but additional factors are required to fully explain the small scaling exponent of Ue in relation to wing loading . Furthermore , mass and wing loading accounted for only a limited proportion of the variation in Ue . Phylogeny was a powerful factor , in combination with wing loading , to account for the variation in Ue . These results demonstrate that functional flight adaptations and constraints associated with different evolutionary lineages have an important influence on cruising flapping flight speed that goes beyond the general aerodynamic scaling effects of mass and wing loading .
According to fundamental aerodynamics the lift force ( L ) generated on a wing is related to flight speed ( U ) as: where ρ is air density , S is wing area , and CL is the lift coefficient [1–3] . In horizontal cruising flight L balances the weight ( m × g ) , and aircraft as well as animals are expected to fly at or near a value of CL giving the maximum efficient lift-drag ratio . Provided that this value of CL is about equal among bird species ( as required for dynamical similarity ) [1] , it follows that cruising flight speed among bird species is expected to scale with body mass and wing loading ( Q = m × g/S ) as U ∝ m1/6 and U ∝ Q1/2 , respectively ( with the former proportionality based also on the assumption of geometrical similarity; i . e . , S varies with m2/3 ) . These scaling rules have also been used to compare general speeds of a wide range of flyers , from the smallest insects to the largest aircraft [1 , 4–6] . In the absence of reliable measurements of the airspeed of different bird species in long-distance cruising ( migration ) flight , theoretically derived flight speeds for species of different mass and wing morphology have been used to explore these scaling rules [4 , 5 , 7–10] . Deviations from the expected scaling exponent in relation to mass have been found because of departures from geometrical similarity—larger birds often tend to have proportionately larger wing area and span [2 , 5 , 9–11] . There are additional possible reasons , besides departure from geometrical similarity , why bird flight speeds may deviate from the aerodynamic scaling rules . Flight adaptations related to the birds' ecology and phylogeny may have consequences for their cruising flight speeds , and different flight modes ( continuous or intermittent flapping ) may constrain the birds' speeds [2 , 10] . A full evaluation of the applicability of aerodynamic scaling rules must be based , not on theoretically derived speeds , but on empirical measurements of airspeeds of a wide variety of bird species in natural cruising flight . Here , we present tracking radar measurements of flight speeds of 138 species from six main monophyletic groups [12] , which were analysed in relation to biometry ( m , S , and wingspan b ) and evolutionary origin ( as reflected by phylogenetic group ) . All speeds reported here refer to flapping flight at cruising speeds of birds on migration . By restricting the analysis to migration flight we expect the birds to fly at an airspeed close to that associated with maximum lift-drag ratio [13] . All speeds designate equivalent airspeeds ( Ue ) corrected to sea level air density [14 , 15] .
Relationships between Ue and m and Q for all different species are plotted in Figure 1 , with the lines showing the allometric relations according to reduced major axis regressions ( Table 1 ) . Mean airspeeds among the 138 species ranged between 8 and 23 m/s . Birds of prey , songbirds , swifts , gulls , terns , and herons had flight speeds in the lower part of this range , while pigeons , some of the waders , divers , swans , geese , and ducks were fast flyers in the range 15–20 m/s . Cormorants , cranes , and skuas were among the species flying at intermediary speeds , about 15 m/s . The diving ducks reached the fastest mean speeds in our sample , with several species exceeding 20 m/s , up to 23 m/s ( Protocol S1 ) . The scaling analyses at the species level are robust against possible biases from few tracks per species and from within-species variation in speed ( see Materials and Methods and Table 1 ) . Because species do not represent an evolutionary independent data point , we also calculated scaling exponents by analysis of independent phylogenetic contrasts [16] according to the procedure and phylogeny [12] presented in Protocol S2 . We used the well-resolved molecular phylogeny by Ericson et al . [12] for our phylogenetic analyses and classifications . The scaling results corrected for phylogenetic dependence agreed very closely with the exponents calculated on the species level ( Table 1 ) , demonstrating that the scaling exponents for Ue in relation to m as well as Q ( 0 . 12 and 0 . 32 , respectively; phylogenetic contrast analysis ) were smaller than the predicted values of 0 . 17 and 0 . 50 , respectively . For the scaling of Ue versus m , the difference from the predicted value was at the significance level of 0 . 05 for the phylogenetic contrasts analysis , and the difference was not statistically significant for the sample of speeds adjusted for within-species variation ( Table 1 ) . Within the different main phylogenetic groups ( species level ) as defined in Protocol S1 ( see Figure 1 ) , the scaling exponents of Ue in relation to m were significantly smaller than the predicted value of 0 . 17 among two of the groups . Swans/geese/ducks showed a remarkable negative scaling exponent of −0 . 15 ( difference from prediction t = 13 . 40 , degrees of freedom ( df ) = 25 , and p < 0 . 0001 ) , and falcons/crows/songbirds showed a scaling exponent of 0 . 08 that was clearly smaller than expected ( t = 6 . 01 , df = 37 , and p < 0 . 0001 ) . For the other four groups , the scaling exponents ranged between 0 . 12 and 0 . 20 and were not significantly different from the predicted value ( p > 0 . 2 ) . The corresponding scaling exponents of Ue in relation to Q differed significantly from the predicted value of 0 . 5 among three of the groups , flamingo/pigeons/swifts ( exponent 0 . 28 , t = 3 . 22 , df = 5 , and p = 0 . 023 ) , divers/cormorants/pelican/herons/storks/crane ( exponent 0 . 36 , t = 2 . 59 , df = 15 , and p = 0 . 021 ) , and falcons/crows/songbirds ( exponent 0 . 28 , t = 4 . 88 , df = 37 , and p < 0 . 0001 ) . For the remaining three groups , the scaling exponents ranged between 0 . 42 and 0 . 54 and were not significantly different from the predicted value ( p > 0 . 4 ) . To determine if there were geometrical differences in wing shape associated with differences in mass and wing loading , we investigated whether or not aspect ratio scaled significantly with m and Q . Aspect ratio is a dimensionless measure of wing shape ( =b2/S ) . We found significant departures from isometry with aspect ratio scaling positively to m as well as Q ( p < 0 . 01 on the basis of all species [n = 129] and p < 0 . 05 on the basis of independent phylogenetic contrasts [n = 17] , for both scaling relationships ) . We also investigated the explanatory power of m , Q , aspect ratio , and phylogenetic group to account for the variation in Ue ( Figure 2 ) . Mass accounted for only a small fraction of the variation in flight speed while , as expected , speed was much more closely correlated with wing loading . There was a significant positive correlation between Ue and aspect ratio , but aspect ratio provided no improvement of general linear models ( based on Akaike information criterion [AIC] [17] ) when combined with Q or phylogenetic group . A most potent factor to account for the variation in Ue was phylogenetic group; species of the same group tended to fly at similar characteristic speeds . The groups including birds of prey and herons had on average slow flight speeds for their mass and wing loading , while the average speed for groups including songbirds and shorebirds fell above the overall scaling lines ( Figure 1 ) . Main phylogenetic group alone accounted for a substantial proportion of the variation in Ue ( adjusted R2 = 0 . 55 ) , and a general linear model including both Q and phylogenetic group was the most satisfactory model according to AIC ( with adjusted R2 = 0 . 64; Figure 2 ) . Our estimates of the explanation provided by the phylogenetic component , according to Figure 2 , are likely to be conservative because of the broad grouping across the entire modern bird phylogeny . If tighter monophyletic groups at the family level were used ( 20 phylogenetic groups ) , phylogenetic group accounted for a fraction as high as 0 . 68 ( adjusted R2; F19 , 118 = 16 . 4 , and p < 0 . 001 ) of the variation in Ue , and for a model including both phylogenetic group and Q this fraction increased to 0 . 71 ( adjusted R2; F20 , 108 = 16 . 4 , and p < 0 . 001 ) . However , these models had positive ΔAIC-values ( +8 . 1 and +28 . 8 , respectively ) in relation to the best model in Figure 2 and were thus less satisfactory when considering fit and complexity in combination [17] .
The scaling exponents fell below predicted values for both of the tested relationships , for Ue versus m as well as Ue versus Q . Predicted scaling exponents were based on the assumptions of geometrical and dynamical similarity . Could deviations from one or both of these assumptions explain our results ? Earlier studies have demonstrated that bird species are not , on average , geometrically identical , but larger species tend to have proportionately longer wingspans and larger aspect ratios [2 , 5 , 10] . This was confirmed for the sample in the present study with aspect ratio scaling significantly positively to m as well as Q . An overall scaling exponent of 0 . 14 for flight speed versus body mass was calculated for theoretical flight speeds after taking the slight positive allometry in wing size into account for a large sample of bird species [9] . This fits well with the corresponding exponent for observed speeds in this study , making departure from geometrical similarity a likely explanation for this result . The negative scaling exponent of Ue in relation to m for the swans , geese , and ducks may be an effect of a reduced flight power margin with increasing size restricting the largest flyers like swans to fly close to the minimum power speed rather than at the faster speed associated with maximum effective lift-drag ratio [18 , 19] . Such constrained flight speeds for the largest flyers will also have the effect of reducing the overall scaling exponents , thus providing another contributory explanation for the observed results in this study . Dynamical similarity is reflected by Reynolds number , which will differ between bird species in proportion to their size ( length dimension ) and speed [20] . Reynolds number shows a 15-fold range among the species in our sample ( ranging from approximately 25 , 000 to 375 , 000 based on mean wing chord , S/b , as length measurement ) . Such a range of Reynolds number may well be large enough to give rise to significant departures from dynamical similarity . The main expected consequence would be a reduced coefficient of frictional drag for birds with large Reynolds number ( i . e . , large and fast birds ) leading to an increased optimal cruising speed among these species [14 , 20] . Thus , such a departure from dynamical similarity is expected to show up as an augmented scaling exponent for Ue versus m ( and also for Ue versus Q ) , rather than a scaling exponent lower than expected as in this analysis . In view of the opposite effects on scaling exponents of departures from geometrical and dynamical similarity , respectively [1] , we conclude that only the departure from geometric similarity can explain why the scaling exponent for Ue versus m falls significantly below one-sixth among birds in cruising migratory flight . Do geometrical differences provide a sufficient explanation also for the fact that the scaling exponent for Ue versus Q fell clearly below the expected value of one-half ? One way to evaluate this is to calculate the scaling exponent for flight speed versus span loading ( m × g/b2 , where b is wingspan ) . Span loading is equivalent to wing loading divided by the aspect ratio , and for birds differing in their geometric wing shapes cruising flight speed is expected to scale most closely with the square root of span loading ( under geometrical similarity flight speed is predicted to scale with the same exponent of one-half versus both span loading and wing loading ) [5] . The scaling exponent for Ue versus span loading ( species level , exponent 0 . 36 with 95% confidence interval 0 . 31–0 . 40 , n = 129 and phylogenetic contrasts , exponent 0 . 37 with 95% confidence interval 0 . 26–0 . 48 , n = 17 ) exceeded that versus Q ( with corresponding exponents of 0 . 31 and 0 . 32 , respectively , Table 1 ) although still falling significantly below the predicted value of one-half . This suggests that the geometrical differences explain part , but not all , of the discrepancy between observed and expected scaling of Ue versus Q . Departure from dynamical similarity will , in its most simple form ( as reflected by differences in Reynolds number ) , contribute to an augmented rather than reduced scaling exponent in relation to that predicted and can therefore not provide any useful additional explanation in this case ( see above ) . Still , dynamical differences of other kinds may exist for reasons that are notoriously difficult to predict for flapping flight . Future studies of vortex patterns associated with flapping flight of different species will be important to demonstrate possible dynamical differences between species ( see below ) . We suggest that the unexpectedly small scaling exponent for Ue versus Q may be the result of general evolutionary forces acting to increase cruising speeds for species with the lowest wing loadings and reduce speeds for species with the highest wing loadings . The bird species in our analysis show approximately a 10-fold difference in their range of Q ( from about 15 to 150 N/m2 , Figure 1 ) . With an observed scaling exponent for flight speed of 0 . 31 , this range of Q is associated with a 2-fold ( 100 . 31 = 2 . 0 ) difference in flight speed . However , with a predicted scaling exponent of 0 . 5 we would have expected more than a 3-fold difference in cruising speed ( 100 . 5 = 3 . 2 ) . Given that birds with low Q ( about 15 N/m2 ) fly at a speed about 10 m/s ( as observed ) , species with high Q ( about 150 N/m2 ) would fly at 32 m/s according to the general aerodynamic scaling rules . This may well be impracticably fast and difficult to reconcile with flight performance in situations of start , landing , flock manoeuvres , etc . Conversely , given that birds with high Q fly at a speed about 20 m/s ( as observed ) , species with low Q would fly at only about 6 m/s according to the general aerodynamic scaling rules . Such very slow speeds will be disadvantageous because of sensitivity to wind , vulnerability to predation , etc . Hence , it seems reasonable to expect that there are evolutionary forces operating to compress the range of cruising flight speeds among bird species [5] and thus reducing the scaling exponent for Ue versus Q . This compression of the range of flight speeds is attained partly through general geometrical differences between species ( larger aspects ratios among species with larger mass and wing loading , as discussed above ) , but additional unknown mechanisms , perhaps associated with different kinematics of flight or different muscle operation between species , seem to be required to fully explain the restricted range of flight speeds among bird species . Bounding flight seems to be a mode for small birds ( mainly passerines ) to mitigate the costs of fast flight [1 , 2 , 10 , 21] , while flap-gliding , used by many raptors , is associated with a reduction in cruising flight speed [21] . Both of these styles of intermittent flight are used by species with low or intermediate Q ( Figure 1 ) , and , having opposite effects on flight speed , they are unlikely to provide a sufficient explanation for the low scaling exponent of Ue versus Q among bird species as a whole . Dimensional analyses have demonstrated that scaling relationships between wing loading and total mass differ significantly between different types of birds [5 , 10] . The expected consequence of this is that wing loading will be a more reliable predictor of flight speed , explaining more of the variation in flight speeds among bird species than body mass [1 , 5] . This expectation was fully confirmed in the present study , with Q accounting for almost half of the variation in Ue between species , while m explained only 12% of this variation ( Figure 2 ) . However , our findings that Q still left a large part of the variation in flight speed unexplained and that phylogenetic group accounted for a significant fraction of this remaining variation were unexpected from earlier analyses based on theoretically calculated flight speeds [5 , 10] . What are the causes for the discrepancies in flight speed between phylogenetic groups ? Differences in flight mode and the use of bounding flight by many passerines have been suggested as explanations for important group-specific deviations from aerodynamic predictions of optimal bird flight speeds [15] . We provisionally assigned , based on our own field experience , the different bird species to three main modes of flapping flight; ( 1 ) continuous flapping ( e . g . , shorebirds and ducks ) , ( 2 ) intermittent flapping with short gliding phases ( raptors , swifts , and swallows ) , and ( 3 ) bounding flight ( many but not all passerines use this mode of intermittent flapping with phases of wing folding ) . Ue differed significantly between flyers in these three categories ( p < 0 . 001 , adjusted R2 = 0 . 26 , and F2 , 135 = 25 . 1 ) , and the explanatory power of a model incorporating both flight mode and Q was high ( p < 0 . 001 , adjusted R2 = 0 . 60 , and F3 , 125 = 64 . 5 ) . This suggests that difference in flight mode is one element affecting the characteristic cruising flight speeds among phylogenetic groups . Depending on their ecological life style and foraging , birds are adapted to different aspects of flight performance , e . g . , speed , agility , lift generation , escape , take-off , cost of transport , and power [2 , 10] . These adaptations are likely to have implications for the flight apparatus ( anatomy , physiology , and muscle operation ) and the flight behaviour that may constrain the cruising flight speed . The variations in power-versus-speed relationships between different species [22] and in muscle efficiency ( conversion from metabolic power input to mechanical power output ) with mass and flight speed [23 , 24] may be related to such differential complex flight adaptations among birds . Constraints on flight speed may also be associated with differences in fluid dynamics and vortex patterns , hereto investigated only for a few species [25–27] . Variable airspeeds may still be associated with high power efficiency if accompanied with the proper variation in wing stroke frequency and amplitude [28 , 29] . Species flying at comparatively slow cruising speeds frequently use thermal soaring ( raptors and storks ) , are adapted for hunting and load carrying ( raptors ) , or for take-off and landing in dense vegetation ( herons ) . Associated with these flight habits they have a lower ratio of elevator ( supracoracoideus ) to depressor ( pectoralis ) flight muscle ( particularly low among birds of prey ) compared with shorebirds and anatids [2] . We suggest that functional differences in flight apparatus and musculature among birds of different life and flight styles ( differences often associated with evolutionary origin ) have a significant influence on the birds' performance and speed in sustained cruising flight . Thus , our results strongly indicate that there is a diversity of cruising flight characteristics among different types of birds over and above the general scaling effects of mass and wing loading that remains to be investigated and understood , aerodynamically [30] , kinematically [26 , 31] , physiologically [22] , as well as ecologically [2 , 10] .
Our main dataset , based on tracking radar measurements in Sweden and the Arctic 1979–1999 , consists of 1 , 399 tracks of 102 identified species , with a mean track time of 369 s ( range 20–2 , 220 s ) . Altitudes ranged from sea level to 3 , 600 m . Number of tracks for each species ranged between one and 240 , and mean Ue ( with SD ) , vertical speed as well as information about number of tracks , track time , and biometry data are given for each species in Protocol S1 . An extensive additional dataset of equivalent airspeeds of identified birds , obtained by similar tracking radar techniques , has been published from the work of Bruno Bruderer and his research group in Switzerland , Germany , Israel , and Spain [15] . Flight speed data from tracks of birds in natural migratory flight ( excluding released birds and soaring flight ) were incorporated into our analysis . This additional dataset comprised 64 species , and with 28 species shared between the two sets of data , the combined data added up to a total of 138 species ( Protocol S1 ) . Mean Ue for the shared species were not significantly different between the two sets ( paired sample t-test , t = 1 . 28 , and p = 0 . 21 ) , and we used weighted ( according to the number of tracks ) overall mean Ue for these species in our analyses . The bulk of flight speed data were measured 1979–1999 by tracking radar studies at five sites in southern Sweden and on two expeditions by icebreaker to the Arctic ( for detailed methods see [19 , 32] ) . Targets were identified to species and flock sizes through telescopes simultaneously with radar registrations providing computer readings of range , elevation , and bearing to the target usually every 10 s with the radar in automatic tracking mode . All flight speeds have been corrected for the influence of wind by subtraction of the wind vector at the altitude where the birds were flying from the ground speed vector of the birds . Winds were measured by releasing and tracking hydrogen/helium-filled balloons carrying a radar reflector . Mean airspeed , altitude , and vertical flight speed were calculated for each track , excluding segments with a convoluted flight path . Altitudes were corrected in relation to sea level by adding the altitude of the radar antenna ( 10–185 m above sea level at the different sites ) , and true airspeeds were reduced to equivalent airspeeds ( Ue ) referring to sea level air density , according to the standard atmosphere change in air density with altitude [14 , 15] . Reduced major axis regressions [16] for the scaling relationships between Ue and m and Q , respectively , were performed in Matlab , with calculations of confidence intervals by bootstrapping [33] . Calculations of reduced major axis regressions based on phylogenetic independent contrasts are further described in Protocol S2 . We checked for possible bias arising as a consequence of including species with only one or a few tracks , by restricting the calculations to species with at least five or ten tracks . The results remained the same , as exemplified for the sample of 56 species with ≥10 tracks in Table 1 . For 39 of the species with ≥10 tracks , we could account for the within-species variation of Ue in relation to vertical flight speed , head- and side-wind components , and flock size by multivariate regression ( statistically significant influences were found in 26 of these 39 species; unpublished data ) . Restricting the analysis to intercept values of Ue for these 39 species ( corrected to zero vertical speed , zero wind , and a flock size of one from the multiple regression equations of significant variables for each species ) still gave the same scaling result ( Table 1 ) . General Linear Models ( Figure 2 ) [34] were calculated with Ue as dependent variable . Logarithmic values were used for Ue , m , and Q . Phylogenetic group and flight mode ( limited analysis of this provisionally estimated variable ) were treated as fixed factors . Complex models ( different combinations or interactions of mass , aspect ratio , and phylogenetic group or of wing loading , aspect ratio , and phylogenetic group ) were presented in Figure 2 only if AIC improved from that of models with single independent variables [19] . | Analysing the variation in flight speed among bird species is important in understanding flight . We tested if the cruising speed of different migrating bird species in flapping flight scales with body mass and wing loading according to predictions from aerodynamic theory and to what extent phylogeny provides an additional explanation for variation in speed . Flight speeds were measured by tracking radar for bird species ranging in size from 0 . 01 kg ( small passerines ) to 10 kg ( swans ) . Equivalent airspeeds of 138 species ranged between 8 and 23 m/s and did not scale as steeply in relation to mass and wing loading as predicted . This suggests that there are evolutionary restrictions to the range of flight speeds that birds obtain , which counteract too slow and too fast speeds among bird species with low and high wing loading , respectively . In addition to the effects of body size and wing morphology on flight speed , we also show that phylogeny accounted for an important part of the remaining speed variation between species . Differences in flight apparatus and behaviour among species of different evolutionary origin , and with different ecology and flight styles , are likely to influence cruising flight performance in important ways . | [
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] | 2007 | Flight Speeds among Bird Species: Allometric and Phylogenetic Effects |
Ultraviolet ( UV ) light-induced mutations are unevenly distributed across skin cancer genomes , but the molecular mechanisms responsible for this heterogeneity are not fully understood . Here , we assessed how nucleosome structure impacts the positions of UV-induced mutations in human melanomas . Analysis of mutation positions from cutaneous melanomas within strongly positioned nucleosomes revealed a striking ~10 base pair ( bp ) oscillation in mutation density with peaks occurring at dinucleotides facing away from the histone octamer . Additionally , higher mutation density at the nucleosome dyad generated an overarching “translational curvature” across the 147 bp of DNA that constitutes the nucleosome core particle . This periodicity and curvature cannot be explained by sequence biases in nucleosomal DNA . Instead , our genome-wide map of UV-induced cyclobutane pyrimidine dimers ( CPDs ) indicates that CPD formation is elevated at outward facing dinucleotides , mirroring the oscillation of mutation density within nucleosome-bound DNA . Nucleotide excision repair ( NER ) activity , as measured by XR-seq , inversely correlated with the curvature of mutation density associated with the translational setting of the nucleosome . While the 10 bp periodicity of mutations is maintained across nucleosomes regardless of chromatin state , histone modifications , and transcription levels , overall mutation density and curvature across the core particle increased with lower transcription levels . Our observations suggest structural conformations of DNA promote CPD formation at specific sites within nucleosomes , and steric hindrance progressively limits lesion repair towards the nucleosome dyad . Both mechanisms create a unique extended mutation signature within strongly positioned nucleosomes across the human genome .
UV light causes the formation of cyclobutane pyrimidine dimers ( CPDs ) and , to a lesser extent , 6–4 photoproducts ( 6-4PPs ) [1] , which can induce mutations that promote the development of melanomas and other skin cancers [2] . Whole genome sequencing of melanomas has revealed that most somatic mutations in these cancers match a UV mutational signature , consisting of C -> T substitutions occurring in lesion-forming dipyrimidine sequences [3 , 4] . Due to UV-induced mutagenesis , cutaneous melanomas typically have an extremely high number of base substitutions [5] . These somatic mutations are unevenly distributed across the cancer genome [6–10] , despite little to no selective pressure occurring on the vast majority of these genetic changes . The high frequency and heterogeneous distribution of somatic mutations in cutaneous melanomas confound the ability to accurately identify “driver” mutations based on local abundance and recurrence , especially for less common driver mutations [2 , 7] . Hence , to better understand the molecular etiology of human skin cancers , it is important to elucidate the mechanisms that shape the genomic “landscape” of UV-induced mutation . Chromatin structure is also variable across the genome , regulating cellular processes like transcription , DNA repair , and replication in a cell-type specific manner . Effects of chromatin on mutagenesis have been observed on the global scale , where regions of compact chromatin correlate with elevated mutation density [7 , 9] , and on the local scale , where transcription factor ( TF ) binding [11–15] and individual nucleosomes [16] are associated with variations in mutation density . The impact of chromatin organization on mutation heterogeneity has largely been attributed to inhibition of DNA repair processes by occluding access to DNA lesions [17 , 18] . This assessment has assumed that lesion formation is homogeneous across the genome . However , lesion formation can vary within defined structures of chromatin , such as TF binding sites [15 , 19 , 20] , suggesting that DNA repair efficiency may not be the sole factor affecting mutation rates . Nucleosomes are the fundamental unit of chromatin [21 , 22] , but the potential impact of nucleosome structure on mutation rates in melanoma is not well understood . It has been shown that in the flanking DNA around transcription factor binding sites ( TFBS ) nucleosomes may generate a phasing pattern in mutation density in melanoma [18] . Moreover , in vitro and in vivo studies indicate that histone-DNA contacts within individual nucleosomes modulate the formation of UV-induced CPD lesions across the 147 bp of DNA that is bound by the nucleosome core particle [19 , 23 , 24] . CPD formation peaks every ~10 . 3 bp within nucleosomal DNA , indicating that the rotational setting of DNA along the nucleosome can affect lesion formation ( Fig 1A ) [16] . However , it is not clear to what extent nucleosome positioning in the human genome affects CPD formation , nor if this mechanism affects mutation rates in human skin cancers . Lesion removal by the nucleotide excision repair ( NER ) pathway in the yeast Saccharomyces cerevisiae also occurs more slowly towards the center of the nucleosomes where DNA is strongly bound , and more efficiently at the edge of the nucleosome where DNA is flexible [25–27] . This indicates the linear position ( translational setting ) of the DNA along the nucleosome may also play a role in dictating mutation distribution . To investigate whether individual nucleosomes modulate mutation density in human cancers , we analyzed the positions of melanoma mutations within strongly positioned nucleosomes across the human genome [28] . We show that mutation density in melanoma has a unique oscillatory pattern in strongly positioned nucleosomes , with peaks in mutation density occurring at regular ~10 bp intervals at outward rotational settings in nucleosomes . The relative contributions of lesion formation and repair in generating this pattern were assessed and revealed that lesion formation is likely responsible for the ~10 bp periodicity , while nucleotide excision repair ( NER ) activity appears to generate an overall “translational curvature” in mutation density across the nucleosome ( i . e . higher mutation density near the dyad of nucleosomes than at the edges ) . We additionally parsed nucleosomes by chromatin state [29] , histone modification ( Roadmap Epigenomics ) , and transcription levels [30] . We note the periodicity in mutation density was maintained across nucleosomes regardless of these additional factors . However , nucleosomes within different chromatin states or containing pre-existing histone modifications associated with active transcription displayed differences in mutation translational curvature , revealing the time nucleosomes spend occupying DNA further dictates mutation density .
To determine the impact of nucleosome structure on mutation heterogeneity , we profiled the positions of ~21 million mutations across individual DNA base pairs within the 147 bp “core particle” that surround 1 . 4 million strong nucleosome dyad positions obtained from a nucleosome map derived from DNase-seq data [28] . DNase I digestion has long been used to map nucleosome DNA ( e . g . , [31–33] ) , and is particularly useful for mapping the rotational settings of nucleosomes . In contrast , MNase digestion ( and MNase-seq data ) is generally less accurate in defining the rotational settings of nucleosomes ( e . g . , see [16] ) . From this map , we restricted our analysis to nucleosomes displaying high positioning scores . A score of 10 or greater was chosen empirically as a threshold for strongly positioned nucleosomes , reflecting ≥10-fold higher likelihood that there is a positioned nucleosome at that location relative to the nucleosome-free background . Melanoma mutations within strongly positioned nucleosomes showed a pronounced ~10 bp periodicity ( determined by Lomb-Scargle analysis ) ( Fig 1B and 1C ) with peaks corresponding to outward facing nucleotides and dips corresponding to inward positions . Additionally , there was a slight curvature across the nucleosomal DNA , with more mutations near the central dyad . To assess whether the observed mutation pattern could be accounted for by sequence context , we calculated the expected per-nucleotide mutation density based on the trinucleotide contexts of all mutations ( see Materials and Methods ) . In contrast to the pattern of observed mutations , the overall expected mutation distribution was elevated , due to strongly positioned nucleosomes having a reduced mutation density compared to the rest of the genome ( S1 Fig ) . This reduction is likely due to strongly positioned nucleosomes occurring frequently in transcribed regions of the genome which are known to have lower mutation density [34] . Moreover , the expected mutation distribution failed to produce any apparent oscillation and displayed a slightly opposing translational curvature across the entirety of the nucleosome core particle ( Fig 1B and 1C ) . The stark difference between the observed and expected mutation distributions indicate that the 10 bp periodicity in the observed mutation density as well as the translational curvature across the nucleosome core particle are likely controlled by the presence of the histone octamer on the DNA instead of the underlying DNA sequence . In accordance with this interpretation , normalization of the observed mutation density by the expected mutation density ( i . e . to remove any residual effects of sequence context; referred to hereafter as a “mutation enrichment” ) revealed a strong enrichment of mutations at outward rotational settings ( as expected ) and a striking translational curvature in the mutation density , with peak mutation density near the nucleosome center and lower mutation densities near the edges of the nucleosome ( Fig 1D ) . This curvature can be represented by a best-fit polynomial ( i . e . y = ax2 + bx + c ) and since the primary coefficient for the polynomial describing mutation enrichment is negative , we hereafter refer to this mutation pattern as a “negative curvature . ” Further supporting that the oscillation and curvature in mutation density across strongly positioned nucleosomes is a function of specific histone-DNA contacts , the observed mutations in weakly positioned nucleosomes ( i . e . positioning scores of -5 to -40 ) showed a much weaker oscillatory pattern ( Fig 1E–1G ) . This is reflected in the ~3-fold lower peak of 10 bp periodicity , compared to strongly positioned nucleosomes ( Fig 1F ) . This indicates that weakly positioned nucleosomes do not impact mutation distributions as dramatically as strongly positioned nucleosomes . After normalizing the observed mutations to those expected , the mutation enrichment across weakly positioned nucleosomes was decreased near the nucleosome center ( Fig 1G ) , which is opposite of the pattern observed for strongly positioned nucleosomes . These results suggest that strongly positioned individual nucleosomes are associated with a unique mutation signature , with peaks in mutation density at outward rotational settings in the nucleosomal DNA , and an enrichment in mutation density near the central nucleosome dyad axis ( Fig 1 ) . The main mutagenic process in melanoma derives from UV-induced DNA lesions [2] . To test the hypothesis that the mutational patterns observed in nucleosomes are caused by a mechanism involving UV lesions , we parsed the mutations occurring in dipyrimidine sequences into cutaneous ( UV exposed ) and acral ( typically not UV exposed ) melanoma subtypes [3] . We repeated the analyses evaluating mutation distributions within strongly positioned nucleosomes for each tumor subset . Mutation enrichment from acral melanoma lacked the internal 10 bp oscillation , with the most prominent periodicity at ~30 bp , and showed only a slight negative curvature across the core particle ( Fig 2A ) . In contrast , the cutaneous mutations recapitulated the strong ~10 bp oscillation and negative translational curvature ( Fig 2B ) , indicating that both are derived from UV damage . The acral melanomas contained ~100-fold fewer mutations than cutaneous melanomas , which might make it difficult to detect these mutational patterns in acral melanomas due to the lower total number of mutations . We therefore took 1000 random subsets of the cutaneous mutations ( each subset containing ~1/100 mutations to match the number of mutations in acral tumors ) to test whether the loss of periodicity in the acral tumors was potentially due to a loss of power . We calculated the periodicity for each subset and counted how many subsets exhibited the same periodicity . The vast majority of the cutaneous melanoma subsets ( 99 . 3% ) had the same ~10 bp periodicity , indicating that despite the ~100-fold difference in the number of acral and cutaneous mutations , a sufficient number of mutations were present within the acral melanomas to observe any periodicity if it were to exist ( S2 Fig ) . Similar to mutations from acral melanomas , mutations occurring in dipyrimidine sequences from non-UV-exposed prostate cancers failed to produce any significant oscillation ( Fig 2C ) . We conclude that the oscillatory pattern of mutation density in nucleosomes is a unique feature of the UV-induced mutagenesis of cutaneous melanomas . The specificity of the rotational oscillation and translational curvature in mutations across nucleosomes to cutaneous melanoma raised the question as to whether these patterns were a result of variations in lesion formation , DNA repair , or both . To examine the effects of nucleosome structure on lesion formation , we analyzed the genome-wide distribution of CPD lesions ( generated by CPD-seq ) in human fibroblasts ( NHF1 cells ) irradiated with 100J/m2 of UVC light [15] . We determined the number of CPD lesions that occurred at each base across the 147 bp at strongly positioned nucleosomes and divided these values by similarly acquired lesions from purified genomic DNA treated directly with 80J/m2 of UVC light ( a dose empirically determined to yield similar levels of CPDs compared to the in cell treatment ) . This normalization removes variation in CPD formation based on the intrinsic DNA sequence effects [21] . Each data set was also divided by their total number of reads mapping to dipyrimidines in strongly positioned nucleosomes to account for differences in sequencing depth . This analysis of CPDs within strongly positioned nucleosomes revealed the same ~10 bp rotational pattern with peaks in normalized CPD formation at outward facing dinucleotides ( Fig 3A ) , as observed with melanoma mutations . Additionally , as most melanoma mutations are C -> T ( ~90% ) , we next specifically analyzed potentially mutagenic cytosine-containing CPDs ( mCPDs; i . e . TT CPDs were removed ) and observed a similar ~10 bp rotational pattern in both raw mCPD count and mCPD enrichment ( Fig 3B–3D ) . This analysis indicates that elevated CPD ( and mCPD ) formation at outward rotational settings in strongly positioned nucleosomes is likely responsible for elevated mutagenesis at these same sites in cutaneous melanomas . Assessment of CPD formation within strongly positioned nucleosomes using another published map of CPDs created by the HS-Damage-seq method [17] also produced an ~10 bp oscillation in CPDs across the nucleosome core particle ( S3 Fig ) . However , the maximum of this periodicity was shifted ~5 bases resulting in CPDs occurring more frequently at inward facing dinucleotides in this data set and opposing the oscillation observed in melanoma mutations ( S3J Fig ) . This shift is likely due to HS-Damage-seq under-representing CPDs in non-TT dipyrimidines [17] . TT dinucleotide sequences are over-represented at inward facing rotational settings in nucleosomes [35] , indicating that the underlying sequence specificity of CPD formation is likely driving the oscillation in this data set . Supporting this , normalization of the HS-Damage-seq data set by dividing the in cell CPD formation data set by CPDs measured on UV-irradiated naked DNA shifts the oscillation towards favoring the outward facing dinucleotides ( S3F Fig ) . We then investigated the impact of lesion repair on the mutation distribution at nucleosome positions . We determined the positions of nucleotide excision repair products containing CPD lesions from previously published XR-seq sequencing reads generated from NHF1 cells isolated 1 hr , 4 hr , and 8 hr after treatment with 10J/m2 of UVC light [9] . Subsequently , we counted NER events at each nucleotide among strongly positioned nucleosomes and normalized this data for sequence effects by dividing the number of NER events by the number of CPDs formed in similar positions of naked genomic DNA treated with 20J/m2 of UVC light ( determined by HS-Damage-Seq ) [17] as well as by sequencing depth . HS-Damage-seq data was used to normalize the XR-seq values because XR-seq and HS-Damage-Seq follow a similar methodology and utilize an anti-CPD ( Kamiya Biomedical , MC-062 ) antibody to enrich for lesion-containing DNA . Interestingly , NER activity at strongly positioned nucleosomes maintained an ~10 bp rotational pattern likely due to the increased amount of CPDs at outward facing dinucleotides resulting in higher amounts of repair at these sites . Despite the 10 bp oscillation , the most prominent period by Lomb-Scargle analysis occurs at ~112 bp ( Fig 3E ) . This periodicity is almost the length of the nucleosome , suggesting that it may be caused by the translational position of the nucleosome inhibiting NER near the dyad . Supporting this , extending our analysis 500 bp in either direction beyond a central nucleosome dyad revealed an apparent ~150 bp oscillation consistent with the presence of neighboring nucleosomes ( Fig 3F ) . Additionally , the repair events occurred with a positive translational curvature across the nucleosome , contrasting both CPD lesion formation and mutagenesis . Both the 10 bp oscillation and translational curvature occurred regardless of repair time point accessed ( S4 Fig ) . These results indicate that the primary effect of nucleosome structure on NER efficiency is an inhibition of repair for events towards the nucleosome dyad position with greater accessibility to lesions occurring in DNA at the edges of the nucleosome core particle . Interestingly , while NER activity clearly oscillated with a 10 bp periodicity , the observed repair maxima and minima occur at positions in the nucleosome corresponding to the same maxima and minima sites as CPD formation and mutagenesis . This suggests that the periodicity is likely the result of changes in the frequency of lesion formation , which , in turn , influences the amount of repair activity at each nucleotide . Based on these results , we propose that the patterns of mutation across nucleosomes are established by two major processes: differential CPD formation , resulting in a 10 bp oscillation of mutation favoring outward-facing , more flexible dinucleotides , and decreased repair efficiency towards the center of the nucleosome core particle , which increases the density of mutations near the dyad . Since previous studies have shown globally that chromatin compaction correlates with mutation density , we sought to further classify the nucleosomes to see if their chromatin state altered the prominence of mutation periodicity and/or translational curvature . We analyzed mutation densities across nucleosomes parsed among chromatin states determined by the chromHMM software [29] . Only 7 of the 15 states contained an average of at least 100 mutations at each bp position across their respective composite strongly positioned nucleosome core particle , which we chose as a threshold to ensure sufficient statistical power to observe any mutation patterns . All of these remaining states displayed a mutational periodicity of ~ 10 bp across nucleosomes , associated with peaks in mutation density at outward facing dinucleotides ( Fig 4A–4G ) . Apparent differences in the amplitude of the 10 bp oscillation between actively transcribed chromatin states and heterochromatin result from lower mutation numbers occurring in transcribed nucleosomes compared to heterochromatic nucleosomes and are not indicative of a greater difference in susceptibility of inward and outward facing dinucleotides to UV-induced damage and mutation in heterochromatic nucleosomes . Supporting this , when adjusted for equal sequencing depth among differentially modified nucleosomes , analysis of mCPD enrichment across strongly positioned nucleosomes with histone modifications indicative of active transcription ( H3K27ac , H3K4me1 , H3K36me3 , and H3K4me3 ) or heterochromatin histone marks ( H3K27me3 and H3K9me3 ) produced oscillations of similar amplitude ( S5 Fig ) . These results confirm previous biochemical data indicating that no difference exists in either the UV-damage periodicity patterns or UV absorption strength of DNA in different chromatin condensation states [36] . In addition to the strong ~10 bp oscillation , a peak in mutation density near the nucleosome center , reflected in the overall negative curvature , was also present in all chromatin states analyzed , however the slopes of curvature and overall mutation densities varied significantly among different states ( p-value = 0 . 0014; performed by inverting the axes , binning data , and using non-parametric ANOVA [Kruskal-Wallis] ) . The chromatin states displaying the highest pairwise divergence in nucleosome-associated mutation density were between transcription elongation regions and heterochromatic nucleosomes ( p-value = 0 . 0357; performed by Dunn’s Multiple Comparison ) ( Fig 4A and 4G ) . Nucleosomes within transcription elongation regions exhibited significantly lower overall mutation density and weaker curvature compared to the heterochromatic nucleosomes , possibly due to more efficient NER in the transcription elongation regions ( i . e . due to transcription coupled-NER ) . These two states are defined by specific histone modifications that may themselves alter the generation of mutation oscillation and curvature across nucleosomes , either by specifically recruiting repair factors or modulating transcription . To determine the impact of individual histone modifications associated with these chromatin states , we acquired ChIP-seq data from the Epigenomics Roadmap Project [37] for histone marks H3K27ac , H3K27me3 , H3K4me1 , H3K4me3 , H3K36me3 , and H3K9me3 and determined the locations of nucleosomes containing these modifications using MACS2 software [26] . Consistent with the results obtained from broad chromatin states , the mutation densities in post-translationally modified nucleosomes showed ~10 bp oscillations and negative curvature , but a variety of curvature slopes and overall mutation densities across histone modifications ( Fig 4H–4M ) ( p-value = 2 . 55x10-6 by Kruskal-Wallis ) . A striking difference in mutation density occurred between H3K36me3 and H3K27me3 ( p-value = 0 . 002 by Dunn’s Test ) ( Fig 4K and 4J ) , which are canonically associated with high and low transcription of genes , respectively . Given that the most pronounced differences in mutation density based on chromatin states and histone modifications were also strong indicators of transcription , we hypothesized that transcription levels could be a major contributor to the curvature of mutation density across nucleosomes , especially due to the activity of TC-NER . We therefore repeated our mutation counting analysis with the nucleosomes sorted into high , medium , and low transcription level based upon their average RSEM RNA-seq level in 470 melanomas . We observed the same ~10 bp periodicity as in all previous analyses . However , as transcription level increased , mutation density decreased ( p-value = 3 . 90x10-6 by Kruskal-Wallis ) , ( Fig 5A–5C ) , as did the slope of the curvature in mutation density associated with the translational setting of the nucleosome . The apparent difference in curvature slope could result from lower numbers of mutations in highly transcribed regions reducing the potential change in slope of the best fit polynomial . We therefore normalized each density by their respective average mutation load and generated best-fit polynomials for the normalized densities . Quantification of these curvatures , by calculating the second derivative of each polynomial ( Fig 5D ) , revealed a trend across transcriptional levels ( second derivatives of -7 . 177x10-5 , -5 . 779x10-5 , and -4 . 024x10-5 for Low , Medium , and High transcription , respectively; p-value of 1 . 26x10-4 by Chi-Square between Low and High ) , showing an almost 2-fold reduction in the extent of curvature at high transcription levels compared to low transcription . This difference in mutation curvature might result from differential repair due to changes in nucleosome occupancy as transcription increased . We therefore assessed the translational curvature of CPD lesion formation and repair at the transcription-parsed nucleosomes . Surprisingly , we observed no significant difference in the translational curvature of the normalized lesion or repair data between high , medium and low transcribed nucleosomes ( S6 Fig ) , indicating the mutational process responsible for this difference in curvature may be independent of CPD lesion formation or repair . However , the transcribed strand ( TS ) of genes experiences transcription-coupled repair ( TCR ) meaning that analysis of NER capacity across nucleosomes could be confounded by differences in repair between DNA strands . Performing the same analysis of translational curvature of the melanoma mutations across nucleosomes , but differentiating between the TS and non-transcribed strand ( NTS ) of the genes , revealed an expected lower mutation density on the TS of nucleosomes as compared to the NTS ( Fig 5E–5G ) . Additionally , both the TS and NTS showed decreased mutation density as transcription increased , which corroborated recent results indicating that transcription increased NER repair efficiency of both DNA strands in cutaneous squamous cell carcinoma [34] . However , the second derivatives of the normalized best fit polynomial describing the curvature of mutation density across the nucleosome indicated no difference existed between strands at any of the transcription levels ( Fig 5H–5J ) . Thus , we are unable to detect a role for either CPD formation or CPD repair in generating the differences in mutational curvature across differentially transcribed nucleosomes .
Recent whole genome studies have begun outlining the effects of chromatin states and TFs on where UV lesions form , NER efficiency , and how these effects contribute to mutational heterogeneity in human melamonas [9 , 15–17] . Here , we use maps of CPD formation , NER activity , and UV-induced mutations from sequenced melanomas to elucidate the impact of the nucleosome on mutagenesis in cancer . Our focused analysis of mutations residing in strongly positioned nucleosomes revealed an epigenetic signature ( beyond sequence context ) of UV-induced mutations which fluctuates with an ~10 bp periodicity ( Fig 6 ) . This mutational pattern likely results from higher CPD formation at more flexible , outward facing dinucleotides as DNA is bent around the histone octamer [23] . Both CPDs measured by CPD-seq and NER activity measured by XR-seq also display an ~10 bp oscillation of similar magnitudes ( Fig 3A and 3E ) , indicating that while CPDs preferentially form at outward facing dinucleotides , NER likely accesses lesions equally whether they occur at inward or outward facing positions . This agrees with our past report for CPD removal across nucleosomes in human cells [38] . While repair likely plays a lesser role in producing the observed periodicity , it appears to generate a curvature in mutation density across the length of the nucleosome . We believe this is the result of lesions near the edge of nucleosomes being more accessible to repair enzymes than those near the dyad . Nucleosome “breathing” ( i . e . unwrapping-wrapping motion of DNA on the core histones ) , which has been shown both in models of nucleosome structural dynamics [39] and in in vitro accessibility assays [40] , could provide NER enzymes greater accessibility to UV lesions in these locations . Alternatively , histone modifications or chromatin remodelers may play a role in making DNA at the edges of the nucleosome more accessible to the NER machinery . Both chromatin states and histone modifications broadly correlate with differences in mutation density in a variety of cancers , including melanoma . However , these correlations appear to primarily result from effects derived by higher order structural organization of chromatin , as opposed to differences in the structure of individual nucleosomes . We saw expected differences in the overall number of mutations observed among nucleosomes within repressed and active chromatin states , as well as histone modifications , which are associated with repressed and active genes , respectively . Moreover , more mutations occurred on both the transcribed and non-transcribed stands of DNA as repression increased , which corroborated previous studies [34] . However , the 10 bp mutational periodicity associated with the rotational setting of nucleosomes was maintained regardless of the chromatin state , histone modification , or transcription level of the nucleosomes assessed . Thus , CPD formation appears to be unaltered by the specific modification or compaction state of the nucleosome and is only impacted by the fundamental wrapping of DNA around the histone octamer . In contrast , the degree of translational curvature of mutations differed among nucleosomes based upon chromatin state and histone modification . This effect could result from certain histone modifications facilitating the recruitment of NER proteins to the site of UV damage . H3K36 methylation has previously been shown to be involved in other DNA repair processes [41 , 42] . Additionally , depletion of the acetyltransferase GCN5 in yeast reduces NER efficiency , suggesting that some interaction between the NER machinery and histone modification may exist [43–45] . Alternatively , H3K36 methylation and H3K9 trimethylation are markers of active and repressed transcription , respectively . The different transcription levels associated with these histone marks may facilitate repair near the dyad of nucleosomes by reducing histone occupancy in more highly transcribed regions . We did observe a decrease in mutational curvature across nucleosomes as their transcription level increased . Further examination of CPD-seq and XR-seq levels , however , indicated that no difference existed in the curvature of CPD formation or NER activity across the translational setting of the differentially transcribed nucleosomes . Thus , neither our analysis of lesion formation nor repair could account for the decrease in curvature of more highly transcribed nucleosomes . This effect therefore may originate from differences in the usage of trans-lesion synthesis polymerase η ( which bypasses CPDs with high fidelity [46 , 47] ) or the rate of cytidine deamination [48] at CPDs in different chromosome contexts . The rotational setting of DNA in nucleosomes alters cytidine deamination rates of CPDs [49] . CPD-associated deamination may be similarly affected by the translational setting , especially considering that mutations caused by spontaneous cytidine deamination in yeast are elevated in linker regions between nucleosomes compared to nucleosome bound DNA [50] . Global approaches to mapping deamination rates in the future may allow for this supposition to be tested . A complete understanding of the determinants of mutational heterogeneity in cancer will continue to provide important insights into the mechanistic processes that govern the efficiencies of lesion formation and DNA repair . We describe here an epigenetic regulation of lesion formation , repair , and ultimately mutagenesis by nucleosome structure , however , other chromosomal features additionally exacerbate mutational heterogeneity beyond that expected by sequence preferences for DNA damage . Transcription factor binding has clear impacts on lesion formation [16 , 19] and strongly contributes to increasing mutation frequencies in melanomas [15 , 18] . Likewise , the intrinsic curvature of DNA has also been recently reported to predict regional mutation differences in both yeast model systems and multiple human cancers including melanoma [51] . This impact of DNA curvature appears to relate to less curved sequences accumulating more DNA damage and mutagenesis . In contrast , the elevation of CPDs and UV-induced mutation at outward facing dinucleotides compared to inward facing dinucleotide clearly occurs in curved DNA induced by histone binding . These apparently contrasting results indicate that DNA damage occurring in different chromatin states ( e . g . nucleosome bound , transcription factor bound , or unbound DNA ) may influence which factors provide the dominant physical characteristic to influence the efficiency of mutagenesis . The integration of all these processes into different rates of mutation regionally , or even at a single nucleotide resolution , likely establishes the mutational heterogeneity observed in human cancers , which likewise impacts carcinogenesis by establishing high-risk sites within genomes that may harbor key cancer driver genes . As much of the differences in mutation rate are independent of selection by the tumor ( as most mutations confer no advantage to the tumor ) , mutational heterogeneity also obscures our ability to differentiate selected driver events from mutagenic hotspots [2 , 7] . Our recent determination that Ets family transcription factors greatly sensitize their binding sites to CPD formation , and ultimately mutation , highlights the potential difficulty in this determination [15] . Multiple sites , as exemplified by the Ets site in the RPL13A promoter , are highly recurrent in melanoma , but appear to be unlikely cancer drivers based on function of the gene regulated by the mutated promoter . The extended UV-induced lesion and mutation signature generated by nucleosome structure could produce similar effects , especially considering the large number of dinucleotides in the genome that reside at outward facing rotational settings in nucleosomes . The scope of these sensitive sites greatly expand the potential for strongly positioned nucleosomes to facilitate carcinogenesis by their shaping of the genomic mutational landscape .
Mutations from 184 melanoma samples were obtained from https://dcc . icgc . org/api/v1/download ? fn=/release_20/Projects/MELA-AU/simple_somatic_mutation . open . MELA-AU . tsv . gz and from 216 prostate donors https://dcc . icgc . org/api/v1/download ? fn=/release_25/Projects/PRAD-UK/simple_somatic_mutation . open . PRAD-UK . tsv . gz . Mutations occurring in multiple tumors from the same patient may have arisen before metastasis and were removed . Initial analysis of the impact on nucleosome position on mutation density ( Fig 1 ) utilized all single nucleotide base substitutions . Pre-computed nucleosome scores were acquired from [28] . A greedy algorithm was implemented in C++ to identify the central dyad positions of nucleosomes using the nucleosome scores . The algorithm employed a priority queue to select the next highest nucleosome score , after excluding all nucleosome scores for positions occurring within 117bp of called nucleosome dyads . Nucleosomes that overlapped with ENCODE blacklisted regions ( Duke and DAC ) were excluded . Strong nucleosomes had a score of 10 or greater and weak nucleosomes had scores between -5 and -40 . Only single base pair mutations occurring in dipyrimidine contexts were used for analyses which were normalized by the expected number of mutations . The subtype of each tumor determined from Supplemental Table 1 in [3] . The number of mutations in each possible trinucleotide context were counted and divided by the total number of mutations . Once these frequencies were obtained , the DNA sequences were acquired for each nucleosome and the calculated frequencies were applied to the trinucleotides in the DNA sequences to produce expected mutation counts . Expected mutations were recalculated for each subset analysis to correctly normalize the respective observed mutations . For analyses limited to mutations occurring in dipyrimidine contexts , expected values were likewise calculated only using trinucleotide contexts that contain dipyrimidines . Subsets were generated using the “random” python3 module and randomly choosing ~1/100 of the cutaneous mutations . The ratio of T and C mutations was maintained by choosing proportional subsets from each mutation type . The Lomb-Scargle analysis was performed on each subset to identify the dominant periodicity . Periodicities greater than 100 bp were excluded to detect the presence of ~10 bp peaks . For lesion formation and repair analyses , both the 5’ and 3’ positions of CPDs were used . CPD-seq data was acquired under accession number GSE103487 [15] . Raw sequencing reads for XR-seq data and HS-Damage-seq data were acquired from references [9] and [17] under accession numbers GSE76391 and GSE98025 , respectively . The 1 hr , 4 hr , and 8 hr time points for repair of CPDs measured by XR-seq and the HS-Damage-seq of UV-exposed GM12878 naked DNA were used . These reads were mapped to the hg19 genome sequence using bowtie2 [52] . The position of lesions in XR-seq data was determined as in [15] . The HS-Damage-seq data was processed similarly , with the lesion position occurring 2 bp immediately 5’ of the read end as in [17] . The HS-Damage-seq CPD lesion positions were used for normalization of the XR-seq CPD lesion positions . Nucleosomes were sub-categorized by cross-referencing their positions with the genomic locations of different chromatin states , histone modifications , and transcription level . Chromatin states were acquired for the Nhlf cell line from [29] . Two “repetitive” states had low nucleosome counts ( ~less than 100 per state ) and another 6 chromatin states had low mutation numbers ( ~less than 100 mutation per bp ) and were thus removed from analysis . Location of histone modifications was determined from ChIP-seq data acquired from the Epigenomics Roadmap [37] for H3K27ac , H3K27me3 , H3K4me1 , H3K4me3 , H3K36me3 , and H3K9me3 ( accession numbers GSM1127073 , GSM958150 , GSM958152 , GSM958151 , GSM958160 , GSM958165 , respectively ) . The MACS2 software package [26] was used to call peaks from the ChIP-seq data using standard parameters , with the additional stipulations of calling broad peaks with a p-value less than 0 . 01 . The median expression level per gene for 470 human melanomas [30] was calculated from RSEM mRNA-seq data ( http://gdac . broadinstitute . org/runs/stddata__2016_01_28/data/SKCM/20160128/gdac . broadinstitute . org_SKCM . Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data . Level_3 . 2016012800 . 0 . 0 . tar . gz ) . The CCDS gene positions ( www . ncbi . nlm . nih . gov/projects/CCDS/CcdsBrowse . cgi ) for the corresponding mRNAs were sorted by expression levels , and divided them into 4 quartiles: Low , Medium ( the middle 2 quartiles ) , and High transcription . Statistical analyses were performed using python3 , either with premade subroutines from python modules or personally designed analyses . The Lomb-Scargle analysis was conducted using the astropy module with default parameters . Second order polynomial ( best-fit ) functions were generated using a Least Squares method from the numpy module . Non-parametric ANOVA ( Kruskal-Wallis ) was performed using a subroutine modified from ( https://gist . github . com/alimuldal/fbb19b73fa25423f02e8 ) , as well as post-hoc Dunn’s test . Additionally , to generate distributions from the mutation data for the Kruskal-Wallis analysis , the axes of the data were inverted , where the enrichment values became positions along a continuous range and the bp positions became counts , tallied along the continuous range . The ends of the range were determined by identifying the maximum and minimum values of the combined data and rounding the enrichment ( usually a decimal value ) to the nearest integer . When plotted as a histogram the data sets showed features similar to normal distributions , and thus Kruskal-Wallis could be used to determine if their means were statistically different from one another . Chi-square was performed on the transcript-sorted nucleosomes by binning the observed mutations along the DNA sequence into ~10 bp bins ( to remove the oscillatory effect; 16 bins total ) , and then performing the analysis between all pairwise combinations . Numerical values underlying graphs in the manuscript are provided in S1 Data . | UV-induced mutations are abundant and heterogeneously distributed across melanoma genomes . Understanding the mechanisms that produce this heterogeneity may help decipher which mutations drive the cancer phenotype . While it is known that mutation density correlates with chromatin compaction on a large scale , recent studies have suggested that local chromatin structure impacts mutation distribution in ways previously undetected . We therefore examined the distribution of melanoma mutations in strongly positioned nucleosomes where we observed a striking oscillatory and curvature pattern . UV lesion formation appeared to be responsible for mutation oscillation , despite active repair occurring in the nucleosome core particle . However , more CPD lesions are removed near the edges of nucleosomes , and thus generated an overall translational curvature in mutation density . | [
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"chromatin... | 2018 | Nucleosome positions establish an extended mutation signature in melanoma |
The study of epithelial morphogenesis is fundamental to increasing our understanding of organ function and disease . Great progress has been made through study of culture systems such as Madin-Darby canine kidney ( MDCK ) cells , but many aspects of even simple morphogenesis remain unclear . For example , are specific cell actions tightly coupled to the characteristics of the cell's environment or are they more often cell state dependent ? How does the single lumen , single cell layer cyst consistently emerge from a variety of cell actions ? To improve insight , we instantiated in silico analogues that used hypothesized cell behavior mechanisms to mimic MDCK cystogenesis . We tested them through in vitro experimentation and quantitative validation . We observed novel growth patterns , including a cell behavior shift that began around day five of growth . We created agent-oriented analogues that used the cellular Potts model along with an Iterative Refinement protocol . Following several refinements , we achieved a degree of validation for two separate mechanisms . Both survived falsification and achieved prespecified measures of similarity to cell culture properties . In silico components and mechanisms mapped to in vitro counterparts . In silico , the axis of cell division significantly affects lumen number without changing cell number or cyst size . Reducing the amount of in silico luminal cell death had limited effect on cystogenesis . Simulations provide an observable theory for cystogenesis based on hypothesized , cell-level operating principles .
Epithelial morphogenesis is fundamental to the development and functional specialization of tissues and organs . Tight regulation of tissue size , shape and polarization is critical for normal organ development and function . Disruption of these regulatory mechanisms leads to an array of diseases including autosomal dominant polycystic kidney disease , stenosis , and cancer . Epithelial cells , such as Madin-Darby canine kidney ( MDCK ) cells , cultured in a 3D matrix of natural basement membrane components , can recapitulate in vitro many of the in vivo growth characteristics of epithelial organs . They are thus valuable model systems for studying the cellular mechanisms of in vivo epithelial morphogenesis . Their phenotypic simplicity coupled with accumulated knowledge of their molecular biology provide excellent case studies for gleaning needed insight into how molecular events and environmental feedback pathways at subcellular levels lead to cell- and cyst-level phenotype . These model systems lend themselves to computational analysis and modeling as the means to gain that insight and improve our understanding of organogenesis . To achieve that goal , we must first develop explanatory and easily challenged computational , mechanistic models . In biological research , explanatory mechanistic models generally precede predictive mechanistic models . The operating principles of explanatory mechanistic models of the type described herein are hypotheses about how we think phenomena are generated . The models are part of frameworks for generating and testing mechanistic hypotheses , as described in [1] , [2] . While many aspects of MDCK cyst formation are well understood , quantitative data for cystogenesis has been lacking . The most recent computational models [1]-[4] relied on previously published quantitative data that described a few aspects of MDCK cyst growth in collagen cultures [5] . There is limited data available on the dynamics of cell number , cyst and lumen size , and mean cell size in Matrigel cultures . That caused previous models to assume that cell size remains constant . The presented data demonstrate that cell size varies during the course of cyst growth . An objective of the project was to couple in vitro and in silico model systems to achieve a deeper understanding of cell behavior during MDCK cystogenesis within 3D Matrigel cultures . Of specific interest were the roles played by , and the timing of polarization , apoptosis , and lumen expansion . In order to improve our understanding of the link between individual cell behavior and cystogenesis , we proceeded in parallel on two fronts . We undertook new in vitro experiments designed to provide a more temporally and spatially fine-grained record of cell-level events during the first ten days of MDCK cystogenesis . These experiments and their results are described in this report . A thorough quantitative analysis of these results revealed a third stage of cyst growth after cyst initiation and lumen creation and expansion . That stage was characterized by the presence of a new cell state marked by a decrease in cell division rate and cessation of the decrease in cell size observed in previous stages . We refer to a cell in that state as being “stabilized” . We also developed and iteratively refined abstract , spatially fine-grained , multi-attribute , mechanistic , in silico , MDCK cell analogues ( ISMAs ) capable of cystogenesis . To create and validate ISMAs , we merged two modeling techniques while introducing several novel features . Following rounds of iterative mechanism refinement ( including falsification and validation ) , time-dependent measures of several in silico cystogenesis phenomena , including sizes of cells , cysts , and lumens , cell number , and lumen number , became quantitatively indistinguishable from corresponding in vitro measures . The process led to two successful ISMAs that had similar operating principles but relied on different mechanistic hypotheses for how cells stabilized . In one , cells relied on information about the lumen . In the other , transition to the stabilized state was a simple timed event . Independent in vitro experiments [6] , which used molecular interventions to alter the axis of cell division in two different ways , provided data that challenged ISMA mechanisms and the predictions of the cystogenic consequences of such interventions . ISMA mechanisms survived the falsification challenge: measures of cystogenesis during simulation experiments mimicking both interventions were quantitatively similar to in vitro data . This further supported our hypothesis that the cause-and-effect relationships ( mechanisms ) occurring within ISMAs during in silico cystogenesis ( and thus their morphogenic agenda ) have in vitro counterparts , both in the presence and absence of mechanistic interventions . By challenging these in silico mechanisms we better understand their in vitro cellular counterparts .
In order to study the process of cyst development in detail , MDCK cells were grown and observed in 3D Matrigel culture for one to ten days and analyzed quantitatively each day . As shown in Figure 1 , cysts developed in a manner consistent with previous observations [7]-[9] . A suspension of mostly single MDCK cells divided to form small clusters during the first 24 hours . Most cells polarized ( defined by podocalyxin localization at the nascent apical surface of the cell ) during the first two days of growth and all cells polarized by day 3 . Cysts developed single ( 11 of 20 ) or multiple ( 9 of 20 ) lumens by the end of day 2 . Most cyst cross-sections appeared circular . The deviation from a circle ranged between 2 and 5% . We measured and recorded cyst and lumen area and perimeter , cell number , the number of single and multiple lumen cysts , and the number of single-lumen , single- ( cell ) layer ( SLSL ) cysts . Results are graphed in Figures 2 and 3 . We calculated mean cell area and the ratio of total cellular area to total cyst area . Cell number increased exponentially through day 5 . It slowed and increased at a constant rate after day 6 . Coincident with that shift , the variance in cell number per cyst increased ( Figure 2A ) . Cyst and lumen area increased monotonically ( Figure 2B ) . Mean cell size decreased at a constant rate through day 6 ( Figure 2C ) and then leveled off at roughly the same time that cell division slowed . Mean cell size increased slightly following the shift . Cell size variance was smallest on days 5–8 . We did not find a strong correlation between mean cell size and other cyst measurements , including cell number , lumen size , lumen number , or lumen perimeter/cell number . The ratio of total cellular area to cyst area ( Figure 2D ) indicated that the portion of cyst occupied by cells decreased as cysts expanded ( and thus the portion occupied by lumen increased ) . The ratio decreased quite steeply between days 5 and 6 with very little overlap; the majority of cysts at day 5 had a ratio higher than 0 . 6 and the majority of cysts at day 6 had a ratio lower than 0 . 6 . These observations taken together indicated a shift in cell behavior occurred at approximately day 5 ( referred to hereafter as simply the shift ) . The data also supports the idea that cell compression during lumen expansion may be a factor triggering cell entry into the stabilized state . During the first day of growth , some cysts developed lumens , while others had no visible lumen . From days 2-10 all cysts had at least one lumen ( Figure 3A ) . Multiple lumens appeared in a number of cysts , but their frequency decreased over time . Previous studies [6] considered cysts to be “normal” if they contained a single layer of actin and apical membrane markers surrounding a single lumen . We distinguished between single-layer , single-lumen ( SLSL ) cysts , in which all cells contact both extracellular matrix and lumen; cysts with a single lumen where some cells did not touch the extracellular matrix or the lumen; and cysts with multiple lumens ( Figure 1 ) . After day 2 , the percentage of SLSL cysts ranged between 55% and 85% ( Figure 3B ) , in rough agreement with the 80% of cysts observed by Zheng et al . to be “normal” [6] . In cases where single-lumen cysts did not have a single layer of cells , usually only one or two cells did not contact the lumen or extracellular matrix . These data indicate that the percentage of cysts with multiple lumens decreases over time , likely as smaller lumens merge together into larger . It is possible that a few cysts might increase their lumen number over time even as mean lumen number decreased , but that behavior would only be observed using time-lapse microscopy of individual cysts . In order to create and validate ISMAs , we used a number of modeling techniques and approaches , detailed in Methods . To avoid confusion between in vitro and ISMA components and mechanisms with similar names , we use small caps when referring to the latter . Following the Iterative Refinement Protocol ( IR Protocol ) led to two specifications of cell behavior that achieved all targeted attributes in Table 1 and all prespecified Similarity Measures ( SMs; described below ) . They are the lumen stabilized ISMA ( LS ISMA ) and the timed stabilization ISMA ( TS ISMA ) . There are only three cell states: unpolarized , polarized , and stabilized . Both LS and TS ISMAs have a common morphogenic agenda . It is a consequence of their operating principles , which are a networked consequence of cell state and micromechanisms . The latter are primarily axiom-dependent , and the axioms , in turn , depend on particular local and temporal conditions . The axioms are placeholders for even more fine-grained micromechanisms . The only difference between the LS and TS ISMAs is the mechanism used by polarized cells to shift to the stabilized state . Within the LS ISMA , polarized cells use information about the lumen to decide when to stabilize . Within the TS ISMA , transition to the stabilized state is a simple timed event ( each cell used its own internal clock ) . We did not discover any in vitro observations that would provide a basis for selecting one micromechanism over the other . Cell operating principles require each cell to have knowledge of its internal state and immediate environment , including the size of the neighboring lumen ( for the LS ISMA ) . Cell division is based on factors other than cell size . Early in the process , cyst size can be independent of lumen size . The orientation of cell division is extremely important in influencing the formation and number of lumens within a cyst . We explored alternative mechanistic variations , but failed to find others of comparable simplicity capable of achieving all targeted attributes and prespecified SMs . For simplicity we present and discuss measures from LS ISMA simulations within the text ( Figures 2 and 3 ) and provide the same simulation measures for TS ISMAs in Figures S1 and S2 . Results from earlier ISMA that were falsified because they failed to achieve one or more SMs are also discussed . ISMA cysts were similar to cysts grown within Matrigel ( Figure 4 ) . Cysts began with 1-3 cells at day 0 . Cells polarized and formed lumens within the first two days ( Figure 3 and Video S1 ) . Lumens and cysts expanded at a rate indistinguishable from that observed in vitro . In general , a cyst formed with a single lumen surrounded by a single layer of polarized cells ( Figure 4 and Video S1 ) . Occasionally multiple lumens formed , each separated by an independent layer of cells , such that no cell contacted more than one lumen ( Figure 4 and Video S2 ) . The ISMA successfully achieved all qualitative and quantitative targeted attributes listed in Table 1 . ISMA cell number also exhibited two growth phases , with the rate of cell division decreasing at day 6 ( Figure 2 ) . Lumen and cyst size increased at rates similar to those observed in vitro , but standard deviations were smaller . Cell size also decreased at a rate comparable to in vitro , and its standard deviations were also smaller . As indicated by the values of Similarity Measure 1 ( discussed below ) in Figures 1 and 2 , ISMAs produced quantitative results similar to in vitro values . ISMAs were executed using the parameter settings in Table 2 , and cyst and lumen area were scaled by 2 . 25 µm2 and perimeter by 0 . 75 μm . Simulations produced single and multiple lumen cysts at frequencies comparable to those observed in vitro ( Figure 3A ) , though the percentage of cysts with single lumens was slightly higher than observed in vitro . The percentage of SLSL cysts ( Figure 3B ) leveled off between days 2 and 6 and then increased steadily to day 10 as lumens merged . Cells that stabilized were not allowed to create new lumens , but could contribute to lumen expansion . If this restriction were to be removed and cells were allowed to create new lumens after they stabilized , the percentage of SLSL cysts might remain steady or decrease . To provide a validation target for ISMA cystogenesis and to compare ISMA and in vitro results , we developed SMs [10] , which quantified the similarity within and between the in silico and in vitro data . We posit that , if in silico data satisfies the SMs , then that data would be indistinguishable from data produced by a repeated in vitro experiment . SM1 compared results from individual simulations to in vitro mean values , indicating the similarity of in silico and in vitro results . SM1 is the percentage of in silico observations that fell within±25% of the mean in vitro value for a given measure . SM1 values are listed in Figure 2 . To survive falsification , >50% of simulations must achieve the SM1 target for nine of ten days , as detailed in Methods . For example , the ±25% range for in vitro cell number at day 3 was 6 . 7 to 11 . 1 with a mean of 8 . 9 . Seventy-two percent of simulations had cell numbers within that range at day 3 . SM1 values for cell number , cyst size , mean cell area , and the ratio of cellular to cyst area exceeded 50% at all days , so a degree of validation was achieved . The SM1 value for lumen size exceeded the 50% cutoff for nine of ten days , although the values were lower . To facilitate assessing SM1 values and comparing in vitro and in silico data , we specified and used Self-Similarity Measure 1 ( SSM1 ) . It measured the similarity between the in vitro mean value and individual in vitro values and thus how closely grouped around the mean the individual in vitro values were . Similar to SM1 , SSM1 is the percentage of individual in vitro cyst measures each day that fall within a specified range . SSM1 can be used to evaluate corresponding SM1 values . Large SSM1 values are a characteristic of measures having a small variance . Values of SSM1 were larger than the target for all measures except lumen size , indicating that lumen size in vitro varied more extensively about the mean than other quantities . SM1 did not consider the variance of the data . To address variance , we specified SM2 . It compared the coefficient of variance of in silico and in vitro experiments . SM2 measured the absolute value of the difference between the in vitro and in silico coefficient of variance each day . ISMAs survived falsification if SM2<0 . 15 for nine of ten days ( strong validation ) or <0 . 25 for eight of ten days ( medium validation ) . The current ISMA achieved strong validation for cell number , mean cell area , and the ratio of cellular to cyst area ( Table S1 ) . It achieved medium validation for cyst size and lumen size , comparable to SSM1 values . When MDCK cells can polarize well , they do not need apoptosis to form cysts with lumens [9] . Consequently , cell death is relatively uncommon during in vitro MDCK cyst development [9]: on a given day , no more than 15% of cysts had one or more apoptotic cells within the lumen and no more than 10% of cysts had one or more apoptotic cells with matrix contact . Cell death did occur during ISMA executions , but at slightly lower frequencies than observed in vitro ( Figure 5 ) . In Methods , we specified that the average duration between a cell initiating death and being removed from the simulation to be ten simulation cycles , which maps to five hours . The actual in vitro duration will affect the number of visible apoptotic cells observed each day . When we caused cells to shrink somewhat slower , the cell death values in Figure 5B increased . The experimental results provided in Figure S3 demonstrate that decreasing the value of dyingShrinkRate from 9 to 4 . 5 increased the mean duration of cell death ( from 4 . 6 to 7 . 4 hours ) and increased the percentage of dying cells . It is noteworthy that all validation targets were achieved without requiring stabilized cells to die more frequently than polarized cells . Based on current knowledge , the ISMA accurately mimics in vitro quantitative data , but the duration of apoptosis within MDCK cells in vitro has not been quantitatively established . In order to be certain about the role played by cell death , time-lapse movies using a caspase-3-GFP will be required . After the ISMAs achieved the above , targeted attributes , Zheng et al . [6] reported measuring the consequences of disrupting cell division orientation on MDCK cyst morphology . Knocking down LGN , which plays a role in spindle orientation during cell division , caused cell division orientation to become random instead of aligning with the axis perpendicular to the cellular plane . The frequency of “normal” cysts decreased from roughly 80% to 20-30% . We added those observations to our targeted attributes list and then explored the degree to which cyst morphology following a comparable ISMA intervention would mimic the in vitro results , thus surviving the challenge . We altered cell division so that all cells divided with a random orientation . The results ( Figure 6A ) were similar to those of Zheng et al . The altered ISMA produced less than 20% SLSL cysts and more than 30% multi-lumen cysts at days 2 through 9 . Additional details are available in Figure S4 . In a second experiment , Zheng et al . targeted LGN to the apical membrane . So doing rotated the axis of division by 90° , thus reversing cell division orientation . The procedure reduced the frequency of normal cysts to roughly 10% . We conducted a similar experiment by modifying ISMAs so that the axis of division was parallel , rather than perpendicular to the lumen edge . That intervention produced SLSL cysts less than 10% of the time ( Figures 6B and S5 ) . ISMAs survived both challenges; in both cases , altering the orientation of cell division decreased the percentage of single lumen and SLSL cysts to a degree similar to that observed within in vitro experiments . Cell death contributes to cystogenesis , but it remains unclear to what extent it is essential . In order to explore the consequences of decreased cell death frequency , we executed simulations in which we reduced deathRateLumen from 0 . 02 to 0 . 0 . We did not alter the probability of cell death in cells contacting matrix . We noted no significant difference in cell number during the first six days of growth , but during days 7 through 10 mean CELL number was 10-15% higher than observed during control ISMA growth ( Figure 7A ) . The observed standard deviations also increased . We observed a smaller percentage of SLSL cysts than in control simulations , especially during days 6 to 10 ( Figure 7B ) . Values for cyst area , lumen area , cell size , and the ratio of cellular to cyst area were similar to control values ( Figure S6 ) , while the percentage of single lumen cysts decreased slightly ( Figure S7 ) . Delayed cell polarization is believed to contribute to the differences in cyst growth in Matrigel and collagen [9] , although it is possible that a lower initial rate of cell clustering and a slower growth rate might be factors as well . To explore the effect of delayed polarization on ISMA cystogenesis , we increased the value of polarDelay from 42 ( equivalent to 21 hours ) to 130 ( equivalent to 65 hours ) . Relative to controls , cell number increased at an equivalent rate during the first six days , but was larger during days 7–10 ( Figure 8A ) . Cell polarization ( data not shown ) and lumen formation occurred later than in controls ( Figure 8B ) . The area taken up by cells remained roughly constant , but the delay in lumen formation and resulting smaller lumens caused the ratio of cellular area to total cyst area to be significantly larger than control values during days 2-8 ( Figure S8 ) . Not surprisingly , there were fewer single and multiple lumen cysts during the first three days . When lumen formation began , however , it often resulted in multiple lumens ( >80% for days 4–6 ) ; SLSL cysts were observed infrequently . As lumens expanded and merged during the later stages of growth , the frequency of SLSL cysts increased . The percentage of dying cells not contacting the matrix was significantly larger at days 4–10 , indicating that many of these cells died as lumen expansion occurred ( data not shown ) . Some of these in silico results reflect those observed within growth in collagen , but it seems unlikely that delayed cell polarization in vitro is solely responsible for those differences .
Observations reported herein about in vitro MDCK cystogenesis are consistent with those made previously [6] , [9] , [11] . There is no evidence of behavioral differences between cells within single and multiple lumen cysts . We could not establish a causative connection between the slowing of cell division and the change in cell size . The evidence indicates that initial lumen expansion is somewhat isochoric: early lumen expansion is primarily a consequence of cell shrinkage . After an interval of lumen expansion and cell shrinkage lasting about six days , cell behavior changes: cell size stabilizes and cells begin to stretch as the lumen continues to expand ( Figure 1 ) ; cell division slows dramatically; the expanding lumen becomes the primary driver of cyst size; and the variance in both cell area and cyst size increases . Iteratively constructed ISMAs quantitatively mimicked a targeted set of in vitro data and cell behaviors . Measures of ISMA cystogenesis matched corresponding measures of MDCK cystogenesis over ten days ( Figures 2 , 3 , & 5 ) . The pathways and proteins that play influential roles in cell behavior during MDCK cystogenesis are objects of active research and are increasingly well understood . However , knowledge of how specific cell actions and events are choreographed during cystogenesis is still limited . The latter knowledge is needed to begin establishing causal linkages between molecular level events and systemic phenotype . Previous analogues [2] , [3] used a simple representation of a cell: each cell occupied a single 2D hexagonal grid space . They were falsified when we added qualitative observations about changes in cell size and shape to our targeted attributes list ( Table 1 ) . To mimic these newly targeted attributes , we needed cells to be more fine-grained . To generate the current ISMA , we began with an in silico analogue that had achieved a degree of validation and then conducted in vitro experiments designed to challenge and possibly falsify it . We then reengineered the in silico system to reflect , explore , and challenge new insight provided by the fresh in vitro data . We engineered new analogues using the cellular Potts model ( CPM ) , which provided several capabilities , including enabling cell size and shape change . To slow the increase in cell number after day 6 , we introduced a stable cell state . We envision the above in silico-wet-lab cycle continuing indefinitely . It is straightforward to explore the consequences of in silico mechanistic interventions . If these interventions result in altered system behaviors ( predictions ) , it may suggest new in vitro experiments designed to test them . Examples include the effect of delayed polarization on cyst phenotype , the lack of noticeable changes when cell death is inhibited , and the causal link between lumen size and cell stabilization . Furthermore , we expect a change in cell state ( cell stabilization at day 6 ) to be accompanied by measurable changes in gene expression profiles and biochemical signaling . The ISMA illustrated in Figure 4 achieved all targeted attributes . It was preceded by two earlier versions . These ISMAs differed in the mechanism used to initiate cell stabilization . We hypothesized that in vitro cells might use knowledge of their internal geometry to sense their perceived stretch and subsequently stabilize . One early analogue , the geometrical mechanism ISMA ( GM ISMA ) , directly tested this hypothesis; each cell used measures of its area and geometry to determine when to shift to the stabilized state . To achieve a degree of validation required the use of an axiom specifying that stabilized cells would be more likely than polarized cells to die when not in contact with matrix . This axiom was implemented in order to decrease the number of cells within the lumen and thus increase the number of SLSL cysts . The GM ISMA was falsified when targeted SMs for the percentage of single lumen , multiple lumen , and SLSL cysts were strengthened to those achieved in Figure 2 ( Figures S9 and S10 ) . It was falsified because the time at which cells stabilized was too variable; some cells stabilized early , others much later , resulting in very few SLSL cysts ( data not shown ) . A second version , called the timed stabilization ISMA ( TS ISMA ) , used an internal clock to signal cell stabilization , resulting in a uniform stabilization time and reducing the variance in cyst size . The TS ISMA survived falsification ( Figure S1 ) , providing evidence that stabilization time influences SLSL cyst percentages . The GM ISMA axiom specifying that stabilized cells would be more likely than polarized cells to die when not in contact with matrix was not needed . The TS ISMA was capable of generating high percentages of SLSL cysts even without this axiom , and so the axiom was removed in that and subsequent ISMAs . Although the TS ISMA survived falsification , we were not aware of any in vitro evidence suggesting existence of an equivalent internal clock-based mechanism . If such a mechanism does exist , it might be molecularly equivalent to that of cell polarization . Genes that regulate cellular senescence can suppress the cell cycle , and the sirtuin protein SIRT1 is involved in cellular senescence [12] , [13] . It is possible a cell-autonomous timing mechanism could exist that depends on the regulation of SIRT1 and its downstream targets , as detailed in Supporting Text S1 . We hypothesized that a mechanism that used the geometry of the lumen instead of the geometry of individual cells to signal cell stabilization might bridge that gap and still produce a low variance in stabilization times . We developed the lumen stabilized ISMA ( LS ISMA ) described within this report to test that hypothesis and discovered that in addition to surviving falsification ( Figure 2 ) it generated stabilization variance between the GM and TS ISMAs . We can surmise a mapping between the lumen-based stabilization mechanism and a functionally equivalent in vitro mechanism in which apical sensory input to each cell provides it with information that correlates to lumen size . Current evidence supports the hypothesis that cells in the cyst wall can sense lumen size . One mechanism utilizes the tension generated at the luminal membrane by membrane stretching . This tensional information is transduced by the subapical F-actin network , which acts both as a scaffold for maintaining luminal integrity , as well as a region for aggregation of recycling endosomes that regulate the protein and lipid composition of the apical plasma membrane . Thus , regulators of this F-actin network can regulate lumen and cyst size . Potential molecular mechanisms are detailed in Supporting Text S1 . We should seek additional , in silico mechanisms that are equally effective in enabling ISMAs to achieve validation targets . Given phenomena , what hypothetical generators ( and measures ) might generate them ? Studying an inverse mapping requires multiple , seemingly plausible hypotheses , which then compete against each other during simulation experiments as done here . After falsification and validation using the IR Protocol , those that survive spawn additional , more refined hypotheses . Having multiple mechanistic options for realizing the same behaviors may be biomimetic in that it marginally increases system robustness . An example of a potential additional in silico mechanism is one that uses time-dependent dynamic parameters , which might assist in the exploration of finer-grained , intracellular molecular behaviors . ISMAs currently contain a small number of parameters that can have implicitly dynamic values ( such as the time that elapses between cell division events ) . They change when cells change state . In general , however , all parameters are fixed for the duration of the simulation . Expanding the set of targeted attributes may force consideration of time varying parameter values . If , for example , in vitro data were targeted that demonstrated the build-up of certain proteins along the plasma membrane , dynamic variables could be implemented that controlled the amount of the protein counterpart within the analogue . ISMAs had already achieved all targeted attribute when the work of Zheng et al . [6] was published . Results from their studies provided an independent challenge to ISMA mechanisms and their robustness . The simulation results in Figure 6 are a consequence of two different simulation interventions: making the cell axis of division random ( Figure 6A ) and reversing the cell axis of division ( rotating it 90° ) ( Figure 6B ) . These predictions are fully consistent with the in vitro results of Zheng et al . As previously stated , they defined a normal cyst as one with actin staining at the apical cell surfaces surrounding a single lumen . Included in that definition are our SLSL cysts and cysts with a single lumen . In Zheng et al . , when cell division was randomized , the percentage of cysts with single lumens at day 4 dropped from 81 . 9% to 21 . 5% , a different of 60 . 4% . In ISMA simulations , when divisionReg was changed from 1 ( ordered division ) to 0 ( random division ) the percentage of cysts with a single lumen dropped from 94% to 46% , a difference of 48% , which is quite similar to the decrease observed in vitro ( Figure 6A ) . As seen in Figure 6B , when the axis of division was reversed , the percentage of cysts with a single lumen dropped from 81 . 9% to 11 . 5% , a difference of 70 . 4% . Within the ISMA , when divisionReg was changed from 1 to 3 ( reversed division ) , the percentage of cysts with a single lumen dropped from 94% to 14% , a difference of 80% . In addition , the in silico results provide a prediction of in vitro behavior that could be challenged through in vitro experimentation . When division is reversed within the LS ISMA ( Figure S5A ) cell number continues to increase after day 6 , most likely because the numerous small lumens do not reach a sufficient size to cause cell stabilization . In strong contrast , when division is reversed within the TS ISMA ( Figure S11-2 ) cell number stops increasing at day 5 and remains stable thereafter . Future experiments of the type conducted by Zheng et al . that quantify cytogenesis over longer intervals would provide evidence supporting one or the other mechanistic hypothesis . A cell-level event is one that is visible at the current level of resolution . An event that maps to an intracellular process ( referred to as intracellular ) can occur without causing a visible change; it is below the current level of resolution . Of the events listed in Table 3 , the two marked ( * ) only exist within the in silico system and have no specific in vitro counterpart . Beyond simply modeling cystogenesis , a purpose of this research has been to instantiate an in silico system in which cells , matrix , and lumen have in vitro counterparts , and when executed the ISMA produces a variety of measurable phenomena that quantitatively mimic MDCK cystogenesis . At the systemic level , we have excellent cystogenesis similarity over ten days for multiple measures ( Figures 3-5 ) . Further analogue improvement will , following additional cycles of the IR Protocol , allow intracellular events to become concretized and increasingly fine-grained , thus enabling quantitative in silico-to-in vitro mappings at multiple levels . All specified events were necessary and essential for achieving targeted SMs . For cell-level events , the mappings are clear: they are direct and quantifiable . Intracellular events , axioms , and protocols are below the current level of resolution . There is no requirement that a specific intracellular event , axiom , or protocol has a cell-level counterpart . We simply hypothesize that the set of intracellular events , axioms , and protocols—a cell's operating principles—has an in vitro counterpart , as illustrated in Figure 9 . For some intracellular events , conceptual mappings are clear . Examples include cell initiates dying , death advances , and decrement polarCounter . For others , conceptual mappings are less clear . Examples include decrement shiftCounter ( in the TS ISMA ) , compute TP , and compute G . The expectation is that , in moving forward , as axioms are replaced by concrete , interacting components ( see [14] and the future experiments subsection below ) clear mappings will be easier to establish and quantify . A good example of a project in which intracellular events are incorporated and to some degree mapped back to those in vitro , is the IBCell model [15] , [16] . It is a biomechanical model of MCF-10A cell cystogenesis in which proteins on the outer cell membrane and the extracellular matrix are specifically simulated . The IBCell model successfully reproduced some aspects of cystogenesis , but it remains unclear whether the intracellular details are necessary or could be replaced by coarse-grained components . The quantitative data used to validate the model lacked the level of resolution necessary to falsify intracellular mechanisms . Surprisingly , cysts with little or no cell death can still be well organized with a single lumen . Reducing cell death rates ( Figure 7 ) altered cystogenesis details only marginally , primarily because cell death frequency was already low ( Figure 5B ) . Lin et al . [17] hypothesized that apoptosis is crucial for lumen formation in MDCK cysts , but they reached that conclusion based on observations of cystogenesis in collagen culture only . Martín-Belmonte et al . [9] observed that apoptosis within Matrigel cultures is less frequent than within collagen cultures . Within ISMA simulations , earlier lumen formation results in more organized cyst growth and fewer cells that die after losing contact with the matrix once lumens have formed . It is possible that apoptosis acts simply as a cleanup mechanism within MDCK cysts , but the degree to which it is utilized depends on the environment , the rate of cell growth , and the timing of polarization . Our experiments reducing the rate of cell death showed that although the rate of cell death within cysts during growth is normally quite low , cell death still contributes to controlling cell number and maintaining SLSL cysts . It is possible that environmental adjustments may provide conditions in which MDCK cell cystogenesis produces normal SLSL cysts without requiring cell death , as occurs in human alveolar type II epithelial cells [18] , [19] . Relative to cystogenesis in Matrigel , cells grown in collagen produce smaller cysts with fewer cells and delayed polarization . That delay might play a role in formation of smaller cysts . However , ISMA experiments showed that delaying polarization ( Figure 8 ) increased cell number and decreased the percentage of cysts with single lumens . We take those observations as strong evidence that delayed cell polarization alone is insufficient to account for that difference in cystogenesis within collagen and Matrigel cultures . As illustrated in Figure 9 , a goal is to build , expand , and validate in silico mechanistic networks that map to plausible causal linkages between intracellular details and features of MDCK cell phenotype in culture . A prerequisite is to have cells capable of achieving increasingly fine-grained and expanding coverage of MDCK cell , cluster , and cyst behaviors under different conditions . Advances in imaging technology have made doing so easier . Similar coverage will be needed of intracellular ( subcellular ) dynamics , including the behaviors of cell components under different conditions . We anticipate that studies of in vitro MDCK cell cystogenesis using high-resolution , time-lapse microscopy will reveal new behavioral details at each level . Recent studies have employed confocal time-lapse microscopy to understand lumen formation , but only imaged cells for eight hours [20] . Ewald et al . [21] set the standard for long-term time-lapse microscopy in their work on the elongation of mouse mammary ducts , in which they captured individual images every 15 minutes for five days , using high-sensitivity cameras to avoid phototoxicity . There is ample evidence that tension within the extracellular matrix influences epithelial cell behaviors [22] , [23] . Paszek et al . [22] demonstrated that increasing matrix stiffness resulted in tumorigenic behavior in MCF-10A cells . It seems reasonable to expect changes in MDCK cell , cluster , and/or cyst behaviors as Matrigel stiffness , density , and additives are changed . Experiments similar to those within [22] conducted with MDCK cells and for longer durations are needed to expand ISMA coverage of MDCK phenotype in important ways . Although the underlying in vitro molecular mechanisms to which the TS and LS ISMA map remain unclear , in vitro experiments may indicate one mechanism as being more plausible . Careful analysis of images generated through time-lapse microscopy is expected to be informative . If the elapsed time between individual cyst polarization and stabilization of division rate or mean cell size are similar between cysts , that would be supportive of an internal clock mechanism . However , if the interval varied between cysts , that would falsify such a mechanism . If mean lumen size when division rate and cell size have stabilized are similar between cysts , that would support the shift mechanism based on lumen size . Experiments are suggested in Supporting Text S1 to begin identifying potential molecular counterparts to TS and LS ISMA mechanisms . Five directions for in silico experiments present themselves . The first two require seeking contradictory or supportive literature evidence of in silico experiments . 1 ) Exploring the consequences of parameter changes will provide insight into ISMA's mechanism-phenotype relationships for which there may be biological counterparts [1] . A full suite of parameter change experiments was conducted using the LS-ISMA; results are presented in Figure S11 . One example is to explore the consequences of changing deathRateEpi and deathRateLumen ( Figures 7 and S11 ) , including setting both to 0 . Another is to vary lumenGrowthRate to explore the effect of increased or decreased lumen expansion on in silico cystogenesis ( see Figure S11 ) . Addition of any of several compounds to the culture media in vitro will stimulate cyst expansion . Examples include cholera toxin and forskolin . 2 ) Modify axioms and operating principles to simulate targeted mechanistic interventions . One example ( see Results ) is to modify the way in which cells calculate their axis of division . Another is to modify how matrix is represented in order to explore consequences of altered matrix properties on cystogenesis . Currently , matrix is simply a grid space state . Matrix could be represented using a CPM “cell” that offers resistance to cell advancement . So doing opens the door to exploration of a variety of matrix-cell interactions that could map to proteins altering local matrix properties . 3 ) Systematically expand the targeted attributes while keeping cells atomic . Movies , such as Video S1 from [9] along with the current literature , contain examples of many behaviors beyond the scope of the current ISMAs . Adding any one of the following to the list of targeted attributes will falsify the current ISMAs . At the cell level: when cells undergo mitosis , they enlarge temporarily and then return to a smaller size; some cells ( and cysts ) move around during the early stages of cystogenesis; some cells migrate toward each other and cluster together before initiating division; typically , when cells die in contact with matrix , they are flushed into the luminal space where they shrink and disappear . At the cyst level: cysts spin . The process was described in [20] and recently modeled in [24] . Cyst growth may have an additional later stage characterized by significantly slowed expansion , rather than continuing to grow steadily as predicted by the ISMA . The dynamics of lumen merging are more complex than the merging events that occur during simulations . Also , lumens change shape and move within cysts during the initial stages of growth . 4 ) Increase realism by transforming cells from atomic to composite objects . The axioms used by cells are placeholders for more fine-grained micromechanisms . The latter can be instantiated in future ISMA descendents . Before we can turn our attention to intracellular processes , we need new ISMAs in which cells are composite ( and eventually hierarchical ) analogues that can achieve essentially the same , targeted SMs as the current ISMAs ( Figures 2 and 3 ) . Previous reports [14] , [19] , [25] explained that an in silico analogue ( such as the current ISMA ) that quantitatively mimics many cell-level phenomena can be used to begin the sequential process of drilling down and establishing plausible , causal linkages between phenotype and molecular level details . Using cross-model validation procedures , the atomic cell is replaced by a composite cell where phenomenal axioms are replaced by concrete micromechanisms involving interacting objects that map to subcellular processes and/or components in the referent . 5 ) Once we have the preceding composite cells , we can expand the list of targeted attributes to include subcellular and intracellular behaviors . Alternatively , expanding the list of targeted attributes can require transforming cells from atomic to composite objects . Examples of subcellular and intracellular behaviors include the amount and location of polarization proteins , organelle movement , the organization of the mitotic spindle , formation of a pre-apical patch , location-dependent lipid compartments within the membrane , etc . During cell polarization ( as detailed in [8] ) , PTEN moves to the apical membrane , where it converts PIP3 to PIP2 , which binds to Anx2 and assists in the recruitment of Cdc42 to the apical membrane . The task at this stage , while adhering to a strong parsimony guideline , is to add new mechanisms and details that enable validation against the new , targeted attributes , while retaining all of those mechanisms and behaviors that enabled validation during earlier cycles of the IR Protocol . So doing will enable the in silico exploration , falsification , and validation of increasingly complex in vitro MDCK cell behaviors , which will ultimately correlate to in vivo phenotypes of developing epithelial organs . We hypothesize that the local cause-and-effect relationships ( mechanisms ) occurring in ISMAs during execution , and thus their morphogenic agenda , have in vitro counterparts . Challenging these alternative hypotheses can be a focus for future in vitro experiments and ISMA refinements . Through careful application of the IR Protocol , analogues of MDCK cystogenesis in cultures ( ISMAs ) were developed , falsified , refined , and validated against novel , multi-attribute quantitative data . ISMAs were based on software specifications that enabled in silico behaviors during simulation to achieve degrees of validation: to be mapped quantitatively to measures of cystogenesis ( targeted attributes ) . Those specifications also enabled hypothesizing that ISMA operating principles , axioms , components , events , and mechanisms have in vitro counterparts . Predictions of substantive mechanistic changes were verified by independent experiments . ISMAs were used to explore and test hypotheses about cell and cyst dynamics . The above , coupled in vitro and in silico experiments led to four insights . 1 ) The axis of cell division significantly affects lumen number without changing cell number or cyst size . 2 ) Reducing the amount of luminal cell death had limited effect on cystogenesis . 3 ) Later stages of cystogenesis , marked by a decrease in the rate of cell division and cessation of the decrease in mean cell size , can be explained by the presence of a new cell state ( called stabilized ) , which differs in a few key behaviors . 4 ) The same , multi-attribute phenotype can be a consequence of two fundamentally different mechanisms that , in silico , only alter the mechanism of cell stabilization . By providing a new way of thinking about cystogenesis , ISMA simulations have provided an impetus to explore novel aspects of epithelial morphogenesis .
A single cell suspension of MDCK cells was plated in duplicate on a layer of 100% Matrigel basement membrane ( BD Biosciences ) in the presence of 2% Matrigel in the media . Cysts were allowed to grow for the indicated duration then fixed with 4% paraformaldehyde . The cells were then stained as described in [11] , [26] . Briefly , cells were stained with a monoclonal antibody against gp135/podocalyxn , and a polyclonal antibody against β-Catenin . F-actin and nuclei were stained with Alexa-labeled phalloidin and Hoechst 33342 respectively . Each day , 20 cysts from the duplicate plates were selected at random and imaged using a Zeiss 510 laser scanning confocal microscope ( Carl Zeiss Inc . ) . Images were acquired sequentially in four separate channels . Cell number was determined by counting the nuclei , when visible , and actin borders when not . Cyst and lumen perimeter were traced using ImageJ and the size of the cyst and lumen within each cross section was calculated using the analyze tool . Cellular area was found by subtracting lumen area from cyst area; mean cell area was found by dividing cellular area by the number of cells; and the ratio of cellular area to cyst area was found by dividing cellular area by cyst area . Standard deviations and Similarity Measure values ( defined in Results ) were calculated using R . The number of lumens in each cyst was found by counting the discrete spaces within the cyst bordered by gp135/podocalyxn and actin . The data generated by the in vitro experiments was quantitatively consistent with results from previous studies [6] , [9] , [27] , as well as being internally consistent . The goal of conducting the in vitro experiments was to provide a particular quantitative perspective on MDCK cystogenesis . We sought an abstract mechanistic explanation of one set of cytogenic trajectories . Repeated in vitro experiments using a different batch of cells could result in distinct cytogenic trajectories , which might not be explained by the current ISMAs . Understanding and simulating such different trajectories is outside the scope of this project . An early task in any modeling effort is to state near- and long-term uses; one must then strive to follow a model development path intended to achieve those uses . When dealing with biology , having explanatory mechanistic models necessarily precedes having predictive mechanistic models . This project is an important , early step in developing explanatory mechanistic models of cystogenesis . A truly useful explanatory mechanistic model is one in which we can observe putative cause-effect events at several layers as they unfold . Given those considerations , we envisioned six near-term ISMA uses . 1 ) Instantiate and challenge hypotheses about mechanisms of cystogenesis by MDCK cells under different culture conditions . 2 ) Make it easy to follow mechanistic processes and trace cause-effect relationships . 3 ) Achieve measures of cystogenesis during ISMA executions of increasingly autonomous cells that are quantitatively similar to referent measures ( i . e . , they achieve targeted SMs ) . 4 ) Achieve increasing overlap of an MDCK cell culture's phenotype by an ISMA phenotype . 5 ) For validated ISMAs , explore the consequences of mechanistic interventions on measures of cystogenesis . 6 ) Expose possible gaps in our knowledge of MDCK cell cystogenesis . Implicit in these uses is the ability of ISMA behaviors under different conditions to stand as predictions of MDCK cell and cyst behaviors under comparable conditions . The preceding are prerequisites for achieving six long-term ISMA uses . 1 ) Enable replacing ISMA operating principles with concrete mechanisms composed of interacting components . So doing is required to enable hierarchical linkage of molecular level details with specific phenotypic attributes . 2 ) Execute in silico experiments that test the effect on ISMA cystogenesis of simulated chemical and genetic interventions that affect cell behaviors . 3 ) Enable continuous refinement of increasingly trustable , complex , biomimetic mechanisms that stand as plausible explanations for increasingly large sets of multi-attribute , multi-source wet-lab data . 4 ) Represent uncertainty at multiple levels , including uncertainty in mechanistic hypotheses; provide plausible representations of sources of variability in referent data and phenomena . 5 ) Enable straightforward redeployment and adaptation of ISMA components to represent other cell types and their behaviors; examples include MCF-10A and primary mouse breast organoids . 6 ) Enable concrete translations between in vitro knowledge and epithelial diseases such as autosomal dominant polycystic kidney disease and cancer . Components and mechanisms mapped as closely as possible to components and mechanisms in the referent system . ISMAs were composed of cells , luminal space , and extracellular matrix . We set parameters such as the rate of cell division and the initial size of cells to map to quantities within the in vitro system . Simulation began with 2-4 cells ( to mimic the observed number of initial cells in vitro ) on a 2D 100×100 hexagonal grid . Cells expanded in size and divided using the CompuCell3D [28] cellular Potts model architecture and customized code . Each cell occupied multiple locations on a hexagonal grid , thus allowing cells to expand , divide , change shape , and move in a realistic manner ( Rejniak et al . [16] used an alternative method for enabling cell shape change ) . We coupled that with features of the agent-oriented modeling approach used successfully by [14] , [29]-[31] . Each cycle , cells stepped through the same decision flow ( Figures 10 and S12 ) ; they applied the operating principles described below to change shape , divide , change state , create lumens , and die . Logic design and implementation was constrained by the specifications in Table 1 . Note that cells are atomic objects: they have no internal parts . All of their micromechanisms are in the form of axioms . Some axioms add behavior variability to ISMAs , as noted in Table S2 . Except as noted , simulations ran using the parameter values in Table 2 . A simulated day mapped to an in vitro day and consisted of 48 simulation cycles , equivalent to 30 minutes per cycle . Drawing on several years of prior experience experimenting on MDCK cultures , we specified that when SM1 ( defined in Results ) >0 . 5 for nine of ten days , the results can be considered to be within the range of experimental and biological variability . Specifically , when SM1 was achieved , simulation results were taken to be experimentally indistinguishable from values obtained from an independently repeated in vitro experiment . Empirical parameter tuning was used to obtain frequencies of SLSL cysts comparable to that observed in vitro . When SM targets were not achieved , that specific mechanism was falsified . SMs also allowed for ISMA validation and falsification when new attributes were added to the target list ( discussed below ) . The Iterative Refinement Protocol ( IR Protocol ) , described in [14] , [19] , [25] , [29] , provided the foundation of our methods . Based on the results of prior experiments and literature review , we selected an initial group of qualitative attributes to target and simulate ( the first few in Table 1 ) . We implemented a simple ISMA that reproduced them , thus achieving an initial degree of validation . We then added new data , expanding the set of targeted attributes . So doing falsified the simple analogue . That judgment was based on observation ( for qualitative attributes ) and values of the prespecified SMs ( for quantitative attributes ) . The manner in which the first analogue was falsified informed us how to develop an improved version that would survive falsification . During subsequent cycles , we added new data or features from Table 1 to the targeted set . So doing often resulted in falsification of the then-current ISMA . On some occasions , it was clear that an incrementally more fine-grained set of mechanisms and/or components would be needed to achieve the specified SMs . On other occasions , we undertook an empirical search of parameter space in search of new sets of parameter values that would reestablish validation . When that search failed , new mechanisms , sometimes more fine-grained , were developed . That iterative process ended with the attributes in Table 1 and the corresponding in silico specifications . The IR Protocol has a number of benefits . Chief among them is that once an ISMA is validated against targeted data , additional data can be added and the analogue reengineered without invalidating existing mechanisms . The new data will falsify the current ISMA by design , but a successful revision will survive falsification by both new and existing data . Because in silico components and mechanisms map to their in vitro equivalents , it is often the case that only a subset of ISMA components and/or operating principles must be modified to mimic both new and original phenomena . Examples include adding a new cell state and replacing one axiom with two more specific axioms . Because of the networked nature of all mechanistic details , each ISMA change requires some retuning of the parameterizations of several already existing ( unmodified ) ISMA features . The IR Protocol consists of the following steps: first , specify a list of targeted attributes , which forms the basis for experimental hypotheses . Devise a specification that maps in silico components and operating principles to cell culture counterparts . The operating principles are expected to enable cells to exhibit behavior that is closely analogous to that observed in vitro . Implement the analogue in code and execute it to deduce predictions about the in silico and in vitro system . As stated in [29] , analogue execution is a form of deduction , where the behavior of the analogue follows logically from the premises embodied by its initial conditions and input data . In some cases , this deduction will yield obviously invalid results , which falsifies the current list of operating principles and prompts the modification of mechanistic hypotheses . Once the analogue cannot be falsified by data specific to the current list of targeted attributes , add one or more new , targeted attributes and repeat the IR Protocol . The process facilitates mechanism exploration , leading toward deeper insight into biological counterparts . Undertaking a series of tightly coupled in silico and in vitro experiments further increases the confidence that the results of ISMA intervention experiments can stand as useful predictions of MDCK counterparts . When there is sufficient ISMA and MDCK cystogenesis similarity , we hypothesize there is corresponding mechanistic similarity . Consequently , results of ISMA intervention experiments will stand as predictions of in vitro phenomena following corresponding in vitro interventions . Some of those predictions will merit in vitro follow-up . An advantage of using targeted attributes and specifications is the flexibility of their implementation . We chose to implement the ISMAs using an agent-oriented approach as explained below and described in [25] , but their key aspects include object-orientation , component mapping , spatial orientation , relational grounding and striving for component autonomy . Agent-oriented models are frequently implemented using object-oriented programming techniques , which allow the designer to create individual computational objects corresponding to agents and components within the specification . Components and mechanisms are mapped to analogous components and mechanisms within the referent . So doing makes translating in vitro and in silico observations back and forth more intuitive and less complex . Individual agents can serve as analogues for in vitro components . Agents are quasi-autonomous and they possess their own internal control flow and execute actions independent of enclosing agents . Grounding is defined as the units , dimensions , and/or objects to which a variable or model constituent refers . When grounding is relational , variables , parameters , and I/O are in units defined by other model components . When grounding is absolute , variables , parameters , and I/O are in real-world units like seconds and µg/ml . One advantage of using an agent-oriented approach with relational grounding [25] is that fewer assumptions are required to create or validate the ISMA , and those that are must be clearly specified . The ISMA contains five agents: ISMAs were developed using the CompuCell3D ( CC3D ) architecture [32] , [33] , an implementation of the Glazier-Graner-Hogeweg [34] or cellular Potts model ( CPM ) . A CPM “cell” is not limited to a one-to-one correspondence between objects and grid locations . The CPM extends cellular automata so that each grid location contains an index specifying which simulation object contains that location . A CPM with 100 grid locations can contain anywhere from 1 to 100 cells . This modification allows simulations to address cell size , shape change , and cell-cell adhesion . During a simulation cycle , the Potts agent calls a pseudorandom index change algorithm that randomly selects a user-specified number of locations and evaluates whether each will remain indexed to its current cell or change to be indexed to another cell . If the location remains indexed to the current cell , the grid remains unchanged . When a location's index changes , that location and the “energy” of the system are updated . To calculate whether a location changes index from one cell to another , ΔG is calculated; it is the change in “energy” if that location changes its index to the new cell . An acceptance function generates a probability p based on the value of ΔG , and then checks if the pseudorandom number r[0 , 1]<p . When r<p , the change is accepted and the location is assigned to the new cell , and if not the change is rejected . When accepted , the energy of the system changes . It calculates the value of Gnew and Gold using a Hamiltonian equation: Each of these terms is calculated through a separate equation , detailed below . The energy calculation for EnergySurface depends on LambdaArea ( λA ) and the difference between the target surface area ( TA ) and the current surface area ( A ) : The larger LambdaArea is the more changes in TA will affect the overall energy of the system and the faster these changes will be reconciled . LambdaArea for cells is a user-set parameter , while for lumen it is fixed at 20 to represent the large outward force of the expanding lumen . The calculation of EnergyPerimeter is similar: The “energy” of adhesion depends on the cell type and its location . For location ( i , j ) , the energy is the sum of values calculated between ( i , j ) and all neighboring points residing in separate cells . If , for example , two of the six neighboring points reside in another cell , then the energy of adhesion would be 2·X1–2 , where X1–2 is a parameter controlling the adhesion energy between cells of type 1 and type 2 . Separate adhesion energy parameters are specified for each pair of cell types ( Table S3 ) . The “energy” of connectivity is generally 0 , but if changing the cell index of a location results in a location being isolated from the rest of the cell , an energy penalty is assessed by setting EnergyConnectivity to be very large . As a result , cells cannot split into pieces except when they undergo cell division . In addition to maintaining connectivity between all points in a cell , an ISMA maintains integrity between tight junctions , preventing them from being remodeled in the index change step during a simulation cycle . If the ISMA detects that the change in a point would result in a tight junction being remodeled , it assesses an energy penalty by setting EnergyConnectivityi to be very large . A detailed explanation of tight junction remodeling is provided in Text S1 . CC3D is designed from a system-based perspective . Each simulation cycle , each aspect of the system is executed , from the index change step that selects random points , to the plug-ins that update aspects of the system . CC3D was not designed from an agent-oriented perspective , so it was necessary to expand it to gain required capabilities . MCell objects were added to cell objects to create a bi-directional mapping between individual points and the cells that contained them . These objects and their control flow were executed in sequence by the MDCK plug-in to grant full agency to cells , which previously only executed after a location within the cell boundary changed its index . Every simulation cycle all points in the grid are surveyed to assess which cell they are indexed to and a reference is stored in an MCell object corresponding to that cell , as shown in Figure 11 . Thereafter that MCell can be queried to find out what points are located within its corresponding cell object . The version of CompuCell3D used to develop this project has been superseded ( see Text S1 ) by the current available version . The capabilities provided by the current version were not judged necessary for the ISMA , especially due to the significant addition of custom code . The project was not adapted to the updated version . As shown in Results , we observed that prior to cells stabilizing in vitro , their size correlated with the size of the cyst and its cell number . We hypothesized that operation of yet-to-be identified micromechanisms provides each cell with a target size . We speculated that a cell might use information such as the tension between it and neighboring cells , lumen pressure , and the ratio of lumen and matrix contact area in order to update its target size . To mimic the decrease in mean cell area observed in vitro , we developed and used an algorithm that is a placeholder for yet-to-be-designed , concrete micromechanisms that can be implemented in a future ISMA . Each individual cell adjusted its size and shape so that a target area W , the projected wedge area ( a wedge that includes the portion of the perimeter in contact with matrix and terminates at the cyst center ) , would move toward or equal an ideal value . The parameter wedgeArea was a value based on the early ( pre-stabilization ) 169 µm2 area observed in vitro . An ISMA calculated W using the following formula: A is the area of the cell , M is one-half the number in cell grid edges in contact with matrix , and L is one-half the number in cell grid edges in contact with lumen . This formula assumes that cysts are somewhat circular . Variations in actual cell size caused by non-circular cysts resulted in variance in cell area similar to that observed in vitro . The cell subtracted W from wedgeArea and set its target change in area to the resulting value ( with a final maximum value of wedgeArea ) . Use of this algorithm during early simulation cycles caused mean cell area to decrease and cyst area to increase , mimicking observed in vitro data ( Figure 2C ) . Once cells stabilized , they no longer used the above equation . Instead , cells strove to maintain an area that increased only slightly as contact with the lumen increased . We speculated that cells within cysts in vitro must maintain a minimal cell height even as they are stretched by the expanding lumen . We specified that ISMAs use a similar guideline . From in vitro observations , it seems likely that cells have genetic and environmentally imposed targets for the areas occupied by different surfaces ( cell-cell interfaces , basal , and apical ) . We specified that 2D cells have a target perimeter value ( TP ) that is computed using the cell's current area . For simplicity , we specified that a cell compute TP using the perimeter P of a circle having an area A equal to its own: K is a scaling factor and multiplier is user-specified . Two cells having identical areas will have identical TP values , so if one has a larger P the difference between P and TP will also be larger , causing that cell to move toward circularity faster . The value of polarCounter was set to equal a pseudorandom value r[polarDelay · 0 . 75 , polarDelay · 1 . 25] when a cell first contacted matrix . Thereafter , it decreased by one each simulation cycle . Upon reaching 0 , cell state changed from unpolarized to polarized . Consequently , polarCounter is the cell's counterpart to a cell , having established matrix contact , changing and moving around its components in a process that ends when tight junctions have formed and the apical surface is isolated and complete . A correlation was observed between mean cell size and the rate of cell division in vitro , but a causal link was not apparent . Individual cells may sense the area of matrix contact in part through β1-integrin signaling [26] . They may sense the area of lateral cell-cell contact in part using catenins and cadherins [35] . That information may influence whether a cell divides or not . As stated in Discussion , tension transduced by the subapical F-actin network could allow cells to sense the size of the lumen . Such information supported our decision to use lumen size as a signal for cell stabilization . Each simulation cycle , a cell bordering matrix and lumen queried the lumen for its size . When that value ÷ 1000 was greater than the parameter stableRatio , the cell changed to the stabilized state . Decrementing cycleCounter is a cell's counterpart to moving through the phases of the cell cycle . CycleCounter is a variable that is initialized based on cellCycle ( a user-specified parameter that controls the duration of the cell cycle ) and decremented thereafter . Cells implemented the following method of cell division . For the first cell and for daughter cells after division , the value of cycleCounter was set to a pseudorandom value r[0 . 75·cellCycle , 1 . 25·cellCycle] and then decremented in each cell in every simulation cycle in which the cell had an area > doublingArea/2 . When cycleCounter reached zero , a cell divided ( Figure 12 ) , splitting its area in half on an axis , and using the parameter divisionReg to determine the method of calculating the axis of division . When divisionReg = 0 , cells chose the axis of division randomly . If it was 1 , cells used oriented division , finding their axis of division as shown in Figure 12 . Cells recorded the location of their midbody as a point . When dividing , the cell connected the midbody and the centroid with a line . The cell assigned all points above the line to a new cell and all points below to the old cell . It then set the midbody of the both cells to the centroid of the just-divided cell . When divisionReg = 2 , cells divided randomly until they reached the polarized state and then used oriented division . DivisionReg = 3 specified reversed division , where cells would find the axis of division as stated above , but then add 90 degrees , reversing division orientation . After a cell divided , the value of cycleCounter for both daughter cells was reset to a new random value as detailed above . Its value of polarCounter did not change . The new cell inherited all values from the parent cell , except polarCounter , which was set to r[0 . 5·polarDelay , 1 . 5·polarDelay] – polarDelay + polarCounter ( parent ) . So doing made the newly created cell have a polarCounter value close to but not identical to that of the parent cell . In vitro data analysis revealed that when the cultures began growing , the mean in vitro cell number was about 2 ( Table S4 ) , indicating that a small amount of clustering took place after the cells were plated . That was expected because in Matrigel culture suspended cells settle on the layer of 100% Matrigel and thus most cysts grow in the same plane . Accordingly , simulations began with a single cell , but at simulation cycle 1 , the cycleCounter of that cell was reduced to 1 , causing it to divide during the following simulation cycle . In addition , since in vitro cells are not always at the beginning of their cell cycle when plated , the value of cycleCounter for the two cells was changed to equal a pseudorandom number r[ ( 1 – clusterProb ) x cellCycle , cellCycle] . So doing allowed the amount of clustering to be increased without changing the cell division rate , simply by increasing clusterProb . Cell death is an important factor in MDCK cystogenesis . However , it is not clear that it is required for cyst formation . In order to validate that cysts did not ignore or excessively rely on cell death for normal lumen formation , the amount of cell death observed in silico was quantified and compared to that observed in vitro . In vitro analysis of cell death was conducted in [9]: MDCK cysts were cultured as in this report and fixed and stained with an antibody for activated caspase-3 ( cleaved in apoptotic cells ) . Within the ISMA , a cell began dying when a pseudorandom number r[0 , 1] was less than deathRateEpi if the cell contacted matrix or deathRateLumen if it did not . Once a cell entered the dying state it shrank until its area reached zero . It was then removed from the simulation . Each day , the number of cysts with dying cells was recorded and the percentage calculated ( Figure 5 ) . The data was separated based on whether cells were in contact with the matrix or not . Drawing on literature evidence [36]-[38] and expert opinion we estimated the average time between apoptotic bodies first being visible and a dying cell breaking up into pieces to be roughly five hours . The value of the parameter dyingShrinkRate specified the amount that the TA of a dying cell was lowered each simulation cycle ( Table 2 ) . Mean dying time ranged from 6 . 5 to 13 simulation cycles , with an overall mean value of 9 . 2 , which maps to 4 . 6 hours when one simulation cycle is grounded to 30 minutes . Polarized cells create a new lumen when two conditions are met . 1 ) The cell contacts matrix , but is not in contact with an existing lumen . 2 ) The location chosen for lumen creation is adjacent to another polarized cell also not in contact with an existing lumen . The point chosen for lumen creation is the cell's midbody ( Figure 12 ) , which was the centroid of the parent cell that previously divided to create the current cell . Lumen formation involves cells creating and secreting fluid . Having cells create and release units of lumen content could simulate that . One unit could correspond to a single grid space . Those units could merge with other units or with an existing lumen object . However , absent validation evidence for the other ISMA mechanisms , implementing such a fine-grained ( somewhat complicated , multi-parameter ) mechanism simply because it seems biomimetic would have been contrary to the IR Protocol's strong parsimony guideline . We took advantage of CC3D capabilities and elected to use a more abstract , simpler approach . There is no disadvantage in doing so because a strength of this class of analogues is that a simple mechanism that achieves a degree of validation can later be replaced with a more detailed and realistic counterpart . Using cross-model validation [25] , this can be done without compromising other ISMA mechanisms that have also achieved degrees of validation [14] . Within ISMAs , lumens are a different class of “cell” object . Their only action options are to expand and merge . After a lumen is created , it expands using the following axiom . LumenGrowthRate is a user-specified parameter; estimatedArea is the area of cells in contact with the lumen added to the lumen's area; totalNeighbors is the number of cells in contact with the lumen; and lgrSubtract is a quantity based on a user-specified parameter and the degree cells are stretched . Cells that are more stretched have a higher lgrSubtract value , reducing the rate of lumen expansion . A lumen does not have a target perimeter value—its perimeter is determined entirely by the perimeter of the cells surrounding it . Lumens can merge when their tight junctions are reorganized . Tight junctions ( TJs ) were implemented in order to simulate aspects of MDCK lumen expansion . TJs exist where two cells contact each other and a lumen . A TJ is two points—one in each neighboring cell—adjacent to a point within a lumen ( see and Figure S13 ) . TJs control lumen expansion and merging and prevent cells from contacting multiple lumens . At the end of a simulation cycle , when a TJ is adjacent to a different TJ , the TJs are reorganized and the index of the two TJ points is transferred to the neighboring lumen . Then the two lumens , now in contact with each other , merge together . In addition , TJs can reorganize to allow lumen expansion . To do so , at the end of a simulation cycle all points within TJs execute the following algorithm . A TJ point first surveys its neighboring points to verify they are not in contact with another lumen and that they are not in the matrix or within an unpolarized cell . It then determines if any of its neighboring points are in different cells but are not also TJs . To reorganize , the TJ point computes the free energy change if its index changes to the neighboring lumen and then uses the acceptance function to accept or reject that change . If the change is accepted , the TJ becomes lumen , and the neighboring point becomes a new TJ . We recorded aspects of in vitro cyst growth by obtaining cross-sectional images taken through the center of cysts . These images were necessarily a 2D representation of a 3D structure . Based on the symmetry observed within the cross section , in addition to separate analysis of 3D structures , we believe that cysts were roughly symmetrical in 3D . Using this information , we extrapolated 3D values for total cell number , cyst volume , and lumen volume from the measured values of cross-sectional cell number , cyst area , and lumen area . We found that the trends observed for cell number and mean cell area held when the system was projected into 3D . If future targeted attributes required specific modeling in 3D , we could take advantage of the 3D capabilities of CompuCell3D , addressing considerations raised in [39] . The in silico system recorded data about cells and cysts into a MySQL database as specified within Text S1 . | Epithelial cells perform essential functions throughout the body , acting as both barrier and transporter and allowing an organism to survive and thrive in varied environments . Although the details of many processes that occur within individual cells are well understood , we still lack a thorough understanding of how cells coordinate their behaviors to create complex tissues . In order to achieve deeper insight , we created a list of targeted attributes and plausible rules for the growth of multicellular cysts formed by Madin-Darby canine kidney ( MDCK ) cells grown in vitro . We then designed in silico analogues of MDCK cystogenesis using object-oriented programming . In silico components ( such as the cells and lumens ) and their behaviors directly mapped to in vitro components and mechanisms . We conducted in vitro experiments to generate data that would validate or falsify the in silico analogues and then iteratively refined the analogues to mimic that data . Cells in vitro begin to stabilize at around the fifth day even as cysts continue to expand . The in silico system mirrored that behavior and others , achieving new insights . For example , luminal cell death is not strictly required for cystogenesis , and cell division orientation is very important for normal cyst growth . | [
"Abstract",
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] | [
"physiology",
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] | 2011 | MDCK Cystogenesis Driven by Cell Stabilization within Computational
Analogues |
Kaposi’s sarcoma associated herpesvirus ( KSHV ) persists in a highly-ordered chromatin structure inside latently infected cells with the majority of the viral genome having repressive marks . However , upon reactivation the viral chromatin landscape changes into ‘open’ chromatin through the involvement of lysine demethylases and methyltransferases . Besides methylation of lysine residues of histone H3 , arginine methylation of histone H4 plays an important role in controlling the compactness of the chromatin . Symmetric methylation of histone H4 at arginine 3 ( H4R3me2s ) negatively affects the methylation of histone H3 at lysine 4 ( H3K4me3 ) , an active epigenetic mark deposited on the viral chromatin during reactivation . We identified a novel binding partner to KSHV viral DNA processivity factor , ORF59-a protein arginine methyl transferase 5 ( PRMT5 ) . PRMT5 is an arginine methyltransferase that dimethylates arginine 3 ( R3 ) of histone H4 in a symmetric manner , one hallmark of condensed chromatin . Our ChIP-seq data of symmetrically methylated H4 arginine 3 showed a significant decrease in H4R3me2s on the viral genome of reactivated cells as compared to the latent cells . Reduction in arginine methylation correlated with the binding of ORF59 on the viral chromatin and disruption of PRMT5 from its adapter protein , COPR5 ( cooperator of PRMT5 ) . Binding of PRMT5 through COPR5 is important for symmetric methylation of H4R3 and the expression of ORF59 competitively reduces the association of PRMT5 with COPR5 , leading to a reduction in PRMT5 mediated arginine methylation . This ultimately resulted in a reduced level of symmetrically methylated H4R3 and increased levels of H3K4me3 marks , contributing to the formation of an open chromatin for transcription and DNA replication . Depletion of PRMT5 levels led to a decrease in symmetric methylation and increase in viral gene transcription confirming the role of PRMT5 in viral reactivation . In conclusion , ORF59 modulates histone-modifying enzymes to alter the chromatin structure during lytic reactivation .
Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , also known as human herpesvirus 8 ( HHV8 ) , is a member of the gammaherpesvirus family that is associated with Kaposi’s sarcoma ( KS ) , Primary Effusion Lymphoma , a subset of Multicentric Castleman’s Disease , and ( in HIV-co-infected patients ) KSHV Inflammatory Cytokine Syndrome [1–4] . KSHV is a double-stranded DNA virus with a large genome that encodes for over 87 open reading frames ( ORFs ) including genes necessary for capsid , tegument , envelope , DNA replication and regulatory proteins . KSHV undergoes a bi-phasic lifecycle , common to other herpesviruses , that features both latent and lytic modes of infection . The virus persists indefinitely in the infected host in a latent form during which time only a small fraction of regulatory viral proteins are expressed , most notably the latency-associated nuclear antigen protein [5–7] . In the latent stage , LANA regulates latent genome replication and tethers the circular viral episomes to the host chromosomes to ensure the segregation of KSHV episomes to daughter cells upon cell division [8–11] Additionally , LANA modulates several signaling pathways to suppress the host immune antiviral responses to induce cell growth and survival [12–17] . During latency , the KSHV genome is maintained primarily in a heterochromatic conformation in which the genome is highly compact with restricted transcription of the viral genes [18 , 19] . Specific ‘repressive’ epigenetic marks on the viral heterochromatin that contribute to the stability and tight regulation of gene expression include trimethylation of lysines 9 ( H3K9me3 ) and 27 ( H3K27me3 ) on histone H3 , ubiquitination of lysine 119 of histone 2A ( H2AK119Ub ) , and CpG-methylation [20] . The compactness of KSHV chromatin during latency was confirmed by sequencing the nucleosomal depleted DNA in FAIRE ( Formaldehyde-Assisted Isolation of Regulatory Elements ) assays , which revealed that only a small percentage of the viral genome , primarily the latency-associated regions , were in an active chromatin ( euchromatin ) state [18 , 21 , 22] . Latent viral genomes reactivate upon transcription of viral genes in a synchronized cascade of immediate early ( IE ) , early ( E ) , and late ( L ) genes , which leads to the production of infectious virion particles . Control of lytic reactivation is governed by the presence of both activating and repressive marks on the viral chromatin [19 , 23 , 24] . These are particularly important for certain regulatory regions of the KSHV genome with a bivalent chromatin structure because the balance between repressive and activating marks can tilt the scale for latency or reactivation [25 , 26] . For example , the promoter of immediate early gene , RTA exists in a bivalent chromatin structure as it simultaneously possesses both activating , H3K4me3 and repressive , H3K27me3 marks [22] . Balance between these epigenetic marks is determined by multiple host cellular and viral factors [19–21 , 25] . There is a growing list of epigenetic marks important for regulating chromatin structure and gene regulation [27] . One of the less studied epigenetic marks is arginine methylation , which is carried out by either type I or type II protein arginine methyltransferases ( PRMTs ) [28 , 29] . PRMT5 is a type II methyltransferase that symmetrically dimethylates arginine 3 of histone H4 , H4R3me2s [30–32] . PRMT5 mediated symmetric dimethylation of H4R3 promotes tri-methylation of histone , H3 lysine 27 ( H3K27me3 ) , a repressive mark that is one hallmark of compact chromatin [33] . In addition , knockdown of a related type II PRMT ( PRMT7 ) is associated with a reduction in symmetric methylation of H4R3; moreover , reduced H4R3me2s levels corresponded to an increase in the activating , H3K4me3 marks [34] . The catalytic domain for the methyltransferase activity of PRMT5 was identified to be in Motif I ( GAGRGP ) , and is essential for symmetrically methylating arginine 3 of histone H4 [31 , 35] . PRMT5 has multiple versatile roles in cell growth and development and its association with specific binding partners influences substrate specificity and subcellular localization [29] . In the cytoplasm , pICln associates with PRMT5 and facilitates the methylation of Sm proteins , which increases their affinity for the SMN ( survival of motor neuron ) protein and facilitates proper assembly of the spliceosome for the formation of snRNPs [36 , 37] . Another study of co-factors influencing substrate specificity of PRMT5 by Guderian et al . showed that PRMT5 bound to pICln or RioK1 recruits and symmetrically methylates nucleolin ( a RNA binding protein ) [38] . One of the cofactors important for regulating the histone methylation specificity of PRMT5 is MEP50 ( also known as Wdr77 ) [39] . In addition to MEP50 , another co-factor that regulates specific methylation of H4R3 by PRMT5 is a cooperator of PRMT5 ( COPR5 ) , which was identified in a yeast two-hybrid assay [40] . Recruitment of PRMT5 through COPR5 leads to a preferential symmetric dimethylation of histone H4R3 [40] . Importantly , symmetric dimethylation of H4R3 at the chromatin is associated with a compact chromatin causing transcriptional silencing [30 , 41] . ORF59 is an early viral protein expressed within the first 24h of viral reactivation and acts as a processivity factor for the viral DNA polymerase during lytic DNA replication [42–44] . ORF59 homologs from other herpesviruses including human cytomegalovirus ( hCMV ) and Epstein-Barr virus ( EBV ) ppUL44 and BMRF1 , respectively , are shown to be essential for lytic DNA replication [45] . The processivity factor dimerizes in the cytoplasm , binds to the viral DNA polymerase , and translocates it into the nucleus to assemble at the origin of lytic DNA replication ( OriLyt ) [46–49] . We recently reported that the phosphorylation of Ser378 and Ser379 is critical for ORF59’s activity , viral DNA synthesis , and virion production [50] . Although ORF59 was classically viewed as a processivity factor , studies have identified additional functions for ORF59; for example , ORF59 hinders the non-homologous end joining ( NHEJ ) repair of DNA double-stranded breaks by blocking the interaction between DNA-PKcs and the Ku complex during lytic replication to promote tumorigenesis [51] . Another report showed that ORF59 interacts with poly ( ADP-ribose ) polymerase 1 ( PARP-1 ) and stimulates its proteosomal degradation to block the PARP-1 mediated cell cycle control and apoptosis [52] . In this study , we identified ORF59 binding proteins by immunoprecipitating the Flag epitope tagged ORF59 in BAC16 and classified bound proteins by mass spectroscopic analysis . The assay identified few viral proteins and a large number of cellular counterparts including RNA processing proteins and chromatin modifying enzymes . Here , we determined the association and significance of PRMT5 on viral chromatin and our data show that PRMT5 depletion in latent cells can trigger the transcription of lytic genes , suggesting PRMT5’s role in altering the chromatin landscape . Depletion of PRMT5 was linked to a reduced level of symmetric dimethylation of H4R3 ( repressive mark ) on viral chromatin , which correlated with lower H4R3me2s amounts detected during viral reactivation . Decrease in symmetric methylation during reactivation was linked to decreased levels of PRMT5 bound to the viral chromatin that facilitated the formation of euchromatin for active gene transcription . Our results confirmed that ORF59 competitively removed PRMT5 from its linker molecule , COPR5 , which alters its specificity to symmetrically methylate H4R3 . Furthermore , the loss of H4R3me2s marks lead to an increase in the tri-methylation of H3K4 ( H3K4me3 ) , an activating mark , and confirmed the role of symmetric methylation in regulating chromatin landscape . Taken together , we propose a mechanism by which ORF59 disrupts PRMT5’s mediated compact chromatin leading to the formation of open chromatin important for lytic replication .
In order to confirm the binding of PRMT5 with ORF59 , we performed co-immunopreciptation assays on endogenous as well as over expressed proteins . First , the ORF59-HA and PRMT5-Flag expression vectors were transfected into 293T cells and an immunoprecipitation with anti-Flag antibody for PRMT5 co-precipitated the HA-tagged ORF59 ( Fig 2A , lane 4 ) . Lack of a detectable band of ORF59-HA with control vector ( Flag ) , confirmed that these proteins associate specifically ( Fig 2A , lane 3 ) . Furthermore , the reverse CoIP in which PRMT5-Myc with ORF59-Flag or Flag vector were expressed and immunopreciptiated with anti-flag antibody , showed specific precipitation of PRMT5-Myc ( Fig 2B , lane 4 ) . Flag Vector was unable to precipitate PRMT5-Myc ( Fig 2B , lane 3 ) , which confirmed specificity of the assays . Next , we used KSHV-infected cell lines , TRExBCBL1-RTA and iSLK . 219 to affirm this finding in an endogenous system . Each cell line was induced for lytic replication by treatment with doxycycline and ORF59 was immunoprecipitated with anti-ORF59 antibody . Immune detection of PRMT5 in ORF59 precipitated lanes showed a distinct band in both , TRExBCBL-1 and iSLK . 219 cell lines and confirmed the association of these two proteins during lytic reactivation ( Fig 2C and 2D , lanes 3 ) . The control antibody , IgG did not co-precipitate any PRMT5 , which again confirmed specificity of the assay ( Fig 2C and 2D , lanes 2 ) . After we confirmed the binding between these two proteins , we were interested in determining whether they localize to the same nuclear compartment during viral reactivation . To test this , we performed immune localization of these proteins in doxycycline induced TRExBCBL1-RTA cells . These proteins were detected using respective antibodies . Both ORF59 and PRMT5 are nuclear proteins that showed nuclear localization , as expected , and many of these foci showed localization of both proteins in the same nuclear compartment ( Fig 2E , merge panel ) . Latent ( uninduced ) cells do not express ORF59 , therefore undetected , but the subcellular localization of PRMT5 was seen to be similar as in the induced cells ( Fig 2E ) . This confirmed that ORF59 associates with PRMT5 in reactivated cells . To further investigate the association between ORF59 and PRMT5 , a systematic series of truncation constructs of these proteins were generated ( Fig 3A and 3B ) . The association of ORF59 with PRMT5 was first tested by using GST fused ORF59 truncations with in vitro translated 35S-methionine labeled full-length PRMT5 . GST-tagged ORF59 segments or GST-control were used to precipitate the in vitro translated PRMT5 ( Fig 3C ) . The first segment of ORF59 ( 1-132aa ) precipitated PRMT5 most strongly compared to the other segments indicating the segment to be the primary site of PRMT5’s binding ( Fig 3C , compare lane 3 with lanes 4 and 5 ) . The specificity of their binding was confirmed by the lack of any bound PRMT5 with GST control ( Fig 3C , lane 2 ) . The binding assay was conducted with equal amounts of in vitro translated PRMT5 represented in the input lane ( Fig 3 , lane1 ) , while GST proteins used for binding are shown with coomassie staining ( Fig 3C , bands marked with asterisks ) . Next , we determined the domain of PRMT5 primarily responsible for its interaction with ORF59 . Full-length ORF59 fused to GST ( GST-59 ) or control GST was used to precipitate the in vitro translated PRMT5 and its truncations . Equal proportions of the in vitro translated PRMT5 and respective truncations were used in the binding assays with GST or ORF59-GST . As expected , ORF59-GST but not control GST precipitated full-length PRMT5 ( Fig 3D . lane 3 , PRMT5 panel ) . Interestingly , ORF59-GST bound most strongly with the PRMT5 210-420aa region ( Fig 3D . lane 3 , PRMT5-M panel ) . Although a small proportion of PRMT5 1-210aa also bound with ORF59-GST it is very evident that ORF59 interacts most robustly with the 210-420aa regions of PRMT5 , which notably possesses the catalytic domain for methyltransferase activity . This association was further confirmed using an over-expression system in which HEK293T cells were transfected with the various truncation mutants of PRMT5 ( Flag epitope tagged ) and with HA-tagged ORF59 . Immunoprecipitations with anti-Flag antibody for PRMT5 truncations showed efficient binding of PRMT5 segment 210-420aa with ORF59 detected in a western blot with HA antibody ( Fig 3E , lane 7 ) . The binding of PRMT5 truncations with ORF59 showed similar pattern as detected with the in vitro translated proteins i . e . PRMT5-M ( 210-420aa ) of PRMT5 associated most strongly with ORF59 ( Fig 3E ) . The N-terminal domain of PRMT5 showed also showed binding , although lower compared to the middle region , which is similar to the in vitro binding data ( Fig 3E , compare lane 6 and 7 ) . The C-terminal domain of PRMT5 did not associate with ORF59 in both in vitro binding and immunoprecipitation assays ( Fig 3D and 3E ) . Since the middle region of PRMT5 ( PRMT5-M ) was the primary site of interaction with ORF59 , and possesses the catalytic domain required for methyltransferase activity , we wanted to determine whether the minimal catalytic domain associated with ORF59 [31] . To do so , we constructed a PRMT5 truncation containing the catalytic motif fused to GFP and HA . This was used for binding with GFP tagged ORF59-Flag or GFP-Flag ( Fig 3F ) . Immunoprecipitation with anti-Flag antibody showed specific precipitation of catalytic domain of PRMT5 with GFP-59-Flag but not with the GFP-Flag control ( Fig 3F ) . This confirmed that ORF59 associated with PRMT5 through its N-terminal domain ( 1-132aa ) to the middle , catalytic region of PRMT5 , possibly to interfere with the methyltransferase activity . A balance between repressive and activating epigenetic marks on the KSHV genome has been shown to regulate the viral gene expression and control the switch between latent-lytic cycles of the virus [19–21 , 25] . The immediate early gene , RTA promoter region is one example of bivalent chromatin , displaying both repressive , H3K27me3 and activating , H3K4me3 marks but upon reactivation activating marks are enriched following a decrease of the repressive marks . This is a particularly ingenious mechanism as it allows the virus to respond to environmental stimuli rapidly . PRMT5 , an arginine methyltransferase responsible for the H4R3me2s modification , promotes heterochromatinization . To understand the importance of PRMT5 and the symmetric methylation of histone H4R3 during the viral life cycle , we determined the symmetric methylation levels on H4R3 of viral chromatin during latency and lytic reactivation . We immunoprecipitated symmetrically dimethylated H4R3 containing chromatin from un-induced and doxycycline induced TRExBCBL1-RTA cells . Specificity of the antibody for the symmetrically methylated form of H4R3 chromatin was confirmed by immunoprecipitation and detection with symmetric antibody , which did not cross-react with asymmetric form ( Fig . B in S1 Text ) . DNA extracted from the bound chromatin was sequenced ( ChIP-Seq ) and analyzed for enriched regions ( ChIP-peaks ) using the ChIP analysis tool of CLC Workbench [53] . The peak score representing the relative levels of H4R3me2s containing chromatin was detected throughout the genome with enrichment at specific regions on the uninduced latent genome ( Fig 4B ) . Interestingly , the levels of symmetrically methylated H4R3 ( H4R3me2s ) chromatin on the lytically reactivated genome were significantly reduced throughout the genome with certain regions totally devoid of the H4R3me2s chromatin ( Fig 4B ) . Considering the transcriptionally restrictive nature of the latent genome in contrast to highly active viral gene transcription during reactivation , the abundance of symmetrically methylated H4R3 in latent cells and subsequent reduction in lytic cells was to be expected ( Fig 4B ) . In addition , we tested histone H4 occupancy on viral genome by performing histone H4 ChIP-Seq on the same latent and lytic TRExBCBL1-RTA cells and analyzing for any enriched ChIP-peaks ( Fig 4C ) . Mapping the reads of histone H4 ChIP-seq to the viral genome showed a consistent occupancy and the ChIP peak calling software did not detect peaks with significant peak core due to the uniformed presence of histone H4 throughout the genome . Importantly , the occupancy of histone H4 on the viral genome remained similar after induction suggesting that reduction in H4R3me2s during induction was not due to overall reduction of histone H4 from the viral genome . To further ensure that the changes in H4R3me2s enrichment were not due simply to changes in genome copy numbers , we tested the levels in TREx-BCBL1-RTA cells induced for lytic replication for 12h and treated with replication inhibitor , 0 . 5mM PFA . At 12h time point , we still detected significant reduction in H4R3me2s enrichment at indicated region of the viral genome including OriLyt , RTA promoter , K8 promoter , and ORF21 promoter ( Fig . C in S1 Text ) . Since PRMT5 symmetrically methylates H4R3 to make the chromatin compact , we wanted to determine whether depleting PRMT5 would be sufficient to alter the chromatin into a transcriptionally active form . To this end , we performed PRMT5 knockdown by transducing shRNA in KSHV positive , BCBL-1 cells and transfection of siRNA on iSLK . 219 cells . Both shRNA and siRNA for PRMT5 significantly reduced the levels of PRMT5 compared to the shControl and scrambled siRNA ( si-Cntrl ) , respectively ( Fig 4F and 4G ) . The transcriptionally active nature of the chromatin was determined by quantifying the levels of viral mRNA in PRMT5 depleted cells compared to the control cells . Interestingly , BCBL-1 cells with depleted PRMT5 showed significantly higher levels of almost all the viral transcripts compared to the control cells ( Fig 4D ) . Furthermore , PRMT5 depleted iSLK . 219 cells also showed an overall increase , although lesser fold than BCBL-1 cells , in the number of viral transcripts ( Fig 4E ) . This suggested a restrictive role of PRMT5 in regulating KSHV gene expression . Next , we wanted to determine whether the levels of symmetrically methylated H4R3 chromatin were reduced in those PRMT5 depleted cells . To achieve this , we performed H4R3me2s ChIP on both; BCBL-1 and iSLK . 219 , control and PRMT5 depleted cells and quantified the symmetric methylation of H4R3 at representative viral gene promoters and genomic regions ( Fig 4B ) . The levels of H4R3me2s in PRMT5 depleted cells were calculated relative to the control cells , which are represented by light grey bars in while the darker grey bars indicate PRMT5 knock-down cells ( Fig 4H and 4I ) . In both the cell lines , PRMT5-depletion correlated with a significant decrease in the amount of H4R3me2s at those representative regions ( Fig 4H and 4I ) . These results suggest that the symmetric dimethylation of H4R3 on the viral genome is dependent upon the expression of PRMT5 and depleting PRMT5 results in a loss of the H4R3me2s modification . Thus , PRMT5 knockdown of KSHV-infected cells impairs H4R3me2s while simultaneously upregulating viral gene transcription . The reduction of H4R3me2s marks on the viral chromatin during lytic reactivation suggests a dynamic state of arginine methylation that can be altered to favor viral transcription/replication during lytic reactivation . The reduction of H4R3me2s marks at various viral promoters shown in the PRMT5 knockdown cells implies that the presence of PRMT5 on the chromatin can be correlated with the levels of H4R3me2s marks . Taking into consideration PRMT5’s association with ORF59 and its depletion leading to transcriptional activation , we compared the levels of PRMT5’s association to the viral chromatin before , and following lytic induction . To test this , TRExBCBL1-RTA cells were harvested at uninduced , 12h induced or 24h induced time points and used for PRMT5 and ORF59 ChIP-Seq analysis . ( Due to the insufficient quantity of ORF59 ChIP DNA isolated from uninduced samples , ORF59 ChIP-Seq was only performed on the 12h and 24h induced cells ) . PRMT5 showed enriched binding across the latent viral genome , however; upon reactivation at both , 12h and 24h the peaks were diminished suggesting a lower binding of PRMT5 after induction ( Fig 5C ) . Not surprisingly , ORF59 was enriched at numerous loci on the viral genome at both , 12h and 24h post-induction ( Fig 5D ) . Furthermore , in previous studies we tested ORF59’s binding to chromatin in the presence of replication inhibitor , PFA . When replication was inhibited , although perhaps slightly less than control-treated cells , ORF59 still showed significant enrichment at several viral loci ( Fig . C in S1 Text ) . This demonstrated an important link between ORF59 binding and chromatin structure modulation . The accumulation of ORF59 yet reduction in PRMT5 binding to the viral chromatin suggested a mechanism where ORF59 is responsible for displacing PRMT5 from the chromatin . Interestingly , PRMT5 binds to the chromatin through a ligand , cooperator of PRMT5 ( COPR5 ) [40] . COPR5 functions as a linker molecule that causes PRMT5 to preferentially modify H4R3 residues [40] . To test if PRMT5 is displaced from the chromatin by the detachment of this linker molecule , we also performed COPR5 ChIP Seq on uninduced , and 24h induced TRExBCBL1-RTA cells . In contrast to the PRMT5 ChIP Seq results , COPR5 binding to the viral chromatin remained similar after induction ( Fig 5B ) , confirming that displacement of PRMT5 from COPR5 binding alters symmetric methylation of H4R3 . Despite the fact that ORF59 , PRMT5 , and COPR5 are all DNA-binding proteins , ChIP-seq performed with control-IgG on uninduced , 12h induced , and 24h induced cells did not show any particular peak on the viral genome confirming the specificity of these ligands in chromatin immunoprecipitation ( Fig 5E ) . To study the mechanism by which ORF59 could deplete PRMT5 binding from the viral genome in more detail , we tested the binding between ORF59 and the PRMT5-chromatin linker molecule , COPR5 . To this end , we created constructs to mimic previously described regions of COPR5: 1-140aa , and 141-184aa . The first larger segment ( 1-140aa ) corresponds to the linker-function of COPR5 and associates with histones whereas the smaller C-terminal truncation of COPR5 ( 141-184aa ) is important for binding to PRMT5 . Displacement of PRMT5 from the chromatin prompted us to test whether ORF59 was disrupting the association of PRMT5 with its linker , COPR5 . Thus , we performed an in vitro binding assay using GST-fused ORF59 protein with 35S-methionine labeled , in vitro-translated COPR5 . First , we determined the binding region of ORF59 by using ORF59 full length and its truncations used previously ( Fig 6A ) . Equal amounts of in vitro-translated COPR5 were added to the binding reaction with each of the GST constructs and a representative amount of input is shown ( Fig 6B , lane 1 ) . While control-GST showed no association with COPR5 ( Fig 6B , lane 2 ) , ORF59 full length showed strong binding ( Fig 6B , lane 3 ) . Notably , ORF59-1 , ORF59-2 showed some binding but segment 3 ( ORF59-3 ) did not show any binding ( Fig 6B , compare lanes 4 , 5 with 6 ) . Next , we determined the ORF59 binding domain in COPR5 by using in vitro translated COPR5 and its truncations ( Fig 6C ) with full-length ORF59-GST . Binding was compared with their respective inputs ( Fig 6D , lanes 1–3 ) . ORF59-GST interacted with full-length COPR5 and remarkably only the C-terminal region of COPR5 141-184aa ( Fig 6D , lanes 7 and 9 ) . The two proteins associated specifically as the control GST did not show bindings ( Fig 6D lanes 4–6 ) . The binding of ORF59 to the same domain of COPR5 required for recruiting PRMT5 ( 141-184aa ) , but not to the histone-linking region ( 1-140aa ) , suggested a mechanism in which ORF59 disrupts the binding of PRMT5 bound to COPR5 . This was tested with competitive Co-IPs by expressing ORF59 in cells with COPR5 and PRMT5 . In other words , the ability of PRMT5 to bind COPR5 ( and vice-versa ) was analyzed in the presence or absence of ORF59 . In the first set , immunoprecipitation of PRMT5-Myc confirmed its association with COPR5 ( Fig 6E , lane 5 ) . However , the presence of ORF59-HA reduced the amounts of co-precipitating COPR5 with PRMT5 ( Fig 6E , IB:Flag panel , compare lane 6 with lane 5 ) . The levels of COPR5 in the lysates were comparable ( Fig 6E , IB:Flag panel-input lanes , compare lanes 1–3 ) . We also attempted a reverse co-IP with COPR5 for assaying its binding to PRMT5 in the presence of ORF59 ( Fig 6F ) . Detection of PRMT5 immunoprecipitating with COPR5 confirmed their binding ( Fig 6F , lane 5 , IB:Myc panel ) . Presence of ORF59 decreased COPR5’s binding with PRMT5 ( Fig 6F , IB:myc , compare lanes 5 and 6 ) . These co-precipitations demonstrate that expression of ORF59 competitively disrupts PRMT5’s association with COPR5 to assist in facilitating structural chromatin changes that favor lytic replication . ORF59 is an essential protein for lytic viral replication; however , most of the studies were done in presence of a robust viral transcactivator protein , RTA , which is necessary and sufficient to facilitate lytic reactivation [54 , 55] . Therefore , we wanted to investigate the role of ORF59 in modulating chromatin structure in cells lacking this overarching factor , iSLKTet-RTA-Bac16-RTASTOP [56] . iSLKTet-RTA-Bac16-RTASTOP cells were transiently transfected with ORF59 for the detection of chromatin modifications using ChIP assays . Expression of ORF59 and absence of RTA in iSLKTet-RTA-Bac16-RTASTOP cells was confirmed by immune detection ( Fig 7A ) . We then determined the enrichment of ORF59 on the viral chromatin across a number of important loci including OriLyt region and the RTA promoter region ( Fig 7B ) . The amounts of ORF59 bound to the viral promoters were calculated relative to the control-plasmid transfected iSLKTet-RTA-Bac16-RTASTOP cells . The data showed varying levels of ORF59 binding despite the absence of RTA on the representative regions of the viral genome ( Fig 7A ) . Next , we used the same cells to determine the association of PRMT5 to the viral chromatin . This revealed a consistent pattern of lower PRMT5 bound at those representative sites ( Fig 7C , darker bars represent PRMT5 binding in presence ORF59 ) . Strikingly , the expression of ORF59 in those iSLKTet-RTA-Bac16-RTASTOP cells was enough to trigger significant reductions of H4R3me2s marks on the viral chromatin at various viral promoter regions and the OriLyt ( Fig 7D ) . In fact , the reduction in symmetrically methylated H4R3 due to ORF59 expression was fairly comparable to those obtained from iSLKTet-RTA-Bac16RTASTOP cells induced for lytic replication with doxycycline for RTA expression ( Fig 7F ) . These cells showed a robust expression of RTA and ORF59 ( Fig 7E ) . These results suggest a model in which PRMT5 binds to the latent viral chromatin to symmetrically methylate the H4R3 but the expression of ORF59 represses H4R3me2s levels by altering PRMT5’s association with COPR5 in order to alter the chromatin landscape ( Fig 7G ) . The change in H4R3me2s marks and expression of viral transcripts due to PRMT5 depletion and ORF59 expression prompted us to evaluate whether reduction in the levels of H4R3me2s leads to an alteration of other epigenetic marks on the chromatin favoring lytic replication . To this end , we evaluated the levels of H3K4me3 , a well-known activating mark on the viral chromatin , previously shown to be enriched during lytic reactivation [20 , 22 , 57] . Interestingly , studies have shown that the presence of H4R3me2s mark inhibits efficient tri-methylation of H3K4 residues to preserve a heterochromatic landscape [34 , 58] . Our results confirmed a decrease of these prohibitive H4R3me2s marks at various viral promoters in the presence of ORF59 , therefore it was necessary to test whether this led to an increase in the abundance of activating , H3K4me3 marks on those sites . To this end , chromatin immunoprecipitation with anti-H3K4me3 antibody was performed on cells with a series of different cellular/viral conditions . iSLKTet-RTA-Bac16-RTASTOP cells were induced for RTA expression by the addition of doxycycline for the precipitation of viral chromatin bound to H3K4me3 . Relative levels of H3K4me3 bound chromatin after lytic induction ( Fig 8B , dark bars ) were calculated by normalizing with the levels in uninduced cells ( Fig 8B , dark bars ) ( a relative fold change of 1 represents the H3K4me3 levels on viral genome before lytic induction ( Fig 8B ) ) . Consistent with previous findings [20 , 22 , 57 , 59] , lytic induction increased the abundance of activating , H3K4me3 marks on the viral chromatin , shown on the represented targets ( Fig 8B ) . In addition , we used the same cell line , iSLKTet-RTA-Bac16-RTASTOP for detecting the role of ORF59 on H3K4 methylation levels in absence of RTA . The expression of ORF59 in these iSLKTet-RTA-Bac16-RTASTOP cells was confirmed by immune detection ( Fig 8A ) . Chromatin bound to H3K4me3 histone showed a significant enrichment at various viral promoters in cells expressing ORF59 as compared to the vector-transfected cells ( Fig 8C ) , which corroborated with reduction in the levels of H4R3me2s marks . These results confirmed that ORF59 by itself was capable of inducing changes in chromatin structure resembling those that occur during lytic reactivation . Next , we wanted to determine the levels of H3K4me3 bound chromatin in cells depleted with PRMT5 , which showed a significant decrease in levels of H4R3me2s bound chromatin . Not surprisingly , the levels of H3K4me3 bound chromatin were increased in PRMT5 depleted iSLK . 219 cells as compared to the control cells ( Fig 8D ) . Similarly , BCBL-1 cells depleted for PRMT5 by shRNA showed significant increase in H3K4me3 bound chromatin compared to the control cells ( Fig 8E ) . This was consistent with the previous observation that H4R3me2s has an inhibitory effect on H3K4me3 and removal H4R3me2s leads to an enrichment of active chromatin mark , H3K4me3 [34 , 58 , 60] . ORF59 has been previously characterized to be essential for efficient viral DNA replication , but given the role we describe here in regards to chromatin structure modulation , we wanted to investigate the effects of ORF59 protein on the transcription of viral genes . To this end , we utilized previously described KSHV Bacmid deleted with ORF59 ( Bac36Δ59 ) in 293L cells [50] . We harvested RNA from wild type ( 293L-Bac36WT ) and ORF59 deleted ( 293LBac36Δ59 ) cells transiently transfected with RTA for the induction of lytic cascade . The levels of RTA were comparable between these two sets , RTA transcripts were 505 and 510 folds in wt and ORF59 deleted cells , respectively ( Fig 9 ) . The levels of the viral genes transcripts in 293L-Bac36WT with RTA cells were calculated by comparing with the control vector transfected , 293L-Bac36WT cells . Similarly , the viral genes transcripts in 293LBac36Δ59 expressing RTA were calculated by taking vector transfected , 293LBac36Δ59 cells , as control . The relative fold change of viral genes with RTA from 293L-Bac36WT and 293LBac36Δ59 were plotted and showed that in cells lacking ORF59 , the mRNA copies of many viral genes were reduced ( Fig 9 ) . We analyzed the effects of ORF59 on the expression of immediate early , early , and late genes . Immediate early ( IE ) genes , displayed in green , showed consistently higher levels of transcripts in the Bac36WT as compared to the cells deleted for ORF59 ( Fig 9 , Dark green-Bac36WT and light green-ORF59 deleted ) . A handful of early genes , shown in blue shade , also showed dependence on ORF59 for their expression and these include ORF59: ORF4 , ORF18 , ORF34 , ORF38 , and ORF47 ( Fig 9 , Dark blue- Bac36WT and light blue- Bac36Δ59 ) . Interestingly , a greater number of late gene transcripts , represented by the red bars ( Fig 9 , Dark red-Bac36WT and light red-ORF59 deleted ) were affected by the depletion of ORF59 , suggesting that ORF59 plays an important role in controlling viral chromatin landscape for gene transcription .
ORF59 encoded processivity factor is one of the viral lytic proteins that helps the viral DNA polymerase with processivity activity and is essential for the productive replication of the viral genome [42 , 44 , 46 , 48 , 50] . We previously showed that phosphorylation of ORF59 with viral kinase is essential for the processivity function and virion production [50] . ORF59 was classically viewed as DNA replication protein but it has been shown to regulate additional processes involved in promoting lytic reactivation . These include , binding to cellular helicases , Ku70/Ku86 to impair non-homologous end joining of replicated DNA [51] , and binding and degrading PARP-1 to alleviate the repressive effects of PARP-1 on viral lytic replication [52] . These additional functions prompted us to identify the proteins interacting with ORF59 during lytic replication to have a clear understanding of its role in the viral life cycle . Using a Flag epitope tagged version of ORF59 in BAC16 was advantageous for identifying the proteins specifically associating with ORF59 , including a large number of viral and cellular proteins . Among them we observed a chromatin modifying protein , PRMT5 , as a significant ORF59 binding protein because ORF59 is detected early during reactivation and is critical for processes involved in DNA replication [24 , 49 , 50 , 61 , 62] . PRMT5 , an arginine methyltransferase , was characterized as a transcriptional repressor bound to the chromatin of a promoter region in conjunction with multiple repressive marks including hypoacetylated H3 and H4 and lysine methylated H3K9 residue [41] . Additional reports confirmed PRMT5 to be a chromatin binding protein specifically on a transcriptionally repressive region in cooperation with other repressive complexes or transcription factors including Blimp1 , Snail , BRG1 and hBRM [63–66] . Our immunoprecipitation and localization studies revealed that ORF59 and PRMT5 interact in the nucleus of KSHV infected cells during lytic reactivation , which was consistent throughout multiple cell lines . After confirming the association between ORF59 and PRMT5 , we postulated that ORF59 , being a protein required for lytic DNA replication , which in turn occurs on open chromatin , might alter the repressive functions of PRMT5 . To better understand the nature of the interaction of these two proteins , we determined the domains required for their association in in vitro binding assays . The N-terminal domain of ORF59 , 1-132aa interacted most strongly with PRMT5 . Interestingly , the N-terminal domain of ORF59 is the domain through which ORF59 homodimerizes and interacts with the viral DNA polymerase [44 , 46 , 47 , 49] . On the other hand , the interaction domain of PRMT5 that binds to ORF59 mapped to its middle domain from residues 210-420aa . The middle segment of PRMT5 possesses the methyltransferase activity , which prompted us to generate a clone containing the catalytic domain of the middle segment for assaying its binding with ORF59 . Intriguingly , ORF59 was able to interact with this small domain of PRMT5 , indicating that ORF59 could be involved in modulating the methyltransferase activity of PRMT5 . A previous report showed that arginine methyltransferases could associate with viral proteins to methylate them and alter their function [67] . KSHV latent protein , LANA associates with an arginine methyltransferase type I , PRMT1 , which methylates arginine residues in an asymmetric fashion [67–69] . This demonstrated that KSHV proteins are not only associated with cellular arginine methyltransferases , but are indeed post-translationally modified by them to alter specific functionality . This begged the obvious question of whether ORF59 is arginine-methylated by associating with PRMT5 . To investigate this , we used a methylation prediction software , MASA ( Methylation site based on the Accessible Surface Area ) available at http://masa . mbc . nctu . edu . tw/predict . php [70] , which found that only a single arginine , R384 could potentially be methylated ( Fig . D in S1 Text ) suggesting that its association with PRMT5 may be required for its function . However , we did not pursue the methylation of ORF59 in this report [71] . As mentioned earlier , PRMT5 symmetrically dimethylates the H4R3 residues of histone H4 to form a transcriptionally repressive chromatin , so we determined the levels of H4R3me2s marks on latent viral genome by performing a sequencing of the H4R3me2s bound DNA . This showed a significant enrichment throughout the latent genome while the levels of symmetrically methylated H4R3 on the viral chromatin were reduced upon reactivation . This coupled with the similar levels of H4 occupancy on the viral genome confirmed that histone arginine methylation is differentially regulated during different phases of the viral life cycle . The dynamic nature of the chromatin landscape of the viral genome facilitates rapid change for controlling the KSHV lifecycle phases . The viral genome persists in a highly ordered chromatinized episome within the host cells and the associated chromatin is subject to modifications for the concealment or exposure of particular genes for transcription as needed during latency or the lytic reactivation cascade [20 , 21 , 25 , 26] . The availability of genes for transcription is regulated by the presence or absence of a variety of multiple histone-tail modifications , including H4R3me2s [28] . To date , most of the reports on H4R3me2s methylation show its association with transcriptionally repressed gene [60 , 72] . However , one study found PRMT5 to be over-expressed in transformed chronic lymphocytic leukemia ( B-CLL ) cells with elevated levels of global H4R3me2s marks [73] . Interestingly , this enrichment in H4R3me2s also resulted in the transcriptional repression and subsequent downregulation of a tumor suppressor family of genes , pRB [73] . Another study conducted using EBV positive cells as a model tested the effects of PRMT5 mediated gene repression [74] . They used a PRMT5 inhibitor to determine the importance of repressive epigenetic marks in context of EBV tumors and concluded that PRMT5 was critical for B cell transformation and malignancies because the overexpression of PRMT5 helped to silence the tumor suppressor genes [74] . Another report generated a sophisticated in silico modeling procedure to analyze the ChIP-Seq data from 20 different histone methylations concluded that in the context of simultaneous activating and repressive marks , H4R3me2s is one of the most abundant repressive marks associated with gene silencing [60] . Thus , after confirming the association between ORF59 and PRMT5 , the reduction of H4R3me2s marks ( that PRMT5 is responsible for bestowing ) on the viral genome during lytic reactivation appeared in agreement with its role and we elucidated a mechanism by which ORF59 might be directly or indirectly facilitating these changes to the viral chromatin . It was interesting to note that the binding of ORF59 to the viral genomic regions reduced the levels of chromatin bound PRMT5 , which was primarily due to a change in specificity of PRMT5 with its linker COPR5 [40] . PRMT5’s binding specificity to the chromatin is determined in large by the co-factors it associates with and we found that ORF59 disrupted the association between PRMT5 and the linker molecule COPR5 that tethers PRMT5 preferentially to the chromatin for symmetrically methylating the H4R3 residue [40] . In essence , presence of ORF59 , which expresses during lytic reactivation , disrupts the binding of PRMT5 to the chromatin by competitively causing its detachment from its linker molecule , COPR5 . Notably , the binding of COPR5 to the viral chromatin did not appear to change significantly which in conjunction with the fact that ORF59 did not associate with the histone H4 binding region of COPR5 1-140aa , suggests that ORF59 does not disrupt that function but rather blocks PRMT5’s specific affinity for histone H4 . Moreover , ORF59 appears to be directly involved in regulating these lytic reactivation-permissive changes to the chromatin as the KSHV genome lacking immediate early genes ( RTA-stop cells ) showed similar reduction in chromatin bound PRMT5 and H4R3me2s marks at the viral promoters when supplemented with ORF59 . The reduction of the levels of PRMT5 bound to the chromatin led to a reduction in the symmetrically methylated H4R3 and gene activation . Similarly , it was previously seen in cancer cells in which treatment with a drug , AS1411 ( a quadruplex-forming oligonucleotide aptamer ) led to the decreased association of PRMT5 with known gene promoters ( cyclin E2 , tumor suppressor genes ) [29 , 75] . As a result of this depletion , PRMT5 regulated genes regained their activity [75] . Thus , the presence or absence of PRMT5 on the chromatin can be linked to the levels of repressive marks and transcriptional activity , even on the KSHV genome . Cross-talk between different histone modifications to epigenetically regulate gene expression has been extensively studied in recent years and although still imperfectly understood , it is known that the relative presence or absence of modifications at different residues of the histone tails influences precise transcription patterns [76] . Indeed , our experiments suggest that there is a cross talk between repressive H4R3me2s marks and activating H3K4me3 marks . When H4R3me2s was reduced at various viral promoters ( due to PRMT5 depletion or ORF59 overexpression ) , an enrichment of H3K4me3 at the same promoters was detected ( Fig 8 ) . This corroborated with a previous report , which showed that H4R3me2s sterically inhibits H3K4 methyltransferase activity to promote gene silencing [34] . This study on an analogous Type II arginine methyltransferase ( PRMT7 ) implicated the presence or absence of methyltransferase in the prevalence and levels of the corresponding histone marks [34] . Yao et al . determined that overexpression of type II arginine methyltransferase , PRMT7 in cancer cells inhibited the expression of a specific gene promoter by causing an elevation of H4R3me2s marks , shown in conjunction with reduced H3K4me3 , H4ac , and H3ac [58] . Furthermore , knockdown of PRMT7 led to a restoration of gene expression by repressing the levels of H4R3me2s and increasing the H3K4me3 and acetylation of H4 [58] . This strongly confirms that regulating the activity of arginine methyltransferases can cause epigenetic changes to influence gene expression . Other KSHV proteins are capable of modulating viral chromatin during infection , which includes LANA and RTA [19] . RTA has been previously shown to not only autoactivate its own promoter , but also transactivate other viral gene promoters including ( but not limited to ) vIL-6 , PAN ( polyadenylated RNA ) , ORF57 , and ORF59 [77–80] . Due to the known robust effects of RTA , which is also present during viral reactivation , we wanted to determine the impact of ORF59 on KSHV chromatin structure and viral gene expression . Our data showed that ORF59 overexpression , independent of RTA , led to a reduction of PRMT5 binding , loss of H4R3me2s marks , and enrichment of H3K4me3 marks at various viral promoters signifying the role ORF59 in these alterations . The downstream effects of changes to the chromatin structure were evident by the modulated transcription of the lytic viral genes , meaning those structural chromatin changes orchestrated by ORF59 ultimately affect lytic gene transcription . Importantly , the transcription of the lytic viral genes in the absence of ORF59 was not as efficient in ORF59-depleted cells as compared to the wild-type cells . Therefore , this study clearly shows that the lytic protein ORF59 has multiple functions during viral reactivation to facilitate efficient gene transcription for viral replication .
293T and 293L ( ATCC , Manassas , VA ) cells were grown in high-glucose Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 8% bovine growth serum ( HyClone , Logan , UT ) , 2 mM l-glutamine , 25 U/ml penicillin , and 25 μg/ml streptomycin . Additionally , 293Ls harboring any of the following: BAC16WT , BAC16 ORF59-Flag , BAC16RTASTOP ( generous gift from Dr . Jae Jung ) , BAC36WT , BAC36Δ50 , or BAC36ΔORF59 , were cultured in above DMEM supplemented with 50μg/mL hygromycin B . iSLK . 219 ( generous gift from Dr . Don Ganem ) , iSLKTet-RTABAC16WT , and iSLKTet-RTABAC16RTASTOP cells ( generous gift from Dr . Jae Jung ) were maintained in DMEM supplemented with 10% Tet-Free Fetal Bovine Serum with additional 600μg/mL hygromycin B , 400μg/mL G418 , 1μg/mL puromycin . iSLK . 219 cells with recombinant KSHV BACs were induced by doxycycline . TRExBCBL1-RTA ( generous gift from Dr . Jae Jung ) and BCBL-1 ( ATCC , Manassas , VA ) , and cells were grown in Roswell Park Memorial Institute medium ( RPMI ) supplemented with 8% fetal bovine serum ( HyClone , Logan , UT ) , 2 mM l-glutamine , 25 U/ml penicillin , and 25 μg/ml streptomycin . BCBL-1 transduced with shRNA PRMT5 lentiviral vectors ( GE Dharmacon , Lafayette , CO ) were selected and maintained on 1μg/mL puromycin . BACmid-containing 293L cells were transduced with indicated lentiviral vectors and maintained on 1μg/mL puromycin . Constituently expressing ORF59-Flag , and dsRedORF59-Flag in 293L cells were generated using a lentivirus system and ORF59-Flag expressing BACmid . pLVX-ORF59-Flag or pLVXdsRed-ORF59-Flag lentiviral vectors were generated by introducing the gene into respective vectors , and transfecting into 293T cells along with the packaging vectors ( CMV-dR8 . 2 , pCMV-VSVG ) ( Addgene , Cambridge , MA ) for producing virions . Respective vector was also transfected into 293T cells for producing vector control lentivirus . Collected viruses were added to the target cells for transduction followed by selection with puromycin ( 2 mg/ml ) to obtain a pure population of cells . BAC16 ORF59-Flag was transfected into 293L cells with Metafectene Pro ( Biontex Laboratories GmbH , San Diego , CA ) as previously described [50] followed by selection with hygromycin to obtain cells maintaining the KSHV BACs . The selection in both cell lines was monitored with GFP or RFP signals encoded by the lentivirus and the BACmid . All cultures were incubated at 37°C in a humidified environment supplemented with 5% CO2 . The following plasmids were generated by PCR amplification and cloning: pLVXORF59-Flag , pLVXdsRedORF59-Flag , pA3M-PRMT5-Myc , pA3F-PRMT5-Flag , pxiORF59-HA , pA3M-COPR5-Myc , pA3F-COPR5-Flag , pLVX-RTA , pGex-ORF59-GST . The following plasmids were then sub-cloned: pA3F-PRMT51-210aa-Flag , pA3F-PRMT5210-420aa-Flag , pA3F-PRMT5420-637aa-Flag , pA3F-COPR51-140aa-Flag , pA3fCOPR5141-184aa-Flag , pGex-ORF591-132aa-GST , pGex-ORF59133-264aa-GST , pGex-ORF59265-396aa-GST . pGIPz shRNA PRMT5 and Control shRNA lentiviral vectors were obtained from commercial source ( Thermo Scientific Inc . ) . Packaging lentiviral vectors were obtained from Addgene . PRMT5 , COPR5 and their truncations were generated by PCR amplification using specific primers listed in Table C in S1 Text and cloning into respective vectors . The integrity of clones was confirmed by sequencing at Nevada , Genomics Center , Reno . To generate a recombinant KSHV BAC16 with ORF59-Flag tagged , the epitope tag was inserted at the C-terminus of ORF59 by homologous recombination using a two-step “seam-less” galK positive/counter selection scheme . First , the Galk-KanR cassette with homologous flanking sequence was PCR amplified using primers with 50bp-homologus sequences ( before and after the target sequence ) in the sense and anti-sense primers . Galk-KanR cassette was amplified by including 20-nt of homology to the Galk-KanR region . Primers used for Galk-KanR cassette insertion ( bold case is homologous to ORF59 locus , lower case is homologous to the Galk-KanR plasmid ) : Forward primer: 5’-GATCGTGGGAAGGTGCCCAAAACCACATTTAACCCCCTGATTGACTACAAAGACGATGACGACAAGTGAcctgttgacaattaatcatc-3’ , Reverse primer: 5’-CTGAAGAGCGACAGAGCGCGCTCACTGTCCAGGCGGCACATGGTGctcagcaaaagttcgattta-3’ . The PCR product containing the target sequence was subjected to DpnI digestion , followed by agarose gel purification to remove any residual template plasmid . PCR product was then electroporated into competent E . coli strain , SW102 containing BAC16 . The Galk-KanR cassette containing mutants were selected on chloramphenicol/kanamycin agar plates and correct insertional mutants were confirmed by restriction digestion with BamHI and Southern blot analysis of fragment containing the GalK cassette . The GalK-KanR cassette was replaced by electroporating a double stranded oligo ( 5’-ACATTTAACCCCCTGATTGACTACAAAGACGATGACGACAAGTGACACCATGTGCCGCCTGGACAGTGAGCGCGCTCTGTCGCTCTTCAG-3’ ) with homology to the flanking site and plating the bacteria on 2-deoxy galactose ( DOG ) containing agar plates for counter selection . Correct colonies were screened and subjected to confirmation by southern blot analysis , PCR amplification of the junctions and sequence analysis . The following antibodies were used: mouse anti-Flag ( M2 , Sigma-Aldrich , St . Louis , MO ) , rabbit anti-Flag ( F7425 , Sigma-Aldrich , St . Louis ) , mouse anti-RTA ( mouse hybridoma ) , mouse anti-LANA ( mouse hybridoma ) , mouse anti-Myc ( mouse hybridoma ) , rabbit anti-HA ( 6908 , Sigma-Aldrich , St . Louis , MO ) , mouse anti-HA12CA5 ( sc-57592 , Santa Cruz Biotechnology ) , mouse anti-GFP ( G1546 , Sigma-Aldrich , St . Louis ) , mouse anti-GAPDH ( G8140 , US Biological , Salem MA ) , and rabbit anti-Myc ( SAB4300605 , Sigma-Aldrich , St . Louis , MO ) , goat anti-PRMT5 ( C-20 , sc-22132 , Santa Cruz Biotechnology ) , mouse anti-ORF59 ( generous gift from Dr . Bala Chandran ) , rabbit anti-Control IgG ( sc-2027 , Santa Cruz Biotechnology ) , mouse anti-Control IgG ( sc-2025 , Santa Cruz Biotechnology ) , rabbit anti-H4R3me2s ( 61187 , Active Motif , Carlsbad CA ) , rabbit anti-Histone H4 ( #61299 Active Motif ) , rabbit-anti-PRMT5 antibody ( #61001 Active Motif ) , rabbit anti-control IgG ( ChIP grade—Cell Signaling Technology #2729 , rabbit anti-COPR5 antibody ( Novus Biologicals #NBP2-30884 ) , and additional rabbit-anti ORF59 antibody custom synthesized for our lab by GenScript . For overexpression experiments , 293T cells were plated to 70–80% confluency followed by transfecting them with expression vectors by combining PEI , transfection reagent and 150mM NaCl , mixing thoroughly and incubating it for 15 minutes at room temperature . The mixtures were then added onto 70–80% confluent 293T cells and incubated for 6 h at 37°C with 5% CO2 before changing the medium to remove PEI . Transfected cells were harvested after 48 h post-transfection for immunoprecipitation assays . Harvested cells were washed with ice-cold PBS and lysed in 0 . 5 ml ice-cold RIPA buffer ( 1% Nonidet P-40 [NP-40] , 50 mM Tris [pH 7 . 5] , 1 mM EDTA [pH 8 . 0] , 150 mM NaCl ) , supplemented with protease inhibitors ( 1 mM phenylmethylsulfonyl fluoride , 1 μg/ml aprotinin , 1 μg/ml pepstatin , 1μg/mL sodium fluoride , and 1 μg/ml leupeptin ) . Cell debris were removed by centrifugation at 13 , 000×g ( 10 min and 4°C ) , and lysates were then precleared for 1h with rotation at 4°C with 30 μl of Protein A-Protein G-conjugated Sepharose beads . Approximately , 5% of the lysates were saved for input control and remaining was added with 1 . 0 μg of indicated antibodies to capture the protein by rotating overnight at 4°C . Immune complexes were captured with 30 μl of Protein A-Protein G-conjugated Sepharose beads with rotation for 2h at 4°C . The beads were pelleted and washed three times with RIPA buffer . Input lysates and the immunoprecipitated complexes were boiled for 5–7 min in Laemmli buffer , resolved on SDS-PAGE and transferred onto nitrocellulose membrane ( Bio-Rad Laboratories ) . The membranes were incubated with appropriate antibodies followed by detection with infrared-dyes tagged secondary antibodies and imaged on an Odyssey imager ( LICOR Inc . , Lincoln , NE ) . Chromatin immunoprecipitation was performed as described previously [16] . Briefly , approximately 20 million cells were cross-linked with 1% formaldehyde for 10 min at room temperature , followed by addition of 125 mM glycine to stop the cross-linking reaction . Cells were washed with cold PBS containing protease inhibitors ( 1 μg/ml leupeptin , 1 μg/ml aprotinin , 1μg/mL sodium fluoride , 1 μg/ml pepstatin , and 1 mM phenylmethylsulfonyl fluoride ) . Cells were resuspended in 1 ml cell lysis buffer [5 mM piperazine-N , N′-bis ( 2-ethanesulfonic acid ) ( PIPES ) -KOH ( pH 8 . 0 ) -85 mM KCl-0 . 5% NP-40] containing protease inhibitors , incubated on ice for 10 min followed by centrifugation at 2 , 500 rpm for 5 min at 4°C to collect the nuclei . Nuclei were resuspended in nuclear lysis buffer ( 50 mM Tris [pH 8 . 0]-10 mM EDTA-1% SDS containing protease inhibitors ) , followed by incubation on ice for 10 min . Chromatin was sonicated to an average length of 700 bp followed by removing the cell debris by centrifugation at 13 , 000 rpm for 10 min at 4°C . The supernatant containing sonicated chromatin was diluted with ChIP buffer ( 0 . 01% SDS-1 . 0% Triton X-100-1 . 2 mM EDTA-16 . 7 mM Tris [pH 8 . 1]-167 mM NaCl including protease inhibitor ) . Samples were precleared with a salmon sperm DNA-protein A-protein G Sepharose slurry for 1h at 4°C with constant rotation . The supernatant was collected after a brief centrifugation ( 2 , 000 rpm at 4°C ) . Ten percent of the supernatant was saved for input and the remaining 90% was used for capturing chromatin by rotating the complexes overnight at 4°C using indicated antibodies . The antibody bound chromatin was precipitated by protein A/G slurry . Beads were then washed sequentially three times with a low-salt buffer ( 0 . 1% SDS-1 . 0% Triton X-100-2 mM EDTA-20 mM Tris [pH 8 . 1]-150 mM NaCl ) , and twice in Tris-EDTA . Chromatin was eluted in an elution buffer ( 1% SDS-0 . 1 M NaHCO3 ) and reverse cross-linked by adding 0 . 3 M NaCl at 65°C overnight . Eluted DNA was precipitated , treated with proteinase K at 45°C for 2h and purified with Qiagen Min-Elute PCR purification columns . Purified DNA was used as a template for qPCR amplification of indicated regions of KSHV genome using primers listed in Table B in S1 Text . We performed LowCell ChIP using the LowCell ChIP Kit ( Diagenode Inc . ) . Briefly , 1 million cells were fixed as described above followed by a PBS wash and re-suspending them in the kit-provided chromatin-shearing buffer . Chromatin was sonicated to an average size of 500bp using the Bioruptor Pico ( Diagendode Inc . ) and chromatin with specific antibodies were precipitated as described above . TRExBCBL1-RTA cells were used for ChIP-seq assay by precipitating the chromatin from both un-induced and doxycycline induced ( 12h or 24h ) cells with indicated antibodies . DNA extracted from the chromatin bound to respective targets and their appropriate controls was used for preparing the DNA sequencing libraries using NEXTflexTM Illumina ChIP-Seq Library Preparation Kit ( Bioo Scientific Inc . TX , USA ) . Mature libraries were analyzed for purity and quantification using Bioanalyzer 2100 ( Agilent Technologies , Inc . ) and kappa library quantitation kit ( Kappa Biosystems , Inc . ) , respectively . Sequencing of these libraries was done on Illumina NextSeq500 ( Illumina Inc . ) and the sequences were analyzed using CLC Workbench 10 . 0 . 1 ( Qiagen Inc . ) . Briefly , the sequences obtained from input , control IgG antibody and specific antibody samples were mapped to the KSHV genome ( Accession number GSE98058 , https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE98058 ) using ‘map reads to the reference’ tool of the CLC Workbench 10 . 0 . 1 . Mapped reads were analyzed for enriched peaks in ChIP samples , represented as peak score , with respect to input reads using the ‘ChIP-seq’ tool of the CLC Workbench 10 . 0 . 1 with the minimum peak-calling P-value set to 0 . 05 [53] . SmartPool siRNA for control or PRMT5 ( Dharmacon , GE Life Sciences ) was transfected into iSLK . 219 cells using RNAiMax transfection procedure ( Lipofectamine , Thermofisher Inc . ) . Lipofectamine RNAiMax reagent was diluted in Opti-MEM medium and combined with siRNA diluted in Opti-MEM medium and incubated 5 minutes at room temperature . siRNA-lipid complex was added to cells at a 30pmol final amount of siRNA per well of 6-well plate and incubated 96 h before harvesting . pGIPz shPRMT5 and pGIPzshControl ( Dharmacon , GE Life Sciences ) vectors were transfected with packaging plasmids into 293Lenti-X T cells ( Clontech Laboratories , Inc . ) and induced with NaB for 12 hours . Supernatant was collected every 12 hours over the next 3 days and lentiviral particles were concentrated by centrifugation . Lentivirus was added to BCBL-1 cells treated with polybrene and the transduction efficiencies were monitored by the visualization of GFP . Transduced cells were selected with 1μg/mL puromycin and the knockdown efficiencies of both methods were tested with Western blotting and comparative qPCR . qPCR analysis was performed by calculating the relative fold change values against the control knock-down samples and then the fold change values from 3 replicates were averaged and displayed in the graphs Fig 4D and 4E with standard deviation represented by error bars . 293L with BAC36WT , BAC36Δ50 , BAC36Δ59 , BAC16WT , or BAC16RTASTOP were transiently transfected with pLVXdsRedORF59-Flag , pLVX-RTA , or pLVXdsRed , vector control using PEI transfection reagent and cells were harvested 36 hours post transfection . Additionally , above cell lines were lentivirally transduced with either pLVXdsRedORF59-Flag or vector control and the expression was confirmed by Western blot analysis . PRMT5 knock-down cell samples ( detailed above ) were also subjected to transcriptome analysis by real-time qPCR analysis . Briefly , approximately 2 million cells were harvested and RNA was isolated using GE RNA Spin Kit , samples were eluted in 40μ RNAse-free water . cDNA was generated using High-Capacity RNA-to-cDNA kit ( Applied Biosystems Inc . ) according to manufacturer’s instructions . qPCR was performed using cDNA as template for amplifying viral ORF targets listed in Table A in S1 Text . Target genes were quantitatively assessed by comparative CT values and normalized with untreated/control samples . Fold changes were calculated using ΔΔCt method and the error bars represent standard deviation of three experiment replicates . GST proteins were purified as previously described[50] . Briefly , induced BL-21 bacterial culture with indicated GST-fusion plasmids were harvested and the purified proteins were collected on Glutathione beads . Aliqots were taken and resolved by SDS-PAGE and coomassie staining to estimate relative quantity . TNT-T7 Quick Coupled Transcription/Translation System ( Promega Inc . ) was used to generate 35S-methionine labeled proteins according to manufacturer’s instructions . Briefly , reaction components were thawed on ice and combined with plasmid DNA template ( 5μg ) and 2μl [35S]-methionine followed by incubation for 3 h at 30°C . Expression was confirmed by resolving small aliquots of translated protein on a SDS-PAGE and exposing to autoradiography screens . In vitro translated [35S] methionine labeled proteins were first pre-cleared by rotating at 4°C for 30 minutes with Control-GST protein beads . Pre-cleared samples were then combined with equal amounts of respective GST-fusion proteins and the final volume of the binding mixture was brought up to 700 μl with in vitro binding buffer ( 1XPBS , 10% glycerol , 0 . 1%NP-40 ) supplemented with 1μM DTT and protease inhibitors . Samples were rotated 4°C overnight then washed 3 times with binding buffer before being resuspended in 1X PAGE Buffer . All samples , including inputs were then incubated at 95°C for 5 minutes before being resolved by SDS-PAGE and imaged by autoradiography . The next generation sequence data of ChIP-seq are deposited to NCBI genbank with accession number Series GSE98058 , that includes subseries GSE98057 , GSE98087 , GSE100044 . All the data were analyzed using Prism 6 software ( Graphpad Inc . ) for significance . | Kaposi’s sarcoma-associated herpesvirus ( KSHV ) must carefully regulate both phases of its lifecycle in order to persist and proliferate effectively in the infected cells . In this study , we show the importance of dynamic epigenetic modifications on the viral chromatin in dictating whether KSHV displays the latent or lytic phase of its life cycle . Various chromatin-modifying enzymes are responsible for adding activating or repressive ‘marks’ on chromatin , one of these is a PRMT5 ( protein arginine methyltransferase 5 ) , which symmetrically dimethylates arginine 3 of histone H4 ( H4R3me2s ) and associates with condensed chromatin leading to restricted gene expression . An early lytic protein of KSHV , ORF59 associates with PRMT5 to disrupt its binding with the chromatin leading to a loss of repressive , H4R3me2s mark and corresponding gain of activating H3K4me3 during lytic reactivation . | [
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"research",... | 2017 | KSHV encoded ORF59 modulates histone arginine methylation of the viral genome to promote viral reactivation |
Cell-to-cell gene expression noise is thought to be an important mechanism for generating phenotypic diversity . Furthermore , telomeric regions are major sites for gene amplification , which is thought to drive genetic diversity . Here we found that individual subtelomeric TLO genes exhibit increased variation in transcript and protein levels at both the cell-to-cell level as well as at the population-level . The cell-to-cell variation , termed Telomere-Adjacent Gene Expression Noise ( TAGEN ) was largely intrinsic noise and was dependent upon genome position: noise was reduced when a TLO gene was expressed at an ectopic internal locus and noise was elevated when a non-telomeric gene was expressed at a telomere-adjacent locus . This position-dependent TAGEN also was dependent on Sir2p , an NAD+-dependent histone deacetylase . Finally , we found that telomere silencing and TAGEN are tightly linked and regulated in cis: selection for either silencing or activation of a TLO-adjacent URA3 gene resulted in reduced noise at the neighboring TLO but not at other TLO genes . This provides experimental support to computational predictions that the ability to shift between silent and active chromatin states has a major effect on cell-to-cell noise . Furthermore , it demonstrates that these shifts affect the degree of expression variation at each telomere individually .
Responsiveness to minor changes in the environment requires exquisitely sensitive phenotypic plasticity . This can be executed via many different mechanisms , operating on different time scales , with different types of condition-specific responses , but usually includes changes in transcriptional and translational profiles . Variation between independent populations of cells that are presumed to be isogenic can be due to altered epigenetic properties , such as chromatin status of specific genes or chromosomal regions [1] , [2] , to cell-to-cell variations in gene expression [3] , [4] . Such population and cellular variations are likely to operate continuously in natural environments . Microbes living within a mammalian host encounter a variety of host niches . For example , organisms that reside throughout the GI tract must be able to survive conditions in the oral cavity ( pH 6 . 5–6 . 9 , 33–35°C ) , the stomach ( pH 2 , 37°C ) , the small intestine ( pH 7 . 4 , 37–40°C ) , and anaerobic niches in the colon . Accordingly , the ability to acclimate rapidly to changing environments is thought to provide a selective advantage and is supported by studies in yeast and bacteria [5]–[9] . Gene expression noise , defined as cell-to-cell variation in levels of transcription and/or translation , provides phenotypic diversity within an isogenic population , enabling sister cells to respond differently to environmental challenges . Noise can be extrinsic , generally assumed to be due to differences in an environment or to natural variations in cell components such as transcription or translation factors that affect multiple alleles similarly [2] , [3] , [10] . By contrast , intrinsic noise is allele-specific and is often due to changes in the frequency with which transcription initiates from a given promoter [11] , [12] . Intrinsic noise can provide a larger range of responses to environmental conditions , because the relative amounts of one gene product to another can shift more dramatically [13] . The quantitative contributions of extrinsic and intrinsic noise can be distinguished using different fluorescent protein fusions driven from otherwise identical alleles; extrinsic noise will result in correlated relative expression of both alleles , while intrinsic noise will result in independent relative expression of each allele [13] . The degree to which these types of noise contribute to different aspects of organismal survival by producing phenotypic diversity remains to be determined . C . albicans is an organism that survives and flourishes in a wide range of niches within its human host . It engages in a benign commensal lifestyle , residing in the oral cavity and colonizing the GI tract [14] . In some hosts , especially following antibiotic treatment or immune suppression , it switches to a pathogenic state and becomes blood-borne , colonizing internal organs including the kidney , heart , or brain . C . albicans is generally found in the diploid state and it is known to tolerate high levels of genotypic and protein variation including aneuploidy and codon ambiguity [15]–[17] . Under stress conditions , e . g . during drug exposure , certain aneuploidies can provide improved fitness , largely due to increased expression of genes specifically found in extra copies on the aneuploid chromosomes [18]–[21] . Furthermore , while aneuploidy in general often incurs a high fitness cost , some aneuploidies have very little cost , even under non-selective conditions [22]–[24] . C . albicans also has a highly variable proteome because of the ambiguous CUG codon , which encodes serine most of the time . The CUG codons also encode leucine at low frequency in cells under non-stress conditions and at higher frequencies if cells are stressed [15] . C . albicans is the most virulent of the CUG clade organisms and this is thought to be due , at least in part , to amplification of several gene families thought to be important for virulence . These include the SAP [25] , LIP [26] , and ALS [27] gene families that encode proteases , lipases and cell wall adhesins , respectively . The most amplified of all the gene families in C . albicans are the TLO genes , present in 1 copy in most CUG family members , in 2 copies in C . dubliniensis [28] and in 14 copies in C . albicans [29] . All but one of the TLO genes is telomere-adjacent , usually found as the most telomere-proximal , or the penultimate , gene on the chromosome [30] . The TLO gene family encodes a set of related proteins with a Med2 domain , all of which are thought to function as exchangeable Med2 subunits for the Mediator transcription regulation complex [31] . However , how TLO gene expression is regulated and whether Tlo proteins contribute to the phenotypic plasticity of C . albicans has not been explored . In many organisms , genes at telomeres are subject to telomere position effect ( TPE ) , a transient transcriptional silencing due to specific chromatin complexes that are thought to nucleate at the telomeres and to spread inward along the chromosome arm [32] , [33] . Studies of TPE generally detect two expression states ( “ON” or “OFF” ) using phenotypic read outs interpreted as indicating a biphasic open or closed chromatin state at a given telomere [34] . TPE is dependent upon the Silent Information Regulator proteins Sir2p , Sir3p and Sir4p in S . cerevisiae [35] , [36] . Sir2p , an NAD+-dependent histone deacetylase ( HDAC ) , is highly conserved in prokaryotes as well as eukaryotes [37] and contributes to silencing at the telomeres of organisms ranging from S . pombe to mice [38] . In S . cerevisiae , gene expression noise has been reported to be position-dependent . In one study , noise of two unrelated genes was shown to be influenced by their positions at internal loci on two different chromosome arms [12] . Bioinformatic meta-analysis of gene expression along all chromosome arms showed increased gene noise correlated with increased:1 ) proximity to the telomere; 2 ) prevalence of genes with promoters containing TATA box motifs; 3 ) intermediate levels of expression and 4 ) transitions between silencing-specific histone modifications [39] . The latter is not surprising , given that a number of histone modifiers affect gene expression noise through effects on transcription burst size as well as burst frequency [40] . This likely occurs through the regulation of nucleosome occupancy , which is different between promoters with TATA motifs and those without TATA motifs [41] and likely involves interactions with transcription factors as well [42] . Many of the chromatin modifier genes that affect noise encode HDACs . These include RPD3 and HDA1 [40] . In C . albicans , HDACs have been characterized to some degree , with Sir2 being reported to affect phenotypic switching under at least some conditions [43] and Hst3 , Hda1 , and seven other chromatin modifiers have been shown to alter white-opaque switching [44] , [45] . Additionally , the Set3C complex , Set3 and Hos2 , inhibit the yeast-to-filamentous transition by modulating transcriptional kinetics of key morphogenic regulators [46] . The association of noise with telomere proximity has only been explored experimentally in one study using C . glabrata , a pathogenic yeast most closely related to S . cerevisiae . EPA1 , a subtelomeric gene that encodes a virulence-related adhesin [47] , is subject to TPE and silencing contributed to high levels of EPA1 gene expression noise [48] . This study detected effects at one telomeric locus but did not address the question of whether the effect was due to the telomere-adjacent position of the gene . Nonetheless , this work suggests that telomeric silencing by Sir2p may be associated with the highly variable expression of telomere-adjacent genes . Here we investigated the expression of telomere-adjacent genes in C . albicans , with a focus on the TLO gene family . We detected high levels of variability between isogenic isolates at the population level , and , on average , genes that are most telomere-proximal on each chromosome have higher than average expression plasticity . Furthermore , telomere-adjacent genes exhibited high levels of noise ( cell-to-cell variation in expression levels ) that was largely due to intrinsic noise . Importantly , this telomere-adjacent gene expression noise ( TAGEN ) was dependent on genome position; TLO genes had lower noise levels when moved to an internal locus and a non-telomeric gene had higher noise when moved to a sub-telomeric locus . Similar to telomeric silencing , TAGEN was dependent upon NAD-dependent HDAC activity and , to a large degree , upon Sir2p . Finally , selection for either constitutive expression or constitutive silencing of a TLO-adjacent URA3 gene specifically reduced the expression plasticity of the neighboring TLO , in cis , but had no effect on expression plasticity at other TLO genes in trans . Thus , TAGEN generates expression variability as a consequence of dynamic , local chromatin-mediated position-dependent silencing .
In the course of measuring TLO gene expression under a range of growth conditions , we found that expression levels for many individual TLO genes was strikingly variable ( up to several orders of magnitude ) between isogenic biological populations grown from single colonies under identical conditions ( Fig . 1A–C ) . Furthermore , the level of TLO gene expression variation , measured as the coefficient of variation ( CV; standard deviation divided by the mean; at least five replicates per gene-condition ) [49] , was far greater than that seen for two control genes , SOD2 and HGT20 , that were expressed at similar average levels , irrespective of the growth conditions ( Table S4 ) . Transcript abundance measurements were reproducible for individual populations ( average standard deviation among technical replicates = 0 . 63 cycles vs . 4 . 43 cycles between biological replicates ) , further supporting the idea that the population-level expression of individual TLO genes varied considerably . Genes with high cell-to-cell variation in gene expression are often differentially expressed across a large number of environments [41] . To ask if this is the case for the TLO genes , we analyzed an RNA-Seq dataset for expression of all C . albicans genes under eleven different environmental conditions [50] . Across growth conditions , the 13 telomeric TLO genes generally had high CV values relative to the average for all C . albicans genes analyzed by RNA-seq ( Fig . S1A ) , and , as a group , their mean CV value was significantly higher than for a set of 13 randomly chosen genes ( determined by examining 50 , 000 simulated gene sets , p<0 . 025 , Fig . S1B ) . Cells either mock-treated or exposed to a variety of stresses were equally variable ( Fig . S1C ) . Interestingly , the CV value for TLOα34 , the only non-telomeric member of the TLO gene family , had a lower CV than the average telomere-adjacent TLO gene ( Fig . S1A , blue arrow ) . We next asked if Tlo protein levels were also variable . To detect individual Tlo protein levels , we constructed strains with a single copy of GFP fused to a given TLO gene and detected the fusion protein with an antibody to GFP . Tlo-GFP levels were highly variable among biological replicates grown from single colonies under identical conditions . For example , when different colonies expressing Tloβ2-GFP were prepared for protein extraction from independent log-phase cultures on the same day , the levels of GFP were much more different than a similar comparison of two control proteins ( Fig . 1D ) . Two other Tlo-GFP fusion proteins ( representing all three Tlo protein clades [51] showed similar variability when examined under several growth conditions ( Fig . 1E , F ) . Of note , differences in the protein levels of Tlos generally were less dramatic as those seen for transcripts . Nonetheless , individual Tlo protein levels varied considerably between different biological replicate populations . Expression variability between isolates could be the result of expression differences between whole populations or due to cell-to-cell variation within a population . We hypothesized that this high level of variability from population to population could be due to TLO gene expression differences originating from variability between colonies grown on solid agar plates . Based on the assumption that colony growth on solid media subjects cells to intense founder effects and/ot different local environments [52] , [53] , we asked if Tlo expression differences become less evident after cells from single colonies were propagated in liquid medium , assumed to be a more uniform environment that is also less sensitive to founder effects because cells are continuously mixed . To address this question , we compared Tloα12-GFP expression profiles from 6 individual colonies , originating from a single parent colony , that were grown on solid media plates and the same six populations after two days of passaging in a constantly agitated liquid medium ( Fig . 2A ) . The irregular shapes of expression profiles for cells from individual colonies that were prepared for flow cytometry ( by propagation in liquid medium for two hours ) , suggested that these cultures contained mixtures of different subpopulations . Furthermore , these profile shapes were different for the six colonies , suggesting different founder effects . Because cells lifted from a colony are closely related both genetically and epigenetically ( more likely to be in the same silencing state ) , we think variability in silencing states and , potentially , the local environments within a colony produce these profile differences . In contrast , passaging the same colony isolates in liquid medium for two days resulted in expression profiles that were more regularly shaped and more similar to one another ( Figure 2 ) . Passaging in liquid for two days did not significantly alter Tloα12-GFP mean expression ( t5 = 1 . 38 , p = 0 . 29 ) or mean robust CV ( t5 = 1 . 90 , p = 0 . 12 ) among the five wild-type populations . However , the variance among populations was significantly reduced for both mean expression ( F{5 , 5} = 71 . 7 , p = 0 . 0002 ) and robust CV ( F{5 , 5} = 32 . 7 , p = 0 . 002 ) ( Fig . 2C ) . This suggests that either the populations became more homogeneous because distinct subpopulations were better mixed in liquid culture , and/or because Tloα12-GFP expression was more uniform in a more homogenous environment . In S . cerevisiae , Sir chromatin modifiers affect telomeric silencing , with the Sir2p NAD+-dependent histone deacetylase ( HDAC ) being the most evolutionarily conserved . To ask if Sir2 regulates the colony-to-colony variation observed , we performed flow cytometry on different colonies expressing Tloα12-GFP in a sir2Δ/Δ strain . Mean fluorescence of Tloα12-GFP in a sir2Δ/Δ background did not change ( t5 = −2 . 13 , p = 0 . 087 ) , while Robust CV significantly decreased ( t5 = 14 . 01 , p<0 . 0001 ) after liquid passaging ( Fig . 2B , C ) . As in the wild-type background , both fluorescence intensity and Robust CV show less population-to-population variability after liquid passaging ( mean fluorescence: F{5 , 5} = 10 . 93 , p = 0 . 020; CV: F{5 , 5} = 8 . 76 , p = 0 . 035 ) . Comparing the variance among populations of wild-type and sir2Δ/Δ cells , the wild-type populations were always more variable than the sir2Δ/Δ populations , regardless of the parameter or the timepoint ( D0 , mean fluorescence: F{5 , 5} = 93 . 62 , p = 0 . 0001; D2 , mean fluorescence: F{5 , 5} = 14 . 27 , p = 0 . 011; D0 , CV: F{5 , 5} = 62 . 22 , p = 0 . 0003; D2 , CV: F{5 , 5} = 16 . 65 , p = 0 . 0078 ) . Thus , the absence of Sir2 protein reduced the founders effect seen in WT populations isolated from different colonies , suggesting that the function of wild-type Sir2 is to mediate the variation in expression of Tloα12-GFP . To further test the founder effect on Tlo expression , we examined expression of Tloα12-GFP protein in cells originating from opposite sides of the same colony . Interestingly , flow cytometry profiles ( after 2 hours of liquid growth ) differed for the different colony regions ( Fig . 2D ) , suggesting that populations of cells within a colony have different degrees of expression and that each population can have different levels of cell-to-cell noise . It also implies that the reduction in noise following overnight growth in liquid is not a simple function of more uniform mixing in the liquid media . Thus , it appears that colony regions have different levels of expression and of cell-to-cell noise ( Fig . 2A , C , D ) . In contrast , flow cytometry profiles of Tloα12-GFP expression from different parts of a single sir2Δ/Δ colony were similar ( Fig . 2E ) . Therefore , expression variability between and within single colonies is Sir2p-dependent . Furthermore , although microenvironments may differ within a colony [52] , expression levels do not vary considerably within sir2Δ/Δ colonies , suggesting that the variation seen in wild-type cells is either not due to microenvironmental differences or that Sir2 is required to sense those microenvironmental differences . We propose that the variation at Tlo genes is primarily a function of intrinsic noise rather than a response to the microenvironment . To address the degree of heritability of Tloα12-GFP expression levels and expression noise , we analyzed the expression level of mother-daughter cell pairs by pedigree analysis . We isolated 10 mother-daughter pairs , dissected buds from mothers , and allowed them to grow separately on a plate for 18 hours ( Fig . 3A ) . We compared populations of 50 cells from individual mothers to 50 cells from their own daughters to ask if these related populations were more similar to one another than expected by chance ( Fig . 3B ) . The mean difference in absolute ln ( expression ) was 0 . 58 for the mother-daughter pairs and was 1 . 24 for randomized daughter pairs , with the 5% quantile at 0 . 96 . Thus , the mother-daughter pairs were significantly similar to one another ( p≤0 . 0001 ) than expected by chance ( Figure 3C ) . Interestingly , two daughter populations ( Colonies 2 and 10 , Figure 3C ) did not exhibit perfect overlap with their respective mother populations , indicating that expression similarity , although heritable , can diverge over a small number of generations . The studies above analyzed primarily variation in mean and CV of populations of cells . Gene expression noise is studied at the level of cell-to-cell differences , so we next measured cell-to-cell variation using fluorescence microscopy of individual cells isolated from multiple populations ( originating from single colonies ) . We analyzed the cell-cell variation ( measured as CV ) within each population ( founded from a single colony ) , and also compared the CV between different populations . For microscopy studies we analyzed 50 cells from each population of five Tloα and Tloβ clade fusion proteins , which localize to the nucleus and are expressed at higher levels ( and thus are more detectable by fluorescence microscopy than Tloγ-clade genes ) [51] . Strikingly , the fluorescence signal for subtelomeric Tlo genes varied dramatically from cell-to-cell , ranging from very bright cells to cells with no obvious signal ( Fig . 4A , B ) . The level of population-to-population variation was also higher for subtelomeric Tlo genes , consistent with the detection of expression plasticity at the population level ( Fig . 1 ) . Growth under stress conditions ( 5 mM H2O2 or cell wall stress ) also resulted in high levels of Tloα12 cell-to-cell variation ( Fig . S2; p<0 . 001; significance determined using a bootstrap procedure that compared the measured ratio of CVNup49-GFP/CVTloα12-GFP against the critical value obtained from 10 , 000 simulated datasets that randomized the background of measured cells ) . Consistent with the RNA-seq results , the non-telomeric Tloα34-GFP gene , exhibited minimal cell-to-cell and population-to-population variation ( Fig . 4A , B ) . To measure gene expression levels for much larger numbers of cells , we analyzed GFP expression levels using flow cytometry ( 100 , 000 cells per population ) . Nup49 , which encodes a nuclear pore component expressed at similar average levels to the Tloα and Tloβ proteins , exhibited minimal variation between cells within a population ( evident by examining the peak width ) and between populations ( Fig . 4C , S3 ) . In contrast , both cell-cell and population-population variability was much greater for Tlo-GFP than for Nup49-GFP fluorescence levels ( Fig . 4C ) . Two general sources of cell-to-cell variation have been explored extensively in many different species [1] , [3] , [12] , [13] , [40] . Extrinsic noise is due to conditions that differ between cells , such as a general level of ribosome or a local exposure to different growth conditions ( Fig . 2 ) . In contrast , intrinsic noise operates independently on different alleles of the same gene or promoter . The classic method to distinguish between extrinsic and intrinsic noise is to tag two different alleles of the same gene/promoter with two different fluorescent proteins and to observe the relative levels of each on a cell-by-cell basis . Accordingly , we tagged both alleles of TLOα12 or TLOβ2 , using GFP for one allele and mCherry for the other , and determined the degree to which each of the alleles was expressed in individual cells by fluorescence microscopy ( Fig . 5A ) . Extrinsic noise manifests as variable yet correlated expression of the two alleles , while intrinsic noise results in independent , allele-specific expression levels . The relationship between mCherry and GFP expression in Nup49 ( control ) , Tloα12 , and Tloβ2 were clearly different , based on fluorescence intensities ( Figs . 5B , S4 ) . In each individual population ( 12 populations for each tagged gene , see methods ) a simple correlation test between the two fluorophores indicated that there were considerable differences for the three tagged genes ( Fig . S4 , Table S5 ) . We considered the 12 populations for each gene as independent because different colonies and different locations within colonies were different enough from one another that they were not good predictors of the degree of either intrinsic or extrinsic noise ( Table S5 ) . The levels of both intrinsic and extrinsic noise ( extrinsic: F2 , 33 = 12 . 8 , p<0 . 0001; intrinsic: F2 , 33 = 26 . 5 , p<0 . 0001 , Fig . 5C ) were different for the different genes measured . Post-hoc Tukey tests indicated the difference between the two types of noise; the two TLO genes both had significantly higher intrinsic noise than Nup49 . On the other hand , extrinsic noise levels were not specific to TLO genes . Tloβ2 has significantly less extrinsic noise than Nup49 or Tloα12 ( which were not different from each other ) . Furthermore , for both TLO genes , the contribution of intrinsic noise to total noise was significantly greater than the contribution of extrinsic noise ( Tloβ2: t11 = −16 . 8 , p<0 . 0001; Tloα12: t11 = −6 . 5 , p<0 . 0001 , Nup49 t11 = 0 . 056 , p = 0 . 96 , Fig . 5C ) . To investigate whether increased expression plasticity is a general property of telomere-proximal genes , we examined the expression of sets of 16 genes starting with the most telomere-proximal and stepping sequentially into chromosome internal genes using the available C . albicans RNA-Seq dataset [50] . Both sets of the 16 most telomere-proximal genes ( including 9 of 13 subtelomeric TLOs ) and the set of 16 penultimate telomere-adjacent genes ( including 4 of 13 subtelomeric TLOs ) were significantly more transcriptionally variable than sets of 16 random genes ( Fig . S5A , B; significance determined by a bootstrapping procedure as described above; p<0 . 025 in both cases ) . A similar trend was seen for the genes in the third-most telomere-proximal position ( Fig . S5B ) . However , this pattern did not continue as a general trend along the chromosome ( Figure S5C ) , indicating that any ‘spreading of TAGEN’ inwards from the telomere does not propagate more than ∼8 kb into the chromosome arms . Many studies of S . cerevisiae found that differences in promoter structure correlate with differences in the amplitude of gene noise [54] , [55] . To determine the extent to which telomere position and promoter structure affect the variability of TLO gene expression , we constructed two TLO-NUP49 swap strains ( Fig . 6A ) : NUP49-GFP@TLO , in which the control gene NUP49-GFP , together with its native promoter , was moved to the sub-telomeric TLOα9 locus on the left end of Chromosome 4 ( YJB12963 ) ; and TLOα9-GFP@NUP49 , in which TLOα9-GFP , together with its native promoter , was moved to the internal NUP49 locus on the right arm of Chromosome 1 ( YJB12966 ) . Importantly , when either Nup49-GFP or Tloα9-GFP were expressed at the NUP49 locus , noise ( as measured by fluorescence microscopy ) was significantly lower than when either of these proteins was expressed from the TLOα9 locus ( Fig . 6A–C , Fig . S6; p<0 . 05 ) . Expression of Nup49-GFP and Tloα9-GFP was also significantly lower at the TLOα9 locus compared to the NUP49 locus ( Fig . 6A–C; NUP49: t85 . 42 = 16 . 43 , p<0 . 00001; TLOα9: t85 . 44 = 4 . 71 , p<0 . 00001 ) . Flow cytometric analysis of the four strains ( two with tagged genes at their native loci and two with swapped loci ) also indicated that genes at the subtelomeric TLOα9 locus exhibit a significant decrease in the mean fluorescence signal ( position: F1 = 5 . 04 , p = 0 . 038 , gene: F1 = 0 . 93 , p = 0 . 35 ) and an increase in the level of gene noise ( Robust CV; position: F1 = 10 . 12 , p = 0 . 005 , gene: F1 = 2 . 10 , p = 0 . 17 ) relative to the internal NUP49 locus ( Fig . 6D ) . This suggests that the subtelomeric TLOα9 locus is sufficient to cause increased noise because it is telomere-adjacent and affected by Telomere-Adjacent Gene Expression Noise ( TAGEN ) , which influences both population-to-population ( expression plasticity ) and cell-to-cell ( noise ) variability . Furthermore , TAGEN appears to be independent of the promoters tested . The Sir2p HDAC was required for TLO expression variability between colonies . Therefore , we hypothesized Sir2 may also influence TLO noise among cells in a single population . We first asked whether addition of nicotinamide ( NAM ) , an inhibitor of NAD+-dependent HDACs , or deletion of SIR2 had an effect on TAGEN at TLO genes using qRT-PCR . Addition of NAM or the lack of Sir2p significantly reduced expression plasticity ( measured with qPCR , Fig . 7A , S7; background: F1 = 6 . 44 , p = 0 . 020; NAM: F1 = 7 . 79 , p = 0 . 011; interaction: F1 = 3 . 25 , p = 0 . 086 ) , while neither NAM nor the absence of Sir2p significantly influenced mean TLO gene expression ( Fig . S7; Sir2 background: F1 = 0 . 03 , p = 0 . 86; NAM: F1 = 1 . 42 , p = 0 . 25 ) . Furthermore , the effect of deleting SIR2 together with NAM exposure affected expression and plasticity to a similar degree as either NAM or deletion of SIR2 alone: reduced variability with little effect on expression levels ( interaction; CV: F1 = 3 . 25 , p = 0 . 086 ) . Similar results for wild-type vs sir2Δ/Δ mutants were obtained by microscopy ( Fig . 7B , S8; p<0 . 05 ) as well as by flow cytometry of Tloα10-GFP or Tloα12-GFP ( Fig . 7C; Robust CV; gene: F1 = 1 . 21 , p = 0 . 29; Sir2 background: F1 = 5 . 44 , p = 0 . 03; interaction: F1 = 0 . 165 , p = 0 . 69 ) . Thus , Sir2p makes a significant contribution to expression plasticity of Tloα10-GFP and Tloα12-GFP . To ask if Sir2p contributes to the position-dependent aspect of TLO TAGEN , we compared the level of expression noise for the Nup49-GFP@TLOα9 locus in a sir2Δ/Δ strain relative to the level of expression noise for the Nup49-@TLOα9 locus in a wild-type strain . Importantly , the expression noise for Nup49-GFP was decreased in a sir2Δ/Δ strain only for Nup49-GFP@TLOα9 locus and not for Nup49-GFP at its native locus ( Fig . 7D , Robust CV; position: F1 = 11 . 38 , p = 0 . 005 , background: F1 = 5 . 10 , p = 0 . 042 , interaction: F1 = 6 . 91 , p = 0 . 021 ) . Thus , the position-dependent and promoter-independent TAGEN seen at TLO genes is dependent upon Sir2p and , most likely , dependent upon its activity as a NAD+-dependent HDAC . Telomeric silencing is considered to be a process by which telomeres toggle between “OPEN” and “CLOSED” chromatin states . Such a biphasic switch would be expected to generate two subpopulations of cells that would be distinguishable by flow cytometry as having different expression peaks . Yet , expression profiles of specific Tlo-GFP fusion proteins did not exhibit two clear peaks . This could be due to regulation of TLO expression by multiple factors [4] or a relatively fast rate of switching between two expression states [56] . Thus , we explored the role of additional chromatin modifiers in the regulation of TLO expression levels and the degree of TLO TAGEN . Nine modifiers were analyzed by qRT-PCR . HST1 and SET1 influenced expression plasticity ( HST1: t6 = −2 . 89 , p = 0 . 028 , SET1: t6 = −2 . 60 , p = 0 . 041 ) but not expression levels ( HST1: t6 = −0 . 99 , p = 0 . 36 , SET1: t6 = 1 . 20 , p = 0 . 27 ) , while HDA1 , HOS2 , HST2 , PHO13 , NAT4 , RPD31 , and SET3 had no effect on expression level or plasticity ( Fig . S9 and data not shown ) . Consistent with the qRT-PCR results , deletion of HST1 , a SIR2 paralog that affects some telomere-associated genes in S . cerevisiae [57] , [58] , resulted in decreased fluorescence signal for two GFP-tagged Tlo proteins , Tloα10 ( t187 . 2 = 7 . 03 , p<0 . 0001 ) and Tloα12 ( t139 . 4 = 5 . 30 , p<0 . 0001 ) ( Fig . S10A , B ) , as measured by fluorescence microscopy . Consistent with a role for Hst1 protein at internal as well as telomeric loci , the expression noise for Nup49-GFP at its native locus was reduced in the hst1Δ/Δ strain ( p<0 . 05 ) . Cell to cell noise in the hst1Δ/Δ strains was reduced at Tloα12 ( p<0 . 01 ) but not at Tloα10 ( Fig . S10A , C ) , relative to noise levels in the wild-type HST1 parent strains . Thus , unlike Sir2p , which has a major position-dependent role in enhancing noise at telomere-adjacent loci , Hst1p affects expression noise at internal as well as telomere-proximal regions and it affects expression plasticity and noise of different TLO genes differently . We next asked if TAGEN and TPE are functionally related by measuring TLO expression variability in cells selected for constant expression or constant silencing of a TLO-adjacent selectable marker , URA3 . We measured levels of the adjacent TLO ( in cis ) as well as an unlinked TLO ( in trans ) , when cells were selected for expression of URA3 ( ON state selected on medium lacking uridine ) or when cells were selected for repression of URA3 ( OFF state selected on medium containing 5-FOA ) vs cells being free to ‘toggle’ between the two states ( ON and OFF states , no selection on YPAD medium ) . We first constructed two strains , each with URA3 inserted head-to-head at a TLO-adjacent position ( adjacent to TLOα9 or TLOα12; Fig . 8A ) in the subtelomeres . These strains enabled the selection of cells expressing URA3 ( by growth in media lacking uracil ( “-ura” ) ) , or to select for silencing of URA3 ( by growth in the presence of 5-floroorotic acid ( “5-FOA” ) ) . Growth of TLO-adjacent URA3 strains on media lacking uracil or with 5-FOA reduced or increased transcript abundance of URA3 , respectively ( data not shown ) . We then asked if selection in –ura or 5-FOA influenced TLO expression plasticity ( Fig . 8B ) . Importantly , in both strains , selection either for or against URA3 expression significantly reduced variability of the URA3-adjacent TLO transcript levels , yet it did not affect the transcript variability at an unlinked TLO ( Fig . 8C; presence of selection: F1 , 20 = 40 . 4 , p<0 . 0001 , gene: F1 , 20 = 0 . 28 , p = 0 . 60 , interaction: F1 , 20 = 0 . 174 , p = 0 . 69 ) . This occurred without a significant effect on expression levels ( Fig . S11; presence of selection: F1 , 20 = 0 . 03 , = 0 . 87 , gene: F1 , 20 = 2 . 48 , p = 0 . 13 , interaction: F1 , 20 = 0 . 145 , p = 0 . 71 ) . Thus , TAGEN at a specific TLO locus requires that cells toggle between the ON and OFF states and is lost if expression of an adjacent gene is constitutively ON or OFF . Furthermore , the effect of telomeric silencing on TAGEN occurs in cis and does not affect silencing or TLO expression at other subtelomeres .
Here , we discovered and characterized Telomere-Associated Gene Expression Noise ( TAGEN ) , which is detectable not only as intrinsic variation at the cell-to-cell level but also generates variation at the population level . TAGEN is position-dependent , affecting only the most telomere-proximal genes , and it is reduced when cells are locked in a constant chromatin state or when cells are grown for multiple passages in liquid medium . TAGEN is subject to regulation by Sir2p in a position-dependent manner and also to other position-independent chromatin modifiers and transcription factors , e . g . , Hst1p , which affect different TLO genes differently . Importantly , TAGEN is largely promoter-independent and it is tightly associated , in cis , with telomere position effect dynamics . Thus , TAGEN and TPE appear to reflect different aspects of the same phenomenon—the chromatin structure and its impact on gene expression at telomeres is dependent upon proximity to a telomere . Furthermore , increased expression plasticity and noise at telomere-adjacent genes ( TAGEN ) requires the dynamic process by which telomere-adjacent genes toggle between the ON and OFF states of expression presumably due to the OPEN and CLOSED states of telomeric chromatin . At most genomic loci , noise is a phenomenon detectable only when cells are analyzed as individuals [13] . In contrast , TAGEN is detectable in populations of cells isolated from different colonies and also as a cell-to-cell variability largely due to intrinsic noise . The inherited epigenetic expression state is dependent upon telomere-adjacent position , SIR2 , and the initial level of expression appears to exert a founder effect . Importantly , toggling or switching between the ON and OFF epigenetic state of cells in each population likely drives colony-to-colony variation seen at the population level ( Fig . 8 ) . A similar effect was seen for one telomere-adjacent gene , EPA1 , in C . glabrata [48] . TAGEN is detected as large variations in levels of transcripts , measured by either qRT-PCR or by RNA-Seq ( Figs . 1 , S1 ) . TAGEN is also evident at the individual cell level , when levels of GFP fusion proteins are measured by fluorescence microscopy or by flow cytometry ( Figs . 2–7 ) . This suggests that some of the transcriptional plasticity that affects TLO gene expression is buffered by post-transcriptional mechanisms , although we cannot rule out that the long half-life of GFP fusion proteins may contribute an additional buffering mechanism [59] . Since Tlo proteins are produced at levels far higher than they are needed [31] , and since all TLOs encode a related subunit present in a single copy per Mediator complex , we suggest that excess Tlo proteins are likely subject to proteasome degradation [60]–[62] . Amplified gene families that promote growth within a relatively new environment are often located at telomeres . For example , S . cerevisiae strains used to produce wine , sherry or beer carry amplified MEL , SUC , and MAL genes , respectively , which promote breakdown of the predominant sugars in the respective fermentation processes . It is thought that the cost of amplification and diversification of gene family members is lower near telomeres [63] . In addition , the work here suggests that noise at telomeric loci may be exacerbated in a non-uniform environment ( Fig . 2C ) . The fact that this noise is Sir2p-dependent suggests that it is a function of both TAGEN and TPE . Increased gene noise is also associated with duplicated genes [64] , a common feature of expanded gene families at telomere ends . Based on this idea , subtelomeric loci populated with gene families would be expected to be transcriptionally noisy because of the reduced fitness costs associated with noise when multiple functional homologs are present . Bioinformatic analysis of gene expression in S . cerevisiae found that telomere-adjacent loci were expressed with higher levels of transcriptional noise [39] . Thus , telomeres are not only safe neighborhoods for gene amplification but they are noisy neighborhoods for gene expression . We suggest that , because increased noise in non-uniform conditions is Sir2p-dependent , that it is intrinsic feature of TAGEN and , most likely , of TPE as well . Intrinsic noise is generally thought to be influenced by the chromatin state at a given locus and is often ascribed to specific promoter structures or to interactions with specific components of the transcription regulation machinery . Consistent with this , most chromatin modifiers affect either the transcription burst frequency ( frequency with which a promoter switches into a transcriptionally active state ) and/or the transcription burst size ( the total number of transcripts or proteins produced during each transcriptionally active state ) [40] , [56] , [65] . Interestingly , mutations affecting TAGEN often reduced the noise level without causing a substantive change in gene expression levels ( Figs . 7 , S7 , S9 ) . We suggest that regulating the rate of switching between silent and active chromatin at telomeres will reduce the noise , even if it does not affect the net expression levels [3] , [56] . Thus , TAGEN levels are dependent upon the frequency with which telomeric silencing opens and closes the chromatin . TAGEN is dependent upon NAD+-dependent HDACs . Sir2p and the Sir2-like Hst1p contribute to TPE in S . cerevisiae as well as in Schizosaccharomyces pombe , Plasmodium falciparum and Drosophila melanogaster [66]–[68] . This provides further support for the idea that both processes are likely related to one another . TAGEN shows fairly smooth distributions of different expression levels per cell through a population ( Figs . 2 , 4 , 6–7 ) , yet TPE is considered a biphasic switch between two states [34] , [69] . This is likely because TPE is often measured as a growth phenotype that must cross a specific threshold to be detected [33] , [70] and has been considered as a largely population effect . In contrast , we measured TAGEN at the molecular level and , thus , detected a continuous distribution of expression levels and high levels of intrinsic noise . Importantly , the two processes appear to be inextricably linked: when cells with a TLO-adjacent URA3 gene were selected for URA3 expression to be either in all “OFF” or all “ON” , expression levels for the adjacent TLO gene were less variable than when no selective pressure was applied ( Fig . 8D ) . This supports the idea that TAGEN is a consequence of dynamic switching between TPE states , rather than a consequence of silencing or depression of telomere gene expression per se . In C . albicans , TLOs all encode the Med2 subunit of Mediator . In S . cerevisiae , Mediator interacts with Sir2 to modulate TPE [71] , [72] . If a similar relationship exists in C . albicans , then one would expect Tlo proteins to be components of the silencing machinery itself . Consistent with this , a strain lacking Med3p , which interacts with Tlo proteins in the C . albicans Mediator complex tail , exhibits lower levels of TAGEN ( data not shown ) . Thus , noisy TLO expression may contribute to TAGEN , and may proscribe an interesting feedback circuit . Whether the amplification of TLO genes has been an important adaptation for the recently evolved virulence features of C . albicans , and whether TAGEN and Mediator feedback play a role in this process remains to be determined .
N/A Yeast cells were grown in standard conditions in rich medium ( YPAD ) at 30°C [73] unless noted otherwise . Assays were performed by diluting an overnight culture 1∶100 in fresh YPAD and grown at 30°C , 39°C , with 10% fetal bovine serum , with 5 mM H2O2 , with 100 µg/µl Congo Red , or with 2 mM nicotinamide for 4 hours , as indicated . Strains are listed in Table S1 . Transformations were performed using lithium acetate as previously described [73] . Strains carrying NUP49 and TLO tagged with GFP or mCherry at the C-terminus were constructed by PCR amplification from plasmid p1602 [74] , p2120 , or p2343 [75] , which contain GFP and URA3 , GFP and NAT1 , or mCherry and NAT1 , respectively , using primers with at least 70 bp of homology to the target gene ( Table S2 ) . Correct insertion of the fluorescent protein in frame with the relevant TLO gene was first detected as described previously [51] . Only strains in which insertion was detected as a single unambiguous PCR fragment from a single chromosome arm were analyzed further . Integration of the construct at the expected locus was confirmed by PCR , Sanger sequencing , and Southern Blot analysis as described [51] . Locus swapping strains ( Fig . 4 ) were constructed using a PCR amplicon containing the full open reading frame ( ORF ) to be moved , including either all sequences up to the adjacent open reading frames or 1 kb upstream and 1 kb downstream , whichever was shorter , the fluorescent tag , and the selectable marker from previously constructed strains . Transformation and screening were performed as described above . Transcript abundance measurements by qRT-PCR were performed as described [51] with primers listed in Table S3 . Absolute quantification of SYBR fluorescence using the 2nd derivative maximum value was used to calculate ΔCT values using SEC14 as a control . All qRT-PCR results represent the average abundance of at least four independent cultures for each strain of interest . RNA-Seq data for C . albicans grown under 11 different conditions in biological duplicates was obtained from Bruno et al [50] . We determined the coefficient of variation ( CV = standard deviation divided by the mean ) for each gene in each of the eleven environments that data were available for . We then averaged across all environments to determine the average CV for each gene . To determine whether a group of genes was significantly more transcriptionally variable than average , we conducted a bootstrap procedure to obtain a distribution of mean CV values for a group of genes of the appropriate size ( i . e . , 13 to examine TLO expression plasticity , 16 to examine position effects ) . We simulated 50 000 gene groups using the ‘sample’ function in the R Programming Language on the 6006 ORFs measured in the Bruno dataset; the 97 . 5% quantile of these 50 , 000 datasets was used to determine the critical value . Protein lysates were collected as previously described [76] . Briefly , cells were inoculated into liquid YPAD cultures and grown overnight to stationary phase at 30°C with constant shaking . A 1∶100 dilution was then transferred to fresh YPAD and grown for four hours at 30°C with constant shaking prior to collecting lysates . Proteins were separated on a 12% polyacrylamide geland transferred to PVDF membrane ( Immobilon-P , Millipore , Billerica , MA ) as previously described [75] . Western blots were performed with mouse anti-GFP ( Roche , Penzberg , Germany ) , rabbit anti-H4 ( Santa Cruz Biotechnology , Santa Cruz , CA ) , and mouse anti-PSTAIR ab10345 ( abcam , Cambridge , MA ) followed by HRP-anti mouse or HRP-anti rabbit antibody ( Santa Cruz Biotech , Santa Cruz , CA ) . Densitometry of band intensities was quantified using Fiji/ImageJ v1 . 46 ( NIH , Washington D . C , District of Columbia ) . TLOα12-GFP cells were struck onto SDC agar plates . Ten single cells were isolated using an Olympus BX40 dissecting microscope and followed during growth and division . Following the first division the mother and daughter cells were separated and allowed to grow up for 18 hours on the SDC agar plate . Tloα12-GFP expression was visualized by microscopy . We compared the mean difference in absolute ln ( expression ) values from colonies of daughter cells with 10 , 000 randomized affiliations . Overnight cultures in YPAD were diluted 1∶100 in fresh SDC medium and grown at 30°C for 3–4 hours . DNA was stained with DAPI ( 4′ , 6-diamidino-2-phenylindole ) ( Sigma , St . Louis , MO ) diluted 1∶1000 for 25 minutes , washed twice in fresh SDC , and imaged using differential interference contrast ( DIC ) and epifluorescence microscopy with a Nikon Eclipse E600 photomicroscope ( Chroma Technology Corp . , Brattleboro , VT ) . Digital images were collected using a CoolSnap HQ camera ( Photometrics , Tucson , AZ ) and MetaMorph software , version 6 . 2r5 ( Universal Imaging Corp . , Downingtown , PA ) . A total of 8 fields , were collected with 8 fluorescent images along the z axis , in 1-µm increments , for each cell to insure that any signal present was captured throughout the diameter of the cell . Exposure times were 500 ms for Nup49 and Tlo fluorescent fusion proteins . Projections of the z series were constructed with the stack arithmetic/sum function of MetaMorph for analysis and presentation . Fluorescent-tagged protein abundance for each cell was measured by subtracting the average pixel intensity of three 4×4 regions of adjacent background from each of three 4×4 pixel regions within each nucleus . The signal intensity was defined as the average of the three background-subtracted nuclear regions . Nuclear signal intensity was determined for all cells in a minimum of 50 cells for each strain of interest . For all experiments , an equal number of cells were examined for expression and noise; for strains where data from more than the minimum number cells was collected , we used the ‘sample’ procedure in the R programming language [77] to randomly select cells to be analyzed . Extrinsic and intrinsic noise was calculated as in Elowitz et . al [13] . Three strains ( NUP49-GFP/NUP49-mCherry , TLOβ2-GFP/TLOβ2-mCherry , and TLOα12-GFP/TLOα12-mCherry ) were streaked onto YPAD solid agar plates . Three colonies were chosen for each strain and cells from four regions of each colony were sampled ( two from the edges of the colony and two from the center ) . These cells were suspended in liquid and the expression of the GFP and mCherry tagged genes was quantified by fluorescence microscopy for 50 cells using the method described above . The cells were also then cultured in liquid YPAD media for two days with passaging every 24 hours . Cells were taken in logarithmic growth ( OD600∼0 . 5 ) after two days and 50 cells were measured again for GFP and mCherry fluorescence signal by microscopy . Cells for flow cytometry were prepared using a modified protocol from Sudbery [78] . An overnight culture in YPAD was diluted 1∶100 in fresh SDC media and grown at 30°C for 3–4 hours . Cultures in mid-logarithmic growth ( OD600∼0 . 5 ) were collected at 1500×g , resuspended in 4% methanol-free formaldehyde ( Thermo Scientific , Rockford , IL ) , and incubated on a tube rotator for 30 minutes . Cells were then spun down and resuspended in ice cold methanol for 3 minutes , washed three times in 55 mM HCl , resuspended in 500 µl of 5 mg/ml pepsin in 55 mM HCl , and incubated for 30 minutes at 37°C with gentle shaking . Cells were collected by centrifugation , washed three times with 1 ml of 10 mM Tris ( pH 7 . 5 ) , and resuspended in 460 µl Buffer A [78] . Cells were incubated in 40 µl of 1 mg/ml Zymolyase-20T ( ICN Biomedicals , New York , New York ) in 0 . 1 M phosphate buffer ( pH 7 . 5 ) and 1 µl β-mercaptoethanol for 30 minutes at 37°C with gentle shaking and washed 5 times with 1% bovine serum albumin ( BSA ) in phosphate-buffered saline ( PBS ) . Cells were resuspended in 500 µl of primary antibody polyclonal anti-GFP , ab290 ( abcam , Cambridge , UK ) diluted 1∶1000 in 1% BSA in PBS , and incubated overnight on a rotisserie at 4°C , washed 5 times in PBS . Secondary antibody ( 500 µl Alexa Fluor 488 donkey anti-rabbit ( Invitrogen , Carlsbad , CA ) diluted 1∶2000 in 1% BSA in PBS ) was added , samples were incubated 45 minutes in the dark , cells were washed 5 times with PBS , resuspended in 1 ml PBS , and sonicated at 20% duty cycle three times . Flow cytometry was performed using a FACSCalibur ( BD Biosciences , San Jose , CA ) . Measurements were collected for 100 , 000 events and analyzed using FlowJo ( Ashland , OR ) . Events were initially examined on a plot of SSC by FSC and gated to include all events ( cells ) that had measurable FSC and SSC . Mean expression and the Robust CV ( 100*0 . 5* ( Intensity [at 84 . 13 percentile] – Intensity [at 15 . 87 percentile] ) /Median ) of the gated population were collected using cell fluorescence measurements from the FL1 ( fluorescein/GFP ) channel . These measurements were the basis for further analysis . Subtelomeric URA3 was inserted in a head-to-head orientation immediately upstream of the TLO promoter ( ∼600 bp upstream of the TLO start codon ) to produce a subtelomeric , TLO-adjacent URA3 . Insertion sites were identified by PCR and sequencing as well as separation of chromosomes on contour-clamped homogenous electric field ( CHEF ) karyotype gens and Southern blotting . Strains containing a TLO-adjacent URA3 were grown in liquid YPAD and plated for single colonies onto YPAD for no selection of URA3 expression , synthetic complete media ( SDC ) lacking uracil to select for URA3 expression , and 5-floroorotic acid ( 5-FOA ) to select for URA3 silencing [70] . Five colonies from each condition for three different experiments were assayed for gene expression by qRT-PCR as described above . | Genetic diversity is often high at telomeres , the chromosome ends where genes are readily amplified and modified . Phenotypic diversity , e . g . , growth properties under a given condition , is affected by stochastic variations in gene expression exhibited among cells in a homogenous environment . Our studies found that individual subtelomeric genes show high variability of gene expression both between cells within a single population and also between separate sub-populations . Cell-to-cell variation , termed Telomere-Adjacent Gene Expression Noise ( TAGEN ) , affected single telomeric genes . We found that classical telomeric silencing and TAGEN are tightly linked , with both being dependent upon proximity to telomeres and the Sir2 chromatin modifying enzyme . In addition , both are coordinately regulated locally—at the DNA level: at a telomere with transcription that is continually silenced or activated , the level of expression variability is reduced . This work provides experimental support for computational work that predicted this relationship between stochastic chromatin silencing and expression plasticity at each telomere individually . Furthermore , it demonstrates that these shifts affect the degree of cell-to cell noise of telomere-adjacent loci . | [
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... | 2014 | Silencing Is Noisy: Population and Cell Level Noise in Telomere-Adjacent Genes Is Dependent on Telomere Position and Sir2 |
Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain . Coherent activity has now been measured experimentally in many regions of mammalian cortex . Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets , i . e . , in stereotypical , correlated spiking patterns of neural activity . Due to their relevance to coherent spiking , synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena . However , for some time , it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude , or fails to propagate . This has limited the classical synfire chain’s ability to explain graded neuronal responses . Recently , we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes . In particular , we showed that it is possible to use one synfire chain to provide gating pulses and a second , pulse-gated synfire chain to propagate graded information . We called these circuits synfire-gated synfire chains ( SGSCs ) . Here , we present SGSCs in which graded information can rapidly cascade through a neural circuit , and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time . We show that SGSCs are robust in the presence of variability in population size , pulse timing and synaptic strength . Finally , we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained , spike-based , modular neural circuit that is triggered by streaming input , processes the input , then makes a decision based on the processed information and shuts itself down .
Functioning neuronal networks need to store , transmit , integrate and transform their inputs to achieve the neural computation performed by the brain . How this happens in vivo has not been understood . Many proposed mechanisms rely on rate model formulations with proposed mechanisms ranging from how oscillations are generated [1] , to how anatomical architecture can maintain working memory [2] , to how long time scales can emerge from the heterogeneous ( recurrent , feedforward and feedback ) connectivities in the primate cortex [3] . With the advent of modern supercomputing , there have been efforts to simulate the entire ( or a large portion of the ) brain with realistic spiking neurons [4–6] . There has also been work using spiking models to perform Bayesian inference [7 , 8] , statistical machine learning algorithms [9] , arbitrary , highly accurate linear maps [10] , and predictive coding [11] . Numerical studies investigating fundamental computational mechanisms such as information propagation [12–15] have shown that it is possible to transfer firing rates through feed-forward networks when there is sufficient background activity to keep the network near threshold [16] . Further studies have shown that additional coherent spatio-temporal structures ( e . g . hubs or oscillations ) can stabilize the propagation of synchronous activity and select specific pathways for signal transmission [17–20] . Despite many constructive examples , how general computation ( i . e . a Turing complete framework ) can be performed using spikes or firing rates remains an open problem . From the above studies and the large literature on this subject , it is seen that there is a range of levels of approximation at which to understand and model neural function . Marr categorized this range [21] as: 1 ) studies at the computational level that ask what the brain does and why , 2 ) studies at the algorithmic level that attack how the brain performs specific computations , the information representations used and the processes that manipulate this information , and 3 ) studies at the implementational level that look for biophysical mechanisms that make up the building blocks to be used at higher levels . We have demonstrated an information propagation mechanism at the implementation level that may be used as a fundamental building block to construct higher level information processing algorithms , and , we hope , may be used to build yet more abstract structures such as the cognitive operating system used by the brain with its many regions and sub-structures [22] . Our mechanism makes use of gating pulses to propagate information in the form of graded pulses from layer to layer . Experimental evidence supports the idea that information in the brain propagates in discrete spike packets , such as the pulses that arise from our mechanism . Luczak , MacNaughton and Harris ( LMH ) have recently laid out evidence that stereotypical and repeating spike sequences consisting of packets of spikes constitute basic building blocks for neural coding [23] . They show that spike packets have been observed in many different regions of cortex . Thus , they suggest , packet-based information processing is likely to be conserved across much of the brain . Information packets by themselves are insufficient to form the basis for an information processing system . The system must also be powerful enough to perform general computation . That is , it must be as powerful as a universal Turing machine and be capable of storing , transmitting , integrating and transforming information . In our previous work , we demonstrated that graded information could be faithfully propagated through many layers of a neural circuit and that arbitrary linear maps could be performed using appropriate synaptic connectivities [22] . However , one thing that was lacking was the ability to perform decisions . Once a neural circuit can perform a decision , the circuit itself can control subsequent processing , providing the capacity necessary for general computation . In this paper , after providing a more general understanding of graded information propagation than is discussed in our previous work , we demonstrate that , by allowing graded information to interact with neural gating populations , decisions can be made within our pulse-gated information processing framework . We show how this works by implementing a self-contained , spike-based , modular neural circuit that is triggered by an input stream , reads in and processes the input , generates a conditional output based on the processed information , then shuts itself off .
In Fig 1 , we show how graded information may be propagated in an SGSC neural circuit ( Fig 1A ) . This circuit consists of two feedforward networks . One network ( gating chain ) , set up to operate in the attractor synfire regime , generates a fixed amplitude pulse that propagates from layer to layer ( Fig 1D and 1E ) . The second network ( graded chain ) receives gating pulses from the gating chain and is capable of propagating graded currents and firing rates from layer to layer ( Fig 1B and 1C ) . The gating chain delivers pulses offset by time T0 to the graded chain rapidly enough that there is an overlap in the integration of graded information and its transmission from one layer to the next . Graded information , in the form of synaptic currents and firing rates , is faithfully propagated across all 12 layers in the simulation . The observation that spike volleys in successive layers of the SGSC overlap in time led us to consider an extension of our previous mean-field model [22] to allow for the integration of graded information in successive populations to overlap in time . As in our previous work , we consider the idealized case in which the gating pulses are square . In Fig 2 , we show a translationally invariant solution ( Fig 2A ) and gating pulses ( Fig 2B ) from such a mean-field model . Successive gating pulses of length T are offset by time T0 . The solution is divided into segments which are the result of the integration of spikes in the corresponding segment ( shifted by T0 ) from the previous layer during the gating pulse . In the I&F model , both T and T0 result from intrinsic neuronal dynamics and synaptic delays . See Materials and Methods and SI Appendix 2 for the specific parameters that we used in our simulations . For fixed T and T0 , we find time translationally-invariant solutions for synaptic input currents for special values of the feedforward coupling strength , S = Sexact , in the mean-field model ( see Materials and Methods and SI Appendix 1 ) . In Fig 2C , we plot Sexact as a function of η = T/T0 , where η is a measure of the overlap in the integration and transmission of graded information . Note that Sexact becomes flatter as the overlap , η , gets larger . This implies that , for large overlaps , any propagation error in the solution due to deviations from Sexact is small . Thus , in the large η regime , information propagation is robust to variability in both pulse timing and coupling strength . For practical purposes , we find that η > 2 or 3 is sufficiently robust . Furthermore , for a generic feedforward network , there exists a wide range of S ( roughly , S from 1 to 2 . 7 ) where we can find time translationally-invariant solutions for which graded propagation is possible . In Fig 3 , we explore whether our mean-field theory could be used to model our I&F simulation results . First , we determined the parameters ( ηfit , αfit ) that gave the best-fitting mean-field solution to the simulation data , given known T0 . Here , we define α ≡ S ( T0/τ ) e−T0/τ , so that α = 1 at η = 1 . In this expression , τ represents a synaptic time scale ( see Materials and Methods ) . Next , using the simulational synaptic coupling , αsim , we found η = ηsim that corresponded to the time-translationally invariant solution of the mean-field model . Closeness of these two points would give evidence that the mean-field theory , despite the simplications used to derive it ( e . g . precisely timed square gating pulses , linear f-I curve , etc . ) , can be used to model the I&F simulation . We show details of this fitting procedure for two different T0’s ( Fig 3A–3D ) , and summarize the results for T0 = 0 . 003 , 0 . 004 , 0 . 005 , 0 . 006 ( Fig 3E ) . The closeness of model fits with simulation results , for a wide range of overlaps , indicates that the mean-field theory is a good model of the SGSC simulation . In Fig 4 , we investigate the robustness of pulse-gated synaptic current transfer in the SGSC to finite-size effects , variability in synaptic coupling , and inaccuracies in pulse timing . As would be expected , transfer variability decreases as 1 / N 1 ( Fig 4A and 4B ) . Randomness in synaptic coupling either in the gating chain or the coupling between chains has little effect on the variability ( compare Fig 4C and 4D with Fig 4B ) . As we mentioned above , this is expected due to the flatness of Sexact ( η ) for large η . Here , η = 2 . 5 . Similarly , jittering T0 has little effect on the variability of current transfer ( Fig 4E ) . Pulse-gated propagation mechanisms , such as the SGSC , naturally give rise to a probabilistic , spike-based information processing framework in which information , in the form of spiking probabilities , is processed by graded chains and the flow of information is controlled by gating chains [22] . Additionally , logic operations may be performed by allowing graded information to interact with the pulse generator ( see Materials and Methods ) . In previous work [22] , we demonstrated a number of example circuits that made use of pulse-gated networks to control the flow of graded information through a neural circuit . We showed how a working memory could be constructed . We also showed how matrix transforms , such as a Hadamard transform ( a Fourier transform using square-wave , Walsh functions with different frequencies as a basis ) , could be performed on streaming input . Finally , we showed how pulses could be used to re-entrantly guide information through neural subcircuits to perform iterated computations . Here , to illustrate the capability of pulse-based information processing to perform complex computations , we show results from a toy model circuit meant to serve a representative function of the brain: to process incoming , time-dependent information , then make a decision based on the processed data . For instance , a similar ( non-toy ) circuit would implement the recognition of a person’s face , transformed from visual information as it streams into the visual cortex , or the recognition of a word or phrase in the auditory system . Upon recognition , the circuit might send on the information and transfer control to a downstream circuit responsible for the reaction to the recognition . The neural circuit that we demonstrate is triggered by a streaming input . The input is a function that oscillates in time . The circuit encodes the input in graded pulses , then transforms it in order to determine frequency and phase information . It then makes a decision based on the transformed input that affects subsequent processing ( Fig 5 ) . The neural circuit consists of ( see Fig 5A ) 1 ) a trigger , 2 ) a module used to keep sampled streaming input in short-term memory , 3 ) a 4 × 4 Hadamard transform , 4 ) a second set of Hadamard outputs ( Hadamard Copy ) representing output copy to a downstream circuit , 5 ) an Input population , 6 ) a Shut Down population to terminate processing , 7 ) a Compute gating chain to drive the computation , 8 ) a Vigilance gating chain that serves as a processing indicator and clock to synchronize the triggering of an output decision , 9 ) an Output Copy gating chain that serves as a decision indicator and is turned on based on the amplitude of the ( arbitrarily chosen ) 8’th Hadamard coefficient , and 10 ) Logic populations for a ) triggering the computation and b ) making the decision to copy the Hadamard output . Output then triggers circuit shutdown by inhibiting all gating chains . This probabilistic , spike-based algorithm uses a self-exciting population coupled to a streaming input to trigger the computation ( see Fig 5B , 5C , 5D and 5E ) , then continuously gates 4 sequential input amplitudes into 4 read in populations and maintains the input values by gating them through working memory populations until all values are simultaneously in 4 working memory populations . The values in working memory are next gated to Hadamard populations transforming the input values into Hadamard coefficients ( one set of positive coefficients and one set of absolute values of negative coefficients [22] ) . At this point , a time-windowed Hadamard transform has been computed on the input . Input for the simulation in Fig 5 is sinusoidal , so the Hadamard transform , which gives a frequency-based representation of the input , outputs phase and frequency information for the sinusoid . Gating pulses are interleaved such that this computation is performed iteratively on successive windows of length 4T from the streaming input . To implement a conditional copy of the transformed data , we combine the output of the ( arbitrarily chosen ) eighth Hadamard coefficient in the present Hadamard output ( representing that a particular frequency and phase of the input was detected ) and the first population in the “Vigilance” gating chain . This operation causes the graded pulse to activate the Output Copy chain when its amplitude is sufficiently high , conditionally causing a pulse to cascade through 4 gating populations with the last population gating the transfer from the subsequent Hadamard output to the 8 output neurons . Once the Hadamard output is copied , it activates the Shutdown population , which inhibits all populations in the gating chains , terminating the computation .
In this Section , we first discuss some technical issues related to the models and circuits that we presented above . Second , we present some ideas for understanding some experimental aspects of spike packets , à la LMH , using our SGSC-based information processing framework . Third , we discuss aspects of oscillatory coherent activity and its possible relationship to pulse-gating . Finally , we discuss some distinctive aspects of information processing using SGSCs and their use . Technical Issues: In parallel circuits such as the decision making circuit depicted in Fig 5 , race conditions can exist . This occurs when the relative arrival times of various pieces of information ( in our context , graded or gating pulses ) are imprecise and thus corrupt a computation . Such a problem could exist in our decision making circuit , however , note that the problem is resolved by the fact that the gating chain ( as may be seen in K22 ) uses a single chain of populations to gate the working memories and the Hadamard transform . Therefore , as long as gating is consistently timed , there can be no race condition in this circuit , since the pulses that control the parallel transfer of information arise from the same source , and thus gate the entire working memory or Hadamard populations at the same time . Furthermore , due to our use of an attractor synfire chain for gating , the timing is stereotypical and consistent . The fact that our idealized mean-field model predicts the value and the range of synaptic strengths for exact transfer so well relies on an underlying time-translational invariance of the spiking and membrane potential probability distribution functions . This correspondence can be made more explicit by using a Fokker-Planck approach and suggests that a mean-firing rate description may work well even when the number of neurons in each population is not so large . Our I&F simulations indicate that N ≈ 100–1000 is sufficient . Analysis of our Fokker-Planck simulations suggests that the dynamics of graded transfer may be captured by the mean firing rate and a few additional variables encapsulating the equilibrium voltage probability distributions [24] . Ongoing work aims to clarify the conjunctive role of the gating current , the synaptic coupling strength , Sexact , and the state of each neuronal population ( as described by its membrane potential distribution ) in the transmission of firing rates and rate correlations . Packets: In our propagation mechanism , an upstream neuronal population sends spikes downstream , the spikes are integrated in the synaptic current of the downstream population , but unless a gating pulse is provided by a pulse-gating population to the downstream population , the information does not propagate . Gating pulses are provided by a feedforward chain with strong connectivity . Graded information propagates through neural assemblies with sparse , weaker connectivity . LMH conjecture that the cortex rests on a skeleton of stronger connections immersed in a sea of weaker ones . Thus , a correspondence may exist between the skeleton of strong connections and a gating chain and the sea of weakly connected neurons and a graded chain . Other evidence LMH discuss is: 1 ) spike-timing reliability progressively decays during a packet giving way to stimulus related firing rates . In our framework , if one considers the combined gating and graded pulses to be a spike packet received by a neuron in a graded population , then the gating pulse occurs at the beginning of a packet , where gating and graded spikes are combined early on . Later spikes in the packet , after the gating pulse ends , consist of spikes from the graded waveform , which decays as spikes are integrated downstream . The early gating pulse would be expected to be the most stereotypical aspect of the mechanism , and gating is purely timing related so many early spikes should be strongly correlated with timing . Also , later spikes ( after the gating pulse ends ) would consist purely of graded information , and so the transformation to largely firing-rate type statistics would also be expected . 2 ) Later spiking in a group of packets may represent feedback containing information concerning behavioral choices . We have constructed and demonstrated re-entrant circuits in previous work , that are nonetheless pulse-gated . In these circuits information is retained in packet form , but can be used to modify subsequent circuit properties . So , pulse-gating and feedback are not inconsistent and may be incorporated in the same packet-based information processing system . 3 ) LMH mention a wide range of functions that packets can serve , including triggering of firing patterns , recall and imagination of sensory stimuli , and attention . The neural circuit of Fig 5 demonstrates three of these functions: pattern triggering via switching on an SGSC , recall in the form of a working memory and attention in the form of the Vigilance gating pulses , and it is straightforward to envision other functions . Coherent Activity: The emerging picture from accumulating experimental evidence is that coherent activity is a fundamental contributor to cognitive function . Accumulating experimental evidence implicates coherent activity as an important element of cognition . Since its discovery [25] , activity in the gamma band has been demonstrated to exist in numerous regions of the brain , including hippocampus [26–28] , numerous areas in cortex [25 , 29–34 , 34–37] , amygdala and striatum [38] . Gamma band activity is associated with sharpened orientation [39] and contrast [40] tuning in V1 , and speed and direction tuning in MT [41] . Attention has been shown to increase gamma synchronization between V4 and FEF [36] , LIP and FEF [34] , V1 and V4 [42] , and MT and LIP [43] . In general , communication between sender and receiver neurons is improved when consistent gamma-phase relationships exist between upstream and downstream sites [30] . Theta-band oscillations are associated with spatial memory [44 , 45] , where neurons encoding the locations of visual objects and an animal’s own position have been identified [44 , 46] . Loss of theta results in spatial memory deficits [47] and pharmacologically enhanced theta improves learning and memory [48] . As defined mathematically ( e . g . [49] , p . 207 ) , coherence ( alternatively , correlation of a signal at a given lag ) is a measure of the efficacy of univariate information transfer between neuronal populations . Note that a matrix-valued definition of coherence is needed to measure the efficacy of multivariate information transfer . Here we have demonstrated a coherent transfer mechanism that dynamically routes graded information through a neural circuit using stereotyped gating pulses and makes decisions via non-linear coupling of graded and gating pulses . As we have shown , SGSCs can be used as building blocks to implement complex information processing algorithms , including sub-circuits responsible for short-term memory , linear maps , and computational logic . As such , synfire-gated synfire chains should be considered as a candidate mechanism whenever coherent activity is implicated in information transfer . We suggested in our previous work that a natural manifestation of neural circuits that repeatedly analyze passive streaming input would be the existence of oscillatory , sub-threshold pulses generated by pulse-gated control signals from gating chains . However , coherent , oscillatory activity in the gamma-band , either spiking activity or sub-threshold voltage oscillations , is typically a transient phenomenon , at least in visual cortex [50] . This makes sense in the computational context that we consider here and is exemplified in the final example in Results . There , processing of streaming information is initiated , requiring repeated ( oscillatory ) sampling of the input . But the neural circuit is subsequently switched off by logic internal to the circuit . Measurements of such circuits would show transient oscillatory coherent activity . An important implication of pulse-gated information processing for experiment is that the gating rhythms and patterns controlling information flow in a neural circuit will depend on the structure and time scales of the underlying algorithm that the brain implements . For instance , in the circuit in Fig 5 , the oscillation frequency is determined by the length of the cyclic gating chains . Thus , different algorithms may be able to be distinguished based on the brain rhythms that they evoke . Alternatively , by observing gating patterns , putative computational algorithms might be able to be determined from brain rhythms . Rapid visual categorization ( RVC ) experiments have demonstrated that objects can be recognized as early as 250–300 ms after presentation . It has been conjectured that massively parallel , feedforward networks are used during RVC computations for maximum speed [12 , 51–53] . With pulse lengths of 25 ms in an SGSC , 10–12 feedforward processing layers would be needed to construct such a network ( Fig 1 ) . The signal-to-noise ratios that we demonstrated for the SGSC ( Fig 4 ) are good enough that it could be used for this type and rate of information transfer . Indeed , in our examples , we show rapid propagation of graded information with 3 ms pulses . In [54] , using calcium imaging in mouse primary visual cortex slices , similar activity to that of a synfire chain was detected . Coactive activity occurred within a 3 ∼ 11ms time window , which is similar to the synfire chain in our SGSC . Spike timing in the spiking pattern was preserved . There is also strong experimental evidence for synfire chains in birdsongs . In [55] and [56] , the authors found repeated bursting activity during bird songs that was well-characterized as a synfire chain . SGSC: To our minds , the success of the SGSC graded information propagation mechanism rests on the structural robustness of the pulse gating mechanism . One contribution to robustness is that the synfire chain that is used for gating pulse generation approaches a fixed amplitude attractor with fixed temporal offset . A second contribution is that by providing overlapping temporal windows for information integration , the constraints on parametric precision to achieve graded information transfer are relaxed ( Fig 2C and related text ) . Having said that , the correspondence between our mean-field model and the SGSC gives weight to the idea that pulse-gating , independently of how it is implemented , is a robust mechanism for controlling information transfer in neural circuits . Thus , there is not a particular reason that other pulse generators should not be entertained . For instance , experiments implicate the PVBC/OLM system of interneurons in cortical pulse generation [57] . A conceptual framework for the manipulation of information in neural circuits arises naturally when one considers graded information transfer in the context of coherently interacting neuronal assemblies . In this framework , information processing and information control are conceived of as distinct components of neural circuits [22] . This distinction has been used previously [17 , 18 , 58 , 59] in theoretical mechanisms for gating the propagation of fixed ( non-graded ) amplitude waveforms . Independently , structures devoted to information gating have been observed experimentally ( see [60] for a review ) . One such circuit is the hippocampus/mediodorsal thalamus ( MD ) /ventral tegmental area ( VTA ) /prefrontal cortex ( PFC ) . In this circuit , both MD and VTA have been shown to gate the hippocampal-PFC pathway [61] . Additionally , frontal and basal ganglia activity has been shown to gate access to working memory in human parietal cortex [62] . Here , by providing a mechanism for the propagation of graded information and including computational logic by allowing graded and gating chains to interact , active linear maps ( see Materials and Methods ) take prominence as a key information processing structure . It is worth mentioning that when we constructed the neural circuit example in Fig 5 , we started at the algorithmic level , then implemented the algorithm in the mean-field firing rate model , then translated the mean-field model into the spiking , I&F network . We feel that this is a major strength of the SGSC-based information processing framework , i . e . that it provides a practical pathway for designing computational neural circuits , either for the purpose of forming hypotheses about circuits in the brain , or for implementing algorithms on neuromorphic chips [63] .
Individual current-based , I&F point neurons in the SGSC have membrane potentials described by d d t v i , j σ = - g l e a k v i , j σ - V l e a k + ∑ σ ′ = 1 2 I i , j σ σ ′ + I i , j σ ( 1 ) τ d d t I i , j σ σ ′ = - I i , j σ σ ′ + S σ σ ′ p σ σ ′ N σ ′ ∑ i ′ ∑ l δ t - t i ′ , j - 1 σ ′ , l ( 2 ) τ d d t I i , j σ = - I i , j σ + f σ ∑ l δ t - s i , j l ( 3 ) where σ , σ′ = 1 , 2 with 1 for the graded chain and 2 for the gating chain , i = 1 , … , Nσ denotes the number of neurons per population for each layer , j = 1 , … , M denotes the layer; individual spike times , { t i , j σ , l } , with l denoting spike number , are determined by the time when v i , j σ reaches Vthres . The parameters gleak and Vleak denote the leak conductance and the leak potential . We have used reduced dimensional units in which time retains dimension in seconds and Vthresh − Vleak = 1 . In these units gleak = 50/sec . The parameter τ denotes the synaptic timescale ( τ = 5 ms , or approximately an AMPA synaptic timescale , in the Results above ) . The current I i , j σ σ ′ is the synaptic current of the σ population produced by spikes of the σ′ population . The parameter Sσσ′ denotes the synaptic coupling strength and the neurons from layer σ to layer σ′ are connected in an IID fashion , with coupling probability given by pσσ′ . I i , j σ is a background noise current generated from Poisson spike times , { s i , j l } , with strength fσ and rate νσ . General SGSC circuits can incorporate a number of subcircuits , such as short-term memory and processing due to non-trivial synaptic connectivities [22] such as the circuit shown in Fig 5 ( Results ) . In this case , more general connectivities are needed and the above equations become d d t v i , j σ = - g l e a k v i , j σ - V l e a k + ∑ σ ′ = 1 2 I i , j σ σ ′ + I i , j σ ( 4 ) τ d d t I i , j σ σ ′ = - I i , j σ σ ′ + S p σ σ ′ N σ ′ ∑ k K j k σ σ ′ ∑ i ′ ∑ l δ t - t i ′ , k σ ′ , l ( 5 ) τ d d t I i , j σ = - I i , j σ + f σ ∑ l δ t - s i , j l ( 6 ) Here , the synaptic connectivity for the graded chain is K j k 11 , the coupling between the chains is K j k 12 , and the connectivity of the gating chain is K j k 22 . Interaction between the graded chain and the gating chain is given by K j k 21 . We use K j k 21 to implement conditional logic operations . To analyze graded propagation for the case in which the integration of graded information in successive populations overlaps in time , we assume that the gating pulse is square with amplitude sufficient to bring neuronal populations up to the firing threshold . We also assume that above threshold the activity function is linear [22] . Firing in the upstream population is integrated by the downstream population . Thus , the downstream synaptic current obeys τ d d t I d = - I d + S m u , where S is a synaptic coupling strength , Id ( t ) is the downstream synaptic current , and τ is a synaptic timescale . In a thresholded-linear model , the upstream firing rate is m u ≈ I u ( t ) + I 0 E x c - g 0 + , where I 0 E x c = g 0 p ( t ) is an excitatory gating pulse , p ( t ) = θ ( t ) − θ ( t − T ) and θ is the Heaviside step function , causing the downstream population to integrate Iu ( t ) , giving the current G I d ( t ) ≡ S e - t / τ ∫ 0 t d s e s / τ I u ( s ) + c . The graded population is pulsed for time T . The offset between successive gating pulses is given by T0 ( see Fig 2 , Results ) . In [22] , we studied the case where T = T0 . That is , the downstream pulse turned on just when the upstream pulse turned off . Here , we focus on the case where η = T/T0 > 1 , and η need not be an integer . Let n be the integer part of η . Then T = nT0 + T1 , where T1 < T0 . In the SI Appendix 1 , we give a general derivation of time translationally invariant solutions in this context . In brief , a translationally invariant , graded current waveform is found for a particular feedforward strength , S = Sexact , by integrating the upstream firing rate over intervals of length T1 , T0 , … , T0 , while enforcing continuity of the solution . For these solutions , Sexact is given by the smallest root of ∑ j = 0 n ( - 1 ) j ( n - j ) ! ( j + 1 ) T 0 - T 1 τ S e - T 0 / τ n - j = 0 . As we demonstrate in Results , current amplitude transfer for the SGSC may be modeled with a piecewise linear activity function , therefore synaptic connectivities between two layers each containing a vector of populations , perform a linear operation on the currents in the upstream layer [22] . For instance , consider an upstream vector of neuronal populations with currents , Iu , connected via a connectivity matrix K to a downstream vector of neuronal populations , Id: I u ( t ) → K I d ( t ) . ( 7 ) With feedforward connectivity , K , the current amplitude , Id , obeys τ d d t I d = - I d + S e x a c t K I u + p u ( t ) - g 0 + , ( 8 ) where pu ( t ) denotes a vector gating pulse on the upstream population . This results in the solution Id ( t − T ) = PK Iu ( t ) , where P is a diagonal matrix with the gating pulse vector , p , of 0s and 1s on the diagonal indicating which neurons were pulsed during the transfer . This discussion has identified three components of an information processing framework that naturally arises from mechanisms such as the SGSC: Note that the pulsing control , p , serves as a gating mechanism for routing neural information into ( or out of ) a processing circuit . We , therefore , refer to amplitude packets , I , that are guided through a neural circuit by a set of stereotyped pulses as “bound” information . In the SGSC , information content is carried by the graded chain ( e . g . Fig 5B and 5D ) , information processing is performed by the synaptic connectivities ( e . g . Fig 5A ) and information control is performed by the gating chain ( e . g . Fig 5C and 5E ) . We will refer to the combination of these control and processing structures as active linear maps . In order to make a decision , non-linear logic circuits are introduced . A simple decision can be implemented in our framework by allowing interaction between information control and content . In our example , a graded and a gating pulse were combined to make a decision , then the output was fed as input to a gating chain . If the graded chain output a low value , the gating chain was not switched on . However , if the graded chain output was high , this initiated pulses in the gating chain , which rapidly approached an attractor . Thus , the interaction caused conditional firing in the gating chain . | Cognitive tasks are associated with the dynamic excitation of neural assemblies . When we consider how quickly and flexibly such collectives may be formed and incorporated in a task , a persistent question has been: how can the brain rapidly evoke and involve different neural assemblies in a computation , when synaptic coupling changes only slowly ? Here , we demonstrate mechanisms whereby information may be rapidly and selectively routed through a neural circuit , and sub-circuits may be turned on and off . The resulting information processing framework achieves the goal that has been pursued , but until now largely not attained , of achieving faithful , flexible information transfer across many synapses and dynamic excitation of neural assemblies with fixed connectivities . | [
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"logic",... | 2016 | Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains |
p63 is a multi-isoform member of the p53 family of transcription factors . There is compelling genetic evidence that ΔNp63 isoforms are needed for keratinocyte proliferation and stemness in the developing vertebrate epidermis . However , the role of TAp63 isoforms is not fully understood , and TAp63 knockout mice display normal epidermal development . Here , we show that zebrafish mutants specifically lacking TAp63 isoforms , or p53 , display compromised development of breeding tubercles , epidermal appendages which according to our analyses display more advanced stratification and keratinization than regular epidermis , including continuous desquamation and renewal of superficial cells by derivatives of basal keratinocytes . Defects are further enhanced in TAp63/p53 double mutants , pointing to partially redundant roles of the two related factors . Molecular analyses , treatments with chemical inhibitors and epistasis studies further reveal the existence of a linear TAp63/p53->Notch->caspase 3 pathway required both for enhanced proliferation of keratinocytes at the base of the tubercles and their subsequent differentiation in upper layers . Together , these studies identify the zebrafish breeding tubercles as specific epidermal structures sharing crucial features with the cornified mammalian epidermis . In addition , they unravel essential roles of TAp63 and p53 to promote both keratinocyte proliferation and their terminal differentiation by promoting Notch signalling and caspase 3 activity , ensuring formation and proper homeostasis of this self-renewing stratified epithelium .
The mammalian epidermis is a self-renewing stratified epithelium on the outer surface of the skin . During embryogenesis , it develops from the surface ectoderm , which is initially a single-layered epithelium . Stratification is initiated ( E10 in mouse ) with the formation of the outer periderm , leading to a bi-layered epidermal organization in which peridermal cells are attached to each other via tight junctions to protect the embryo against the amniotic fluid [1] , [2] . Further epidermal maturation ( E12 . 5–E17 . 5 in mouse ) takes place beneath with the consecutive formation of the spinous , granular and cornified layers , establishing the later epidermal barrier , while the periderm is sloughed off [3]–[6] . Crucial contributions to this later epidermal barrier come from the granular layer , in which cells are sealed to each other via tight junctions , and from the outer cornified layers , providing physical resistance and preventing dehydration . Formation of this water barrier is essential for the adaptation to terrestrial life both during the ontogeny [7] and the evolution [8] , [9] of land-based vertebrates . Accordingly , fish lack epidermal cornification . In zebrafish , the embryonic and larval epidermis is bi-layered , consisting of an outer enveloping layer ( EVL ) segregating from inner cells before surface ectoderm specification [10] , [11] , and a basal keratinocyte layer , resembling the bi-layered organization of the mammalian epidermis at midgestation stages . Further stratification of the zebrafish epidermis only commences with the onset of metamorphosis ( after three weeks of development ) , during which the epidermis becomes multi-layered . However , keratinocytes remain metabolically active throughout , including the outer-most layer , and lack morphological signs of cornification [12] , [13] . This is in contrast to the organization of breeding tubercles , contact and secondary sex organs on the head and pectoral fin rays . Based on morphological studies in several other fish species , they were described as “keratinized epidermal appendages covered by a layer of dead cells with altered content” [14] , suggesting that in breeding tubercles , keratinocytes might undergo more advanced , cornification-like differentiation processes . Mammalian keratinocyte cornification is a multi-step process initiated by a switch in the expression of particular keratin genes , followed by the expression of the keratin-bundling protein filaggrin and proteins such as involucrin and loricrin , which together with keratins become cross-linked by transglutaminases ( Tgm1-4 ) to reinforce the formation of a cornified envelope [15]–[17] . Furthermore , lipids stored in lamellar bodies are extruded into the extracellular space to form a lipid envelope . During these later steps , cells enter an apoptotic-like phase , lose cytoplasmic organelles including the nucleus , and are eventually sloughed from the skin surface . This loss of corneocytes by desquamation is tightly balanced by keratinocyte proliferation in basal layers , allowing constant self-renewal during epidermal homeostasis . A key regulator of mammalian epidermal stratification and keratinocyte proliferation and differentiation is p63 , a homolog of the tumour suppressor and transcription factor p53 [18]–[24] . Use of alternative promoters of the Tp63 gene gives rise to two isoform categories: those with an N-terminal transactivation domain ( TAp63 isoforms ) , as also present in p53 , and those lacking this domain ( ΔNp63 isoforms ) and acting as inhibitory competitors of their TA counterparts . In addition , alternative splicing gives rise to at least three different C-terminal isoforms ( α , β , γ ) in each category , and to different N-terminal isoforms of TAp63 [25] . Recent analyses of mice specifically lacking the ΔN isoforms indicate that ΔNp63 is required for maintaining the proliferative potential of basal keratinocytes in embryonic epidermis while preventing their premature entry into terminal differentiation [26] , consistent with findings obtained in cultured keratinocytes [27]–[29] . In contrast , TAp63 and p53 might promote keratinocyte differentiation . Thus , in inducible cell lines , TAp63 activates genes involved in keratinocyte differentiation , including different tgms [30] . Also p53 , via direct and indirect activation of Notch signalling , has been shown to be required for keratinocyte differentiation in cell culture systems and during squamous cell carcinoma suppression in mouse tumour models [31] , [32] . siRNA-mediated knockdown studies in organotypic cultures of human keratinocytes further suggest that it is primarily p53 that antagonizes the proliferation-stimulating effect of ΔNp63 , and that the contribution of TAp63 to keratinocyte differentiation is minor compared to that of ΔNp63 [29] , [33] . Furthermore , TA-specific p63 mutant mice lack an abrogation of keratinocyte differentiation [34]–[36] , leaving the in vivo role of TAp63 during keratinocyte development unclear . Notch signalling promotes different steps of mammalian keratinocyte differentiation in vivo [37]–[41] . Upon binding of Delta or Jagged ligands , the Notch receptor is cleaved and releases its intracellular domain ( NICD ) , which binds to the transcriptional repressor RBP-J , enabling it to activate target genes that are repressed in the absence of Notch signals . Findings concerning the interconnection between p53/p63 and Notch signalling are controversial . Thus , Jagged1/2 and Notch1 have been reported to be positively regulated by p53 and TAp63γ [31] , [42]–[46] , but also by ΔNp63 [26] , [43] , while in other systems , the Notch target Hes1 was negatively regulated by both TAp63 and ΔNp63 [43] . How Notch signalling promotes keratinocyte differentiation is also incompletely understood [38] , [47] . One described Notch/RBP-target gene in embryonic mouse keratinocytes encodes caspase 3 [39] . This cysteine protease not only executes apoptosis , but also promotes terminal differentiation processes in a range of cell types [48] , leading to delayed basal keratinocyte differentiation in caspase 3 mutant mouse embryos [39] . Here , we identify the zebrafish breeding tubercles as sites with higher keratinocyte proliferation in basal layers as well as more advanced , cornification-like keratinocyte differentiation in upper layers , including transglutaminase expression and stronger keratinization , exclusive presence of tight junctions in second-tier keratinocytes , and rudimentary lipid envelope formation and constant desquamation and renewal of surface keratinocytes . In addition , we identify zebrafish TAp63 isoforms and a TA-specific p63 loss-of-function mutant , revealing essential and partially redundant roles of TAp63 and p53 to promote both keratinocyte proliferation at the base and terminal keratinocyte differentiation in upper layers of breeding tubercles . Both effects are mediated via Notch signalling and activated caspase 3 , although these mediators are restricted to upper breeding tubercle layers , pointing to a combination of cell autonomous and non-cell autonomous effects . These findings will help to better understand the seemingly controversial roles described for TAp63 and p53 in different systems .
Even after metamorphosis ( approximately 30 days post fertilization; dpf ) , when the zebrafish epidermis has become multi-layered , superficial epidermal cells display crucial similarities to the embryonic enveloping layer ( EVL ) , like the presence of tight junctions and distinct microridges at their outer surface [12] . To determine the developmental origin of superficial cells of the adult zebrafish epidermis , we carried out transgenic lineage tracing experiments , using promoter elements that drive embryonic expression confined to the outer EVL ( krt4 ) [49] or the basal layer ( krt19 ) [50] , respectively ( Figure 1A; 5 dpf ) . Employing these promoters in an inducible binary transgenic Cre/Lox-system , embryonic EVL cells or basal keratinocytes were stably labeled via tamoxifen application from 1–4 dpf ( Figure 1B–F ) . At 60 dpf , superficial cells of the regular body epidermis consisted of a mixture of derivates of embryonic EVL cells ( Figure 1B ) and basal keratinocytes ( Figure 1C , D ) . However , both expressed the same marker genes ( Figure 1B–D; and data not shown ) and displayed identical morphological characteristics ( Figure 1E ) . This suggests that in regular epidermis , embryonic EVL cells can persist beyond metamorphosis and are only slowly replaced by derivatives of basal keratinocytes . However , renewed superficial cells seem to have the same properties and functions as persisting EVL cells . A different pattern was observed in spike-like epidermal structures that according to their location and fine structure ( see below ) were identified as breeding tubercles [14] . Here , superficial cells consisted solely of derivatives of basal keratinocytes and failed to express markers of the embryonic EVL and of the superficial cells of the regular epidermis ( Figure 1D; n = 13/13 ) , suggesting that they have been renewed completely and have acquired a different fate . In adult zebrafish , breeding tubercles were exclusively present in a disc- and row-like structure on each side of the lower jaw ( Figure 2A ) , as well as in rows along the bony rays of the pectoral fins of males ( Figure 2B ) , but not females ( Figure 2C ) . Scanning electron microscopy ( SEM ) revealed that superficial cells of breeding tubercles , also called cap cells [14] , lacked the microridges present at the outer surface of regular epidermis ( Figure 2D , E ) . In addition , they lacked expression of tg ( krt4:GFP ) ( Figure 2G ) and other specific markers of superficial cells of regular epidermis ( data not shown ) , and displayed a different cytoplasmic composition in AFOG trichrome stainings ( Figure 2F ) . Furthermore , immunofluorescence revealed a higher keratin content of tubercle keratinocytes compared to regular epidermis ( Figure 2H ) . Correspondingly , expression levels of the type II keratin gene krt8 were significantly higher in breeding tubercles than in regular epidermis ( Figure 2I ) . Furthermore , the type I keratins krtt1c11a ( ZFIN: zgc:136902 ) and krt17 ( ZFIN: zgc:92061 ) were exclusively expressed in lower tubercle layers , but not in regular epidermis ( Figure 2J and data not shown ) , while expression of type II keratin krt5 and type I cytokeratin cki [51] was shared by regular epidermis and lower tubercle layers , but absent in upper layers of the tubercles ( Figure 2K and data not shown ) . Strikingly , tgm1 [52] , encoding the cross-linking enzyme transglutaminase 1 involved in cornification of the mammalian epidermis [53] , was exclusively expressed in upper layers of the breeding tubercles ( Figure 2L ) . Striking differences were also observed at the structural level . In regular epidermis , basal and intermediary keratinocytes were of similar shapes and organized in a rather irregular pattern . In contrast , basal cells of tubercles were more regularly aligned to each other , while intermediary cells displayed a looser and spinous-like organization ( Figures 2F and 3A , B ) , connected to each other via local desmosomes , but with wide extracellular spaces in between ( Figure 3C ) . Tight junctions were only found in the second tier layer directly beneath the cap layer ( Figure 3D ) , similar to their confinement to granular cells beneath the cornified layers in mammals . Desmosomes were also present between second tier and outer cap cells . However , in many samples , they were in the process of regression , leading to a partial dissociation of the two layers ( Figure 3E ) . In addition , cap cells contained large vesicles reminiscent of lamellar bodies [54] , extruding their content into the space between cap and second tier cells ( Figure 3F ) , which was filled with material resembling the extracellular lipid lamellae in cornifying layers of the mammalian epidermis [54] ( Figure 3G ) . Gradual loss of desmosomes was also observed between adjacent cap cells , but accompanied by cell membrane deterioration and cellular fusion ( Figure 3H ) . Intracellular , cap and second tier cells contained large amounts of electron-dense granules ( Figure 3G , H ) , while apart from nuclei ( see below ) , cell organelles like mitochondria were largely absent ( data not shown ) . In sum , these data indicate that breeding tubercles display a more pronounced stratification than regular epidermis , consisting of different layers with distinct morphological properties and some cornification-like features in the superficial layer . Regular epidermis becomes multi-layered during metamorphosis [12] , with final thicknesses between 3 and 10 layers , depending on the position on the body ( data not shown ) . Breeding tubercles develop at the same time . At 21 dpf ( 6 . 0–6 . 5 mm body length ) and 24 dpf ( 6 . 5–7 . 0 mm ) , the future breeding tubercle domain on the lower jaw was still covered by microridge-bearing peridermal cells ( Figure 4A , B ) . At 28 dpf ( 7 . 5–9 . 0 mm ) , first elevations with microridge-free outer cells were present ( Figure 4C ) , which had acquired the mature spiky shape at 31 dpf ( 10 . 0–11 . 0 mm ) ( Figure 4D ) . Already at early elevation stages , tubercles were observed in which some of the smooth outer cells had been lost , exposing second tier cells with a rougher surface ( Figure 4E ) , possibly reflecting former contact points to the lost outer cells . During later stages , entire sheets of outer cells were lifted up ( Figure 4F ) and shed . Corresponding spiky discs ( Figure 4G–I ) or rows were found in the water , in particular after spawning . Confocal analyses after DAPI-staining revealed that the spikes of these sloughed structures were hollow , indicating that they consisted solely of cap layers . To investigate whether shed cap layers are renewed , we took advantage of their unique property to be readily and permissively stained with externally applied dyes like calcein ( Figure 4J ) or methylene blue , reflecting their loss of cell membrane integrity , a hallmark of cell death [55] . In contrast , second tier and deeper cells remained unstained . In daily analyses of the same fish over several weeks , calcein permeability of individual discs or rows was randomly lost within one day and only regained after 7–14 days ( Figure 4K , L; n = 12 ) , suggesting that shed cap cells are replaced by second tier cells and that it takes the latter 7–14 days to terminally differentiate . In contrast to mammalian corneocytes , cap cells of breeding tubercles still contain their nuclei . However , compared to the nuclei of basal and spinous keratinocytes ( Figure 5A , B ) , their chromatin was strongly condensed ( Figure 5C ) , similar to the pyknosis apparent during cell death [56] . Nevertheless , cap cells were TUNEL ( Terminal deoxynucleotidy transferease dUTP nick end labelling ) -negative ( Figure 5D , E ) , pointing to the absence of DNA fragmentation , whereas they showed high levels of activated caspase 3 ( aCasp3; Figure 5F ) . This cysteine protease , which does not only execute apoptosis , but also promotes differentiation of embryonic keratinocytes [39] , [48] , was also present in spinous cells in intermediary tubercle layers ( Figure 5F ) , which had normal nuclei ( Figure 5B ) and excluded externally applied dyes ( Figure 4J ) , thus lacking all hallmarks of cell death . In contrast , aCasp3 was absent from basal tubercle layers and regular epidermis ( Figure 5F ) . This aCasp3 distribution pattern was largely complementary to that of p63 , which was present in all layers of regular epidermis , but confined to lower layers of breeding tubercles ( Figure 5G ) . Cell proliferation , assayed via BrdU incorporation , displayed the same pattern like p63 , complementary to aCasp3 ( Figure 5H ) . However , proliferation rates in lower tubercle layers were significantly higher than in regular epidermis , both in fully grown ( Figure 5H , J ) as well as in developing tubercles ( Figure 5I , J ) . In sum , in contrast to regular epidermis , where keratinocytes of all layers display comparable p63 and proliferation levels while lacking activated caspase 3 , at least two domains can be distinguished in breeding tubercles: aCasp3-positive cells in the upper layers that are postmitotic and undergo more advanced differentiation , and aCasp3-negative keratinocytes at the base , with proliferation rates even higher than in regular epidermis . Furthermore , aCasp3 in upper layers is not correlated with apoptosis . In light of the described observations , we next studied breeding tubercle development in zebrafish loss-of-function mutants in p53 and TAp63 , potential regulators of keratinocyte differentiation and proliferation in mammalian cell culture systems [29] , [31]–[33] . The p53zdf1 allele bears an ( M214K ) exchange of a conserved amino acid residue in the DNA-binding domain that compromises p53 activity [57] . For zebrafish p63 , only ΔN isoforms had been described thus far [58] , [59] . However , via exon prediction of genomic sequences upstream of the ΔNp63-specific exon ( Ensembl accession number ENSDARG00000044356 ) and validation via RT-PCR analyses and cDNA sequencing , two N-terminal TAp63 isoforms , TA1 and TA4 , were identified ( Figures 6A and S1 ) that are similar to the corresponding isoforms in mammals [25] ( 39% aa identity; 55% aa similarity; Figure S2A , B; GenBank accession numbers KF682365 , KF682366 ) . TA1 corresponds to mammalian full-length TAp63 , TA4 to Δ40TAp63 , which was the initially described human isoform [18] . Furthermore , the exon-intron organization is conserved between mammals and fish ( Figure S1 ) , and sequences from shark to human segregate in the expected phylogenetic pattern ( Figure S2C ) . Both predicted TAp63 isoforms ( TA1 , TA4 ) were expressed in adult zebrafish skin ( Figure 6B ) . Comparative regular ( Figure 6C ) and quantitative real-time RT-PCR ( Figure 6D ) analyses further revealed almost exclusive presence of ΔNp63 transcripts in early zebrafish embryos , similar to the situation in mouse [60] . At the onset of metamorphosis ( 20 dpf ) , TAp63 levels were approximately 5fold increased , but still constituted only 1 . 3% of the total p63 levels ( Figure 6D ) . Also in adult zebrafish skin , TAp63 was expressed at much lower levels than ΔNp63 ( approx . 2% ) , whereas higher TAp63 transcript levels were found in the ovary , the site of essential TAp63 function in mouse [34] ( Figure 6D ) . In situ hybridizations with an isoform-specific RNA probe further revealed that TAp63 was expressed in regular epidermis as well as in all layers of the breeding tubercles ( Figure 6E , F ) , similar to the expression pattern of p53 ( Figure 6G , H ) . To investigate whether the zebrafish TAp63 transcripts are translated into biologically functional proteins , we carried out over-expression studies in zebrafish embryos . Injected synthetic mRNA encoding the TA1 or TA4 isoform of zebrafish TAp63γ caused widespread apoptosis during gastrulation stages ( Figure 6I , J ) , leading to embryonic death or severe malformations during further development ( Figure 6K ) , similar to the previously reported effects of injected zebrafish p53 mRNA [61] . In addition , zebrafish TAp63γ mRNA significantly rescued the ( headless ) phenotype caused by ΔNp63α over-expression ( Figure 6L–N ) , as previously reported for mouse TAp63 and p53 [58] . This indicates that zebrafish TAp63 transcripts give rise to a protein with p53-like pro-apoptotic and ΔNp63-antagonizing activities . Using target-selected mutagenesis [62] , a TA-specific zebrafish p63 mutant ( Tp63hu2525 ) mutant was isolated that bears a TCA->TAA nonsense mutation ( S48X ) in the last TA-specific exon truncating the N-terminal isoforms of TAp63 , while leaving ΔNp63 isoforms unaffected ( Figures 6A and S3 ) . RT-PCR followed by restriction fragment length polymorphism ( RFLP ) analysis of skin samples confirmed the presence of the premature stop codon in TAp63 transcripts of TAp63hu2525/hu2525 animals ( Figure 6O ) . Furthermore , qRT-PCR revealed that in TAp63hu2525/hu2525 mutant skin , TAp63 mRNA levels were more than 7 . 5 fold reduced compared to wild-type siblings , whereas ΔNp63 mRNA levels were slightly up-regulated ( Figure 6P ) , suggesting that the mutant TAp63 transcripts undergo nonsense-mediated mRNA decay and that TAp63 normally has a subtle negative effect on ΔNp63 transcription . Unfortunately , we could not investigate TAp63 proteins in TAp63hu2525/hu2525 mutants , as all tested p63 antibodies failed to detect p63 proteins after Western blotting of zebrafish extracts ( data not shown ) . However , upon injection of synthetic mRNAs encoding N- or C-terminally Myc-tagged zebrafish TAp63γ , wild-type mRNA gave rise to full-length fusion protein , whereas hu2525 mutant mRNA yielded no products ( Figure 6Q and data not shown ) , suggesting that the stop codon cannot be by-passed , that no internal start site is used , and that the truncated protein is unstable . Furthermore , in contrast to wild-type TAp63γ , hu2525 mutant mRNA lacked pro-apoptotic activity upon over-expression in early zebrafish embryos ( Figure 6K; compare columns 2 and 4 ) . Together , this suggests that the hu2525 mutant is a TAp63 null . Like p53zdf1/zdf1 mutants , TAp63hu2525/hu2525 mutants were viable and fertile . Anti-p63 immunofluorescence analysis revealed normal staining in regular epidermis of TAp63 mutants ( Figure 7A , B; Figure S4E–G ) , suggesting that ΔNp63 is the major p63 isoform of the adult zebrafish skin , consistent with our RT-PCR data . However , both TAp63 and p53 mutants displayed specific breeding tubercle deficiencies of variable strength ( C1 , C2 ) . SEM ( Figure 7C–E ) , calcein stainings ( Figure 7F ) and histological sections ( Figure 7G–I; Figure S4A–D ) revealed complete absence ( C2 ) in some individuals and reduced numbers and/or sizes of tubercles ( C1 ) in others , while frequencies of strongest phenotypes were significantly increased in TAp63/p53 double mutants ( Figure 7F ) . At the molecular level , even tubercle remnants of more weakly affected ( C1 ) TAp63 and/or p53 mutants displayed significant alterations compared to wild-type tubercles , such as reduced tgm1 ( Figure 7J–L ) and ectopic ( ΔN ) p63 expression ( Figure 7A , B ) in upper layers , as well as reduced proliferation rates at the base , which again was most prominent in TAp63/p53 double mutants ( Figure 7M–P ) . In sum , this makes the molecular signature of breeding tubercle remnants of weaker TAp63/p53 mutants more similar to that of the regular epidermis . Together , our data suggest that TAp63 and p53 are required for proper keratinocyte proliferation at the base of breeding tubercles , as well as for cornification-like differentiation processes in upper layers . In light of their known roles in keratinocyte differentiation in mouse [39] , we also explored the involvement of Notch signalling and caspase 3 during zebrafish tubercle development , as well as their epistatic relationships to TAp63/p53 . Differentiating tubercle keratinocytes displayed strong Notch signalling , as revealed by Tg ( TP1bglob:eGFP ) um13 , a transgenic in vivo reporter with 12 RBP-Jk binding sites [63] , which was strongly expressed in upper layers of breeding tubercles , but not in lower layers and regular epidermis , complementary to the distribution of ( ΔN ) p63 ( Figure 8A ) , but overlapping with aCasp3 ( Figure 8B ) . Double labelling with calcein blue as a marker of differentiated cap cells revealed that during development , Notch signalling in the tubercle domain is initiated several days before the first cap cells have differentiated ( Figure 8C , D ) . Furthermore , TAp63 and p53 mutants displayed strongly reduced Notch signalling and aCasp3 levels in their ( significantly smaller ) tubercles at 50 dpf ( Figure 8F–H , L–N ) , although wild-type tubercles of similar sizes at 30 dpf were strongly positive for both ( Figure 8E , K ) . To study whether Notch signalling and caspase 3 are required for breeding tubercle formation , we treated wild-type fish from 20 to 50 dpf with the specific Notch/g-secretase inhibitor DAPT or the caspase 3 peptide inhibitor zDEVD-fmk . While both treatments did not affect the general conditions and growth of the fish ( Figure S5 ) , DAPT-treatment caused significant reductions in the numbers and sizes of tubercles , in the activity of the Notch reporter and in aCasp3 levels ( Figure 8I , O , Q ) , as also seen in p53 and TAp63 mutants ( Figures 7F and 8G , H , M , N ) . zDEVD-fmk treatment had similar effects on tubercle numbers and sizes and on aCasp3 levels ( Figure 8P , Q ) , while leaving Notch signalling unaffected ( Figure 8J ) . Furthermore , despite the restriction of Notch signalling to upper breeding tubercle layers ( see above ) , DAPT treatment led to a significant reduction of keratinocyte proliferation at the base of the tubercles ( Figure 8R–T ) , comparable to the effects caused by loss of TAp63 and p53 function ( Figure 7P ) . Finally , we re-introduced Notch signalling into TAp63 mutants , using a binary transgenic approach for temporally controlled expression of the constitutively active intracellular domain of Notch1 ( NICD ) [64] . While heatshock-induced NICD expression from 20–50 dpf had little effect in wild-type fish , it significantly elevated tubercle numbers in TAp63 mutants back to wild-type conditions ( Figure 8U–X ) . Together , these data suggest that TAp63/p53 , Notch signalling and aCasp3 constitute a linear pathway required for proper breeding tubercle formation .
Thus far , it had been unclear whether the epidermis of adult zebrafish undergoes self-renewal as in mammals . Our transgenic lineage analyses indicate that both in regular epidermis and in breeding tubercles , derivatives of basal keratinocytes can be found in the outer cell layer ( Figure 1 ) . Furthermore , long-term observations of individual fish show that outer layers of breeding tubercles are shed off , and that it takes cells of the next layer several days to fully develop outer layer properties before they undergo desquamation themselves ( Figure 4 ) . Electron microscopy and marker analyses further revealed striking differences in the differentiation of keratinocytes in breeding tubercles versus regular epidermis . In regular epidermis , basal and intermediary cells display largely identical ultrastructural features [12] . In addition , they are ( ΔN ) p63-positive and mitotically active throughout all layers , and express the same keratin genes ( Figures 2 and 5 ) . Only when ending up in the superficial layer do they become strikingly different , express specific keratins and other structural proteins , including components of tight junctions , which are only present in this layer . Strikingly , basal keratinocyte-derived superficial cells of the regular epidermis are indistinguishable from persisting cells of the early embryonic enveloping layer , with which they form a uniform and continuous periderm-like sheet ( Figure 1E ) . In contrast , breeding tubercles exhibit a much more pronounced stratification ( see Figure 9A for schematic drawing ) . They consist of more epidermal cell layers and display a keratin gene expression pattern strikingly different from that of regular epidermis ( Figure 2 ) . Cell proliferation is confined to basal layers , while the expression of transglutaminase 1 , a crucial cross-linking enzyme during mammalian cornification [53] , which is absent in regular zebrafish epidermis , is confined to upper layers ( Figures 2 and 5 ) . Structurally , at least 4 different layer types can be distinguished . The basal layer , in which cells are organized in a much more regular and columnar fashion than in regular epidermis , several spinous layers , a second-tier layer , in which cells are sealed to each other via tight junctions , and a heavily keratinized outer cap layer ( Figures 3 and 9A ) . This organization is similar to that of the epidermis of adult mammals . In addition , the presence of extruding lamellar body-like vesicles and of lipid deposits at the second tier – cap layer interphase is reminiscent of lipid envelope formation during mammalian cornification [54] , while the progressive degradation of desmosomes between second tier and cap layer cells resembles the fate of corneodesomosomes in mammalian corneocytes [65] . However , there are also differences between zebrafish breeding tubercles and the cornifying mammalian epidermis . Thus , despite their high keratin content , tubercle keratinocytes lack obvious intermediary filament bundles and a cornified envelope ( CE ) , in line with the absence of genes encoding the keratin-bundling protein filaggrin and the CE components loricrin and involucrin in teleost genomes [66] . Furthermore , in contrast to mammalian corneocytes , sloughed cap cells of tubercles still contain their nuclei and display some ( loss of cell membrane integrity , nuclear pyknosis ) , but not all hallmarks of apoptosis [55] , [56] ( Figures 4 and 5 ) . Overall , this makes differentiated zebrafish tubercle keratinocytes more similar to the “immature horny cells with nuclei” observed in fetal human epidermis , which are supposed to represent a “transition phase of keratinisation” [2] . In line with such a more basal state , keratinocyte differentiation in zebrafish tubercles requires Notch signalling and caspase 3 , regulators that are also needed for early steps of epidermal cornification in mammals [37] , [39] . There is compelling evidence in mouse and zebrafish that ΔNp63 is required during early steps of epidermal development , promoting the proliferation and stemness of basal keratinocytes , while possibly blocking differentiation processes [26] , [58] , [59] . However , the in vivo role of its counterpart TAp63 during keratinocyte development and differentiation remained elusive . Also , to our knowledge , no defects during regular keratinocyte development have been reported for p53 mutants as yet [31] , [32] . Here , we revealed specific defects during keratinocyte development in breeding tubercles of zebrafish p53 and TA-specific TAp63 mutants . The used p53 allele , zdf1 ( also called e7 ) , bears a missense mutation in the DNA binding domain . Although the strongest of all available p53 mutants [57] , [67] , it might not be a complete functional null . However , inferences with other members of the p53/63/73 family seem very unlikely , as zdf1 homozygous embryos lack the phenotypes caused by loss of p63 or p73 [58] , [59] , [61] , [67] . Antimorphic effects are also unlikely for the used TAp63 allele , hu2525 , as the mRNA and the resulting truncated protein seem to be unstable ( Figure 6P , Q ) . Furthermore , the protein would only contain part of the transactivating domain , while lacking DNA binding and oligomerization domains ( Figure 6A ) . Therefore , we conclude that both TAp63 and p53 are per se essential for normal breeding tubercle development . Similar tubercle defects as in TAp63 and p53 mutants were obtained upon chemical inhibition of Notch signalling or caspase 3 activity ( Figure 8 ) . aCasp3 levels are strongly reduced in TAp63 and p53 mutants , as well as after loss of Notch signalling , whereas Notch signalling is only lost in TAp63 and p53 mutants , but not after inhibition of caspase 3 . In addition , re-introduction of Notch signalling into TAp63 mutants rescues their tubercle deficiencies . Together with their shared expression in differentiating keratinocytes of wild-type tubercles ( Figures 6E , G and 8B ) , this provides in vivo evidence for the presence and requirement of a linear TAp63/p53->Notch->caspase 3 pathway . Future studies have to elucidate the genetic control of breeding tubercle formation upstream of TAp63 and p53 . As previously suggested [21] , we believe that TAp63/p53 activity in zebrafish tubercles is indirectly promoted via negative interferences with its antagonist ΔNp63 , which in skin is present in vast excess over TAp63 ( Figure 6D ) , but , in contrast to TAp63 ( Figure 6E ) , most likely restricted to the base of breeding tubercles ( Figure 7A ) . This initial inhibition of ΔNp63 production in upper layers of the tubercle anlage might be reinforced by various negative feedbacks between TAp63/Notch/caspase and ΔNp63 . Thus , in mammalian epidermal keratinocytes , Notch1 has been shown to repress ΔNp63 expression [43] , while TAp63 induces caspase-dependent ΔNp63 degradation [68] , consistent with the observed increased ΔNp63 transcript levels in the skin ( Figure 6P ) and the presence of ectopic ΔNp63 protein in upper tubercle layers of TAp63 mutant zebrafish ( Figure 7A , B ) . It might appear paradoxical that in addition to reduced keratinocyte differentiation in upper layers of breeding tubercles ( Figure 7J–L ) , loss of TAp63 or p53 function also leads to reduced keratinocyte proliferation at the base of the tubercles ( Figure 7M–P ) , pointing to both differentiation- and proliferation-promoting effects of these regulators . Reduced tubercle growth and basal keratinocyte proliferation was also obtained upon inhibition of the TAp63/p53 mediators Notch or caspase 3 ( Figure 8E–T ) , although both are only active in post-mitotic keratinocytes in upper tubercle layers ( Figure 8A , B ) , pointing to non-cell autonomous mitogenic effects . Interestingly , in regenerating wings of the fruitfly Drosophila melanogaster , the enzymatic activity of the caspase Dronc generated in apoptotic cells does not only execute cell death in a cell-autonomous manner , but also promotes proliferation of adjacent cells even when Dronc-positive cells are prevented from dying , pointing to the presence of a Dronc-dependent mitogenic signal that acts in an apoptosis-independent and non-cell autonomous manner [48] , [69] . It is tempting to speculate that a similar mechanism might be at play in breeding tubercles , in which a TAp63/p53->Notch->caspase 3 pathway active in upper epidermal layers , while cell-autonomously promoting terminal keratinocyte differentiation , enhances proliferation of cells in lower layers in a paracrine fashion ( via an unknown secreted factor X; Figure 9B ) , accounting for proper tubercle growth during development and for proper balancing between cell loss via desquamation and cell renewal during tissue homeostasis [70] . Such a proliferation-stimulating role seems in contrast to the known functions of Notch and p53 as tumour suppressors [71] , [72] . However , it is in line with the initial identification of p53 as an oncogene [73] and its more recently described function in the context of metabolic control [74] . The breeding tubercle phenotype of zebrafish TAp63 and p53 mutants is not fully penetrant and variable in strength . Interestingly , however , phenotypic penetrance and average strength are significantly higher in TAp63/p53 double mutants , suggesting that the two structurally related transcription factors play partially redundant roles . Although other reasons cannot be ruled out , this suggests that a similar , possibly even more pronounced functional redundancy might also account for the apparent absence of epidermal defects in TA-specific p63 mutant mice [34]–[36] . Of note , we even identified a few TAp63/p53 double mutant zebrafish in which breeding tubercles were not completely lost . This could be due to some remaining p53 activity ( see above ) . In addition , it might point to the existence of further partially redundant factors . TAp73 , the third member of the family , is a candidate , which in mouse is expressed in all examined tissues and required side by side with TAp63 in oocytes to prevent genomic instability and female infertility [24] , [34] , [75] . Particular ΔNp63 isoforms might also be involved . Although in most cases they have dominant negative effects on TAp63 isoforms and p53 , acting as transcriptional repressors , cases have been reported where they transactivate target genes [27] and positively cooperate with p53 [29] . Together , this demonstrates the complexity of the p53/p63/p73 system of transcriptional regulation , while revealing that it can be helpful to perform genetic analyses in different in vivo model systems , taking advantage of variations that have occurred during vertebrate evolution .
Unless stated otherwise , wild-type fish from a mixture of TL and EK were used . The mutant Tp63hu2525 ( S48X ) line was generated upon our request in the Hubrecht Institute , NL , using target-selected mutagenesis ( TILLING ) [62] . The stable transgenic lines Tg ( krt4:creERt2 ) fr33 , Tg ( krt19:dTomato ) fr34 and Tg ( krt19:creERt2 ) fr35 were generated using the Tol2 kit [76] , [77] with the described krt4 [49] or krttc19e [50] ( here abbreviated as krt19 ) promoter fragments for construct generation , followed by standard injection and screening procedures . The mutant line Tp53zdf1 ( M214K ) [57] and the transgenic lines Tg ( krt4:GFP ) gz7 [49] , Tg ( actb2:loxP-STOP-loxP-dsREDEx ) sd5 [78] , Tg ( TP1bglob:eGFP ) um13 [63] , Tg ( 5xUAS-E1b:6xMYC-notch1a ) kca3 [64] and Tg ( -1 . 5hsp70l:Gal4 ) kca4 [63] have been previously described . For NICD expression , Tg ( 5xUAS-E1b:6xMYC-notch1a ) , Tg ( -1 . 5hsp70l:Gal4 ) kca4 double transgenic fish were heat-shocked from 20–50 dpf once a day for 1 hour at 40°C . The TAp63hu2525 allele was genotyped using the dCAPS ( derived Cleaved Amplified Polymorphism Sequence ) method [79] with PCR primers CTGACCCCGAGGTTGTCTAA ( sense ) and TGCTAATCTGTATAGTATTGGAAGCT ( antisense ) and subsequent HindIII digest . The Tp53zdf1 allele was identified via an RFLP ( Restriction fragment length polymorphism ) genotyping assay with PCR primers CCAGAGTATGTTCTGTCCA ( sense ) and TGATTGTGAGGATGGGCCTGCGGAATC ( antisense ) and subsequent BstyI restriction digest . Fish carrying the Tg ( -1 . 5hsp70l:Gal4 ) kca4 or Tg ( 5xUAS-E1b:6xMYC-notch1a ) kca3 transgene were identified by PCR transgene amplification with the primers CGGGCATTTTACTTTTATGTTGC ( gal4 , sense ) , CATCATTAGCGTCGGTGAG ( gal4 antisense ) , CATCGCGTCTCAGCCTCAC ( NICD sense ) , CGGAATCGTTTATTGGTGTCG primer ( NICD antisense ) , yielding a 1 . 2 or 0 . 3 kb amplification product , respectively . All zebrafish experiments were approved by the national animal care committees ( LANUV Nordrhein-Westfalen; 8 . 87-50 . 10 . 31 . 08 . 129; 84-02 . 04 . 2012 . A251; City of Cologne; 576 . 1 . 36 . 6 . 3 . 01 . 10 Be ) and the University of Cologne Tg ( krt19:creERT2 ) fr35 , Tg ( actb2:loxP-STOP-loxP-dsREDEx ) sd5 double transgenic , Tg ( krt19:creERT2 ) fr35 , Tg ( actb2:loxP-STOP-loxP-dsREDEx ) sd5 , Tg ( krt4:GFP ) gz7 triple transgenic , or Tg ( krt4:creERT2 ) fr33 , Tg ( actb2:loxP-STOP-loxP-dsREDEx ) sd5; Tg ( krt4:GFP ) gz7 triple transgenic embryos were treated with 5 µM 4-Hydroxytamoxifen ( Sigma Aldrich; H7904 ) in the dark at 28°C from 24 hpf to 96 hpf before being returned to normal system conditions for growing up . For Figure 1A–D , larvae or adult fish were fixed with 4% paraformaledehyde ( PFA ) /PBS overnight at 4°C , followed by cryosectioning , mounting of sections in Mowiol ( Carl Roth ) containing DAPI and fluorophore analysis with a Zeiss Apotome . For Figure 1E fish were stained with rabbit anti-RFP ( 1∶100; MBL , PM005 ) ( secondary = Alexa Fluor-488 anti-rabbit ( 1∶100; Invitrogen , A11008 ) ) , mouse anti-p63 ( 1∶100 , Santa Cruz , sc-8431 ) ( secondary = Alexa Fluor-647 anti-mouse ( 1∶100; Invitrogen , A21240 ) ) and Rhodamine-Phalloidin ( 1∶100 , Invitrogen , R415 ) , and analyzed via confocal microcopy ( Zeiss LSM710 Meta ) . Epidermal cell proliferation was assessed by BrdU incorporation after incubating adult fish in 100 µg/ml BrdU ( Sigma ) in fish system water for 12 or 24 hours , followed by anti-BrdU immunolabelling . TUNEL assay was performed using the in situ Cell Death Detection Kit , POD ( Roche ) according to the manufacturers recommendations . For in vivo calcein staining , fish were incubated for two hours in calcein green or calcein blue solution ( 100 mg/l; Sigma Aldrich ) . After extensive washings , fish were anaesthetized with Tricaine ( ethyl-3-aminobenzoate methanesulfonate , Fluka ) for fluorescence analysis of live whole mounts or after PFA fixation and cryosectioning . For histological , immunofluorescence and in situ hybridization analyses , adult zebrafish were sacrificed by Tricaine overdose and fixed in 4% PFA overnight at 4°C . Samples for paraffin embedding were decalcified in 0 . 5 M EDTA ( pH 7 . 4 ) at room temperature for 5 days , dehydrated in a graded series of alcohols , cleared in Roti-Histol ( Carl Roth ) and embedded in paraffin wax . 10 µm sections were cut using a Leica RM2255 microtome . Samples for cryosections were orientated in 15% sucrose with 1% agarose in PBS and mounted in tissue freezing medium ( Leica ) . 10 or 12 µm sections were obtained using a Leica CM1850 cryostat . Paraffin sections were stained with hematoxylin & eosin or acidic fuchsin orange G ( AFOG ) trichrome ( Gennova ) according to standard protocols . For immunofluorescence analysis of paraffin- or cryosections , antigen retrieval was performed with 10 mM sodium citrate ( pH 6 . 0 ) at 70°C for two hours , followed by washes and primary and secondary antibody incubations in PBS supplemented with 10% fetal calf serum ( FCS ) , and mounting of sections in Mowiol containing DAPI . Primary antibodies other from the ones described above were: rabbit anti-activated caspase3 ( 1∶1000 , abcam ab-13847 ) , mouse anti-BrdU ( 1∶200 , Roche 1170376 ) , mouse anti-pan Keratin Type II ( 1∶200 , Progen 61006 ) . Secondary antibodies used were: anti-mouse Cy3 ( 1∶1000 , Invitrogen ) , anti-rabbit Cy3 ( 1∶1000 , Invitrogen ) , Alexa Fluor-488 anti-rabbit ( 1∶1000 , Invitrogen ) In situ hybridization on paraffin sections was performed according to [80] . Antisense RNA probes were generated via in vitro transcription with Dig RNA labelling mix ( Roche ) and the following templates and conditions: krt8 ( GenBank BI875660 ) : 1 . 8 kb cDNA fragment cloned from EST into pBluescript SK , linearization with HindIII , transcription with T3 RNA polymerase; cki ( GenBank AF197880 ) : 0 . 6 kb fragment in pSPORT , EcoRI , SP6 RNA pol; krt5 ( GenBank AF197909 ) : 0 . 4 kb fragment in pBluescript SK , KpnI , T3 RNA pol; krt17 ( ZFIN-ID zgc:92061; GenBank BI850052 ) : 1 . 5 kb fragment in pSPORT , EcoRI , SP6 RNA pol . For krtt1c11a and tgm1 , 1 . 0 kb fragments were amplified via RT-PCR and cloned into pGMTeasy ( Promega ) ( krtt1c11a: SpeI , T7 RNA pol ) or pCRII ( tgm1: XhoI , SP6 RNA pol ) . For a TA-specific TAp63 probe , a 408 bp TA1 cDNA fragment was amplified via RT-PCR with the primers 5′-CAGGGGCTAGCTTCTAGTGG-3′ ( sense ) and 5′-TGTAAGGGGCTCCTCAGGCTC-3′ ( antisense ) and cloned into pGEMTeasy . The plasmid was digested with SpeI and transcribed with SP6 RNA pol for antisense , and with NcoI and T7 RNA pol for sense probe . For p53 , EST clone MPMGp609B127Q8 with the full-length p53 cDNA in pSPORTI was linearized with EcoRI and transcribed with SP6 RNA pol for antisense , and with BamHI and T7 RNA pol for sense probe . Images were captured on a Zeiss Axiophot , Zeiss Apotome , Zeiss Confocal ( LSM710 META ) or Leica M165 FC stereo microscope . Transmission electron microscopy ( TEM ) of adult zebrafish was carried out as described [81] . For Scanning electron microscopy ( SEM ) , adult fish were sacrificed and fixed overnight in 4% PFA at 4°C , dehydrated and either cryo-fixed , sputter-coated ( gold/palladium ) and transferred onto the SEM cryo-stage while still frozen , or critical point dried ( CPD ) , sputter-coated and evaluated at room temperature . Fish were raised from 20–50 dpf in E3-medium containing 100 µg/ml of the γ-secretase inhibitor DAPT ( N-N- ( 3 , 5-difluorophenacetyl ) -L-alanyl ) -S-phenylglycien t-butylester; Sigma-Aldrich 208255 ) [82] , 5 µg/ml of the caspase 3 peptide inhibitor z-DEVD-fmk ( Calbiochem 264155-80 ) [83] or 0 . 2% DMSO as control . Standard length ( SL ) of the fish was used to control equal development of each group . RNA of whole zebrafish embryos at different developmental time points or from isolated tissues or organs of adults was isolated using the trizol reagent ( Invitrogen ) . cDNA was generated using random hexamer primers . Regular PCR was carried out with the TA-specific sense primers TAS1-3 or the ΔN-specific sense primer ΔNs ( see Figure S1 ) , combined with a shared reverse primer 5-GTGACTGGGTGGGGCTATTT-3 . Zebrafish actb2 ( GenBank: BC0675676 ) specific primers were used as control ( sense , 5′-AGTTTGAGTCGGCGTGAAGT-3′; antisense , 5′-AGGCTGTGCTGTCCCTGTAT-3′ ) . PCR reactions were performed with an annealing temperature of 55°C for 35 cycles . For cDNA RFLP analysis , the 629 bp fragment shown in Figure 6B was amplified with primers TA3 and the reverse primer 5′-GTGACTGGGTGGGGCTATTT-3′ , followed by overnight digest with MboI ( NEB ) and electrophoresis in 4% agarose gel , revealing 379 , 107 , 101 and 42 bp cleavage products in wild-type , but only 279 , 208 and 42 bp products in hu2525 mutant cDNA . Quantitative RT-PCR was performed in triplicates ( 2 experiments each ) with TaqMan primers ( see below ) and an Applied Biosystems 7500 Fast Real-Time PCR System under default PCR conditions , resulting in specific 65 bp ( TAp63; shared by TA1 and TA4 ) and 72 bp ( ΔNp63 ) products . Used primers were: TA-forward , 5′-GCCTGAGGAGCCCCTTACA-3′; ΔN-forward , 5′-CCAATGCTCCCTCATCCTACA-3′ , TA-reverse and ΔN-reverse , 5′-CATTTTGATCCATGCTGTTGAGA-3′; TA-TaqMan probe , 5′-CTCAGTATACAAGCCTGGG-3′; ΔN-TaqMan probe , 5′-AGCCTCAGTATACAAGCC-3′; standard , rps23 ( ribosomal protein S23; standard; Applied Biosystems; Dr . 0343030371m1 ) . Amplification efficiencies were determined with a dilution series of cDNA from adult skin , and were above 95% for all three amplificants ( TAp63 , 99 , 4%; ΔNp63 , 95 . 9%; rps23 , 95 . 2% ) . Data were analyzed using Biosystems Prism SDS and Excel software , applying ΔCT and ΔΔCT calculations . To generate the TAp63 expression constructs pCS2-TA ( 1 ) p63γ and pCS2-TA ( 4 ) p63γ , a replacement strategy was used , amplifying the N-terminal fragments of the TA1 and TA4 isoforms of TAp63 via RT-PCR from adult skin of wild-type and hu2525 mutants with forward primers 5′-TTGGATCCACCATGACCTCTCCTTATGCAGC-3′ ( TA1 ) or 5′-TTGGATCCACCATGTCACAGGGCCAGGGCTC-3′ ( TA4 ) , and reverse primer 5′-GTGACTGGGTGGGGCTATTT-3′ , followed by BamH1/BspM1 digest and cloning into BamH1/BspM1-digested pCS2-ΔNp63γ [58] . To generate expression constructs for TAp63 with six N-terminal Myc tags , TAp63γ coding sequences were amplified from wild-type and hu2525 mutant pCS2-TA ( 4 ) p63γ plasmids with primers 5′-CGAATTCAACCATGTCACAGGGCCAGGGCTC-3′ ( sense ) and 5′- TTTCTAGATCACACTGATTGAGAACTCTTTTT G-3′ ( antisense ) , digested with EcoRI and XbaI , and cloned into EcoRI/XbaI digested pCS2-MT ( www . addgene . org/vector-database/2296/ ) . For expression constructs with six C-terminal Myc tags , amplification was performed with primers 5′-TTGGATCCACCATGTCACAGGGCCAGGGCTC-3′ ( sense ) and 5′-CGATCGATTCACTGATTGAGAACTCTTTTTGTC-3′ , followed by digestion with BamHI and ClaI , and cloning into BamHI/ClaI , digested pCS2-MT . Capped RNA was prepared after restriction digest of these expression constructs or pCS2-ΔNp63α1 [58] with KpnI , using the Message Machine kit ( Ambion , Austin , TX ) . RNA was dissolved in water , and 1 nl per embryo injected . TAp63γ mRNAs were injected at a concentration of 10 ng/µl , 6xMyc-TAp63γ mRNAs at a concentration of 5 ng/µl , and ΔNp63α1 mRNA at a concentration of 25 ng/µl . Apoptosis and resulting embryonic death or embryonic malformations were scored at 8 hpf and 24 hpf , respectively , ΔNp63α1-induced loss of eyes at 32 hpf , as described [58] . Zebrafish embryos were dechorionated and deyolked , and cells were collected as described [84] . Cell pellets or adult tissues were either directly dissolved in SDS loading buffer as described [84] , or first lysed in chilled CSH buffer ( 50 mM Tris-HCl ( pH 7 . 5 ) , 250 mM NaCl , 1 mM EDTA , 1% Triton-X100 , supplemented with cOmplete Protease Inhibitor Cocktail , Roche ) , followed by protein concentration determination . 10–12% SDS-PAGE , blotting on nitrocellulose membrane , Ponceau staining and immunodetection were carried out as described [84] . Used primary antibodies were: anti-Myc , 9B11 ( mouse , Cell Signaling Technology; 1∶2000 ) ; anti-p63 , 4A4 ( mouse , Santa Cruz Technologies , against aa 1–205 of human ΔNp63 ) , D-9 ( mouse , Santa Cruz Biotechnology , against aa 15–151 of human ΔNp63 ) , H-137 ( rabbit , Santa Cruz Biotechnology , against aa 15–151 of human ΔNp63 ) , H-129 ( rabbit , Santa Cruz Biotechnology , against aa 513–641 at C-terminus of human TAp63α ) . | The mammalian epidermis is a stratified self-renewing epithelium , in which cell loss at the surface is properly balanced by cell proliferation in basal layers to ensure tissue homeostasis . But how is this balance genetically controlled ? Here , we address this question in zebrafish breeding tubercles , epidermal appendages in which keratinocytes undergo more advanced differentiation processes than in regular fish epidermis , sharing crucial features with the cornified mammalian skin . We identify a linear pathway consisting of the transcription factor p53 and its close relative TAp63 , which activate Notch signalling and thereby caspase 3 to promote terminal differentiation and eventual shedding of keratinocytes in upper tubercle layers , while at the same time employing non-cell autonomous mechanisms to promote keratinocyte proliferation at the tubercle base , thereby ensuring proper development and homeostasis of this self-renewing tissue . Such a two-fold function of the pathway is consistent with the formerly reported dual role of a caspase during wing regeneration in the fruitfly . Our findings will help to better understand the seemingly contrary effects described for TAp63 in different mammalian systems , while demonstrating partial functional redundancy between p53 and TAp63 during epidermal development in fish . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2014 | p53 and TAp63 Promote Keratinocyte Proliferation and Differentiation in Breeding Tubercles of the Zebrafish |
Polycomb bodies are foci of Polycomb proteins in which different Polycomb target genes are thought to co-localize in the nucleus , looping out from their chromosomal context . We have shown previously that insulators , not Polycomb response elements ( PREs ) , mediate associations among Polycomb Group ( PcG ) targets to form Polycomb bodies . Here we use live imaging and 3C interactions to show that transgenes containing PREs and endogenous PcG-regulated genes are targeted by insulator proteins to different nuclear structures depending on their state of activity . When two genes are repressed , they co-localize in Polycomb bodies . When both are active , they are targeted to transcription factories in a fashion dependent on Trithorax and enhancer specificity as well as the insulator protein CTCF . In the absence of CTCF , assembly of Polycomb bodies is essentially reduced to those representing genomic clusters of Polycomb target genes . The critical role of Trithorax suggests that stable association with a specialized transcription factory underlies the cellular memory of the active state .
The default state for the organization of genomic material is the chromosomal territory occupied by the folding of the continuous chromatin fiber constituting a chromosome . From this territory , individual regions may loop out to partake in molecular activities such as transcription , heterochromatic silencing , Polycomb repression , etc . A question debated in the past few years is whether these different chromatin states are physically partitioned in the nuclear volume by targeting transcriptional activity to a transcription factory [1] , [2] , Polycomb repression to a Polycomb body [3] , [4] , etc . Is a chromatin domain that binds Polycomb group ( PcG ) proteins ( or becomes transcriptionally activated ) directed to a nuclear volume containing other PcG-repressed chromatin regions ( or transcriptionally active genes ) ? Here we present evidence indicating that insulator elements play a crucial role in the formation of transcription factories as well as of Polycomb bodies . Insulator-binding proteins organize the genomic material by forming networks of chromatin loops that govern both local higher-order folding and distant interactions between remote genomic sites [5] , [6] . Insulator proteins , including CTCF in mammals , dCTCF , CP190 , Su ( Hw ) and BEAF in Drosophila , have been mapped at thousands of sites throughout the genome that differ little from one cell type to another [7]–[9] . Although it is far from clear what fraction of these have enhancer-blocking function , most gene neighborhoods have at least one insulator , which could , in principle , govern the interaction of those genes with other genomic sites . Polycomb bodies have been observed in mammalian and Drosophila nuclei by immuno-staining with antibodies against PcG proteins . Interactions between PcG target genes have been detected by 4C or Hi-C approaches [10] , [11] . Genes residing in two different Hox gene clusters in Drosophila co-localize at significant frequencies within the same Polycomb body , resulting in enhanced silencing of both genes [12] . Fab-7 and Mcp are so-called boundary elements that separate cis-regulatory regions of the Bithorax Complex in Drosophila . Each has been shown to contain two separable functional parts: a core Polycomb Response Element ( PRE ) and an insulator [13]–[16] . Transgenes containing the full Fab-7 boundary region can interact with the endogenous Fab-7 and co-localize at relatively high frequencies inside Polycomb bodies when both are repressed by PcG proteins [3] , [4] . Using constructs containing the bxd PRE , Fab-7 and Mcp elements , we have shown [17] that transgenes containing a PRE alone have no intrinsic ability to co-localize and that the insulator element , not the PRE , is necessary and sufficient to mediate long-distance interactions between Fab-7 or Mcp transgenes . A comparison of our results with previous work [18] , suggested that the addition of an enhancer might strongly increase co-localization . We show here that transcriptional competence is a major factor targeting co-localization: insulators target repressed genes to Polycomb bodies but also direct derepressed Polycomb target genes to foci of transcriptional activity . These associations are different from the local ( 1–3 Mb ) interactions abundantly detected by genome-wide 3C-related approaches and can occur between different chromosomes . Although the interactions require “insulators” they are not constitutive: while insulator protein binding changes little , interactions occur only between genes in similar chromatin states , either both repressed or both active . We find that Trithorax is required for a stronger and stabler association of the derepressed gene to specific transcription factories , raising the possibility that the Trithorax-mediated epigenetic memory may owe more to nuclear localization than to histone modifications .
Using tagged transgenes , we have previously shown that two copies of a Mcp transgene inserted at remote sites can physically co-localize in the nucleus . Constructs containing a minimal Mcp element associated in ∼7% of the nuclei in both eye and wing imaginal disc cells , dropping to less than 0 . 5% when the insulator part of Mcp was deleted [17] . To test the effect of transcriptional activation , we used the eye-specific enhancer of the white gene , active in the photoreceptor and pigment cells of the eye imaginal disc but not in the wing disc . The Mcp-Eye-B and Mcp-Eye-A constructs contain the 820-bp Mcp and the eye enhancer , flanked by FRT and Lox respectively , but with different position and orientation relative to the white reporter gene ( Figure 1A ) . It is important to bear in mind here that , in the Mcp element , the PRE and the insulator have relatively weak effects as silencer or enhancer blocker , respectively [15] . The transgenes include 128 tandem LacO repeats , which are visualized in live cells expressing EGFP-LacI driven by the Ubiquitin promoter [17] . Previous experiments have shown that the vector itself , the FRT and Lox sites cause no interactions or specific localization effects [17] , [18] . Three independent lines were used for Mcp-Eye-B and two for Mcp-Eye-A ( Figure 1B and Figure S1 ) . We combined two transgene insertions into a fly line that also expressed EGFP-LacI ( Figure 1C ) , and visualized the interaction between the two transgenes by live imaging of eye and wing imaginal discs . In the reconstituted 3D images ( Figure 1D and Video S1 ) , nuclei with one dot were taken to indicate co-localization and two dots as no interaction ( see Methods section and [17] , [18] ) . In all combinations , we found that the 820-bp Mcp mediated high frequency interactions ( 50–90% ) in the eye imaginal disc cells , but low co-localization ( 5–10% ) in wing disc or in the cells of the membrane surrounding the eye disc ( Figure 2A ) . The combination of two A transgenes , or two B transgenes , or an A and a B transgene , all give similar co-localization frequencies , irrespective of the different relative position and orientation of the Mcp and enhancer . This and the eye colors of the flies ( Figure S1 ) are consistent with the idea that the enhancer-blocking activity is weak . The orientation does affect pairing-dependent silencing: all the Mcp-Eye-B lines show pairing-dependence while the Mcp-Eye-A lines ( in which the Mcp insulator is interposed between the PRE and the promoter ) are not pairing-sensitive ( Figure S1 ) . These results suggest that , surprisingly , transcriptional activation greatly increases the association of two Mcp transgenes in the nucleus . To demonstrate this , we deleted the eye enhancer ( ΔE ) , using the Cre recombinase . This dramatically decreases the frequency of co-localization in the eye disc cells in all five combinations ( from ∼70% down to ∼20%; χ2 test , p<0 . 0001 ) ( Figure 2A ) . Similar decreases result when the enhancer is deleted from only one of the two transgenes ( Figure S1 ) . Without enhancer , co-localization remained consistently higher in eye disc cells ( ∼20% ) than in wing and membrane cells ( 4–10% ) , probably because the reporter white gene has residual eye-specific activity , as evidenced by the eye colors . We conclude that enhancer activity helps to bring two distant transgenes together in the eye cells , presumably in a different nuclear environment from that of the repressed genes . Rather than in a Polycomb body , we suppose that the site of activity will be a transcription factory . In the absence of the enhancer , some association will persist but at different nuclear locations either when both genes are active from residual eye-specific activity ( transcription factory ) or when both are Polycomb-repressed ( Polycomb body ) . Next , we deleted the Mcp part of the transgene ( ΔM ) to see if it is necessary to mediate the long-range interaction . The results ( Figure 2A ) show that co-localization is almost totally abolished both in eye and wing imaginal disc cells ( from 70% to <2%; χ2 test p<0 . 0001 ) . Double deletion of both Mcp and Eye enhancer gives similar results ( Figure 2A , ΔMΔE ) except in the case of the Mcp-Eye-B4-B15 pair , probably because the two transgenes are fairly close to each other ( ∼5 Mb ) on the same chromosome arm . Therefore enhancer-dependent transcriptional activity is not sufficient to promote long-range interactions in the absence of the Mcp insulator+PRE . We also carried out a 3C assay with one of the combinations ( Mcp-Eye-B4–Mcp-Eye-B19 ) to show that the B4 and B19 transgenes , inserted on two different arms of chromosome 2 , interact physically with one another in the eye disc but not in the wing disc . This interaction is still strong after enhancer deletion , but disappears after Mcp is deleted from one of the two transgenes ( Figure 2B ) . Excision of the Mcp fragment removes both insulator and PRE functions . The Mcp insulator binds the insulator proteins CTCF and CP190 [8] , [17] , [19] , [20] and is required for co-localization [17] . To determine if the insulator is still specifically required for the high level co-localization , we made the flies homozygous for the loss of function mutation CTCFy+2 . The loss of CTCF reduces the interaction of two remote transgenes to a level similar to that seen when Mcp is deleted ( Figure 2C ) . CP190 mutations have a weaker effect in the eye disc ( from 70% to ∼14%; p<0 . 0001 ) , but do not alter interaction frequencies in the wing disc or in the eye disc membrane cells , where the eye enhancer is not active . We conclude that insulator function is still essential for long-range interaction and , in its absence , the enhancer alone cannot promote interaction . The CTCF protein is required for this insulator function , while CP190 contributes to the high frequency interaction but not to enhancer-independent interaction . RNAi components interact with insulator proteins [21] , [22] and have been implicated in the Fab-7-mediated long-distance interactions [4] . The interactions of our Mcp transgenes are also affected by mutations in the RNAi genes piwi , aub and particularly AGO2 ( Figure 2C and Figure S2 ) . Like CP190 mutations , RNAi mutations reduce the high level co-localization in the eye disc without affecting that in wing cells . As previously reported [22] , the role of AGO2 does not require its catalytic activity since the AGO2-V966M mutation , which abolishes catalytic function , has no effect ( Figure S2 ) . In human cells , cohesin proteins co-localize extensively with the insulator protein CTCF and , together with CTCF , mediate long-range interactions [23] , [24] . In Drosophila , ChIP data do not show such a relationship and the genetic evidence does not support it . We found that loss of function of Smc1 or Rad21 does not affect the co-localization frequency in eye or wing disc cells ( Figure S2 ) although it does abolish pairing-dependent silencing effects ( results not shown ) . Since PREs are generally repressive , we reasoned that the high interaction we observed might only need the insulator part of Mcp plus the enhancer activity . To test this , we constructed McpΔPRE-Eye , similar to Mcp-Eye-A but containing only the insulator part of Mcp instead of the 820-bp Mcp ( insulator+PRE ) fragment ( Figure 3A ) . Unexpectedly , several combinations of McpΔPRE-Eye insertions show only the basal 5∼7% insulator-dependent co-localization frequency both in eye and wing disc cells ( Figure 3A ) , similar to that obtained with the insulator alone , with no enhancer or PRE [17] . These results indicate that enhancer-promoted transcriptional activity is not sufficient and that the PRE is in fact important for the enhancer to mediate high frequency long-range interactions . To understand the role played by the PRE , we returned to the 820-bp Mcp-Eye lines , and tested them in a Polycomb mutant background . Since homozygous Pc− flies die at the embryonic stage , we tested heterozygous Pc− larvae and found that halving Pc dosage , which often has a detectable effect on the expression of a PcG-repressed transgene , has no effect on the long-distance interaction ( Figure 3B ) . High-level interaction is therefore not sensitive to PC levels , as expected since PC generally does not bind when the gene is in the active state ( Figure S3 ) [25] . PcG target genes are positively regulated by Trithorax ( TRX ) , a histone methyltranferase homologous to mammalian MLL1 , known to methylate H3K4 and to antagonize PcG repression [26]–[30] . TRX binds constitutively to all known or putative PREs ( therefore also called TREs ) regardless of whether they also bind PcG proteins or whether the target genes are transcriptionally repressed [25] . To test whether TRX is required for high level co-localization , we crossed our Mcp-Eye lines into a trx mutant background . Homozygous trx loss of function mutations are embryonic lethal but , even in heterozygous trx larvae , we found that the high co-localization in the eye disc cells is reduced to the basal level , the same level found in eye membrane and wing disc cells ( Figure 3B ) . Together , these results demonstrate that the high frequency co-localization of the active transgenes is highly dependent on TRX concentration but not on PC concentration and therefore requires TRX/TRE but not PC/PRE function . To test if a different enhancer can also promote the long-range interaction mediated by the insulator , we constructed two new transgenes , Mcp-Ubx-B and Mcp-Ubx-A ( Figure 4A ) , in which the eye enhancer in Mcp-Eye is replaced by the Ubx H1 enhancer , which drives strong and uniform expression in eye , wing and haltere imaginal discs [31] . We recovered two independent Mcp-Ubx insertions , both on the right arm of chromosome 3 ( Figure 4B ) . After combining the two insertions into one line , robust high-level co-localization ( ∼55% ) was observed in all the cells of both eye and wing discs , showing that wing cells are not intrinsically less suited for co-localization than eye cells ( Figure 4C , t test , p = 0 . 535 ) . Co-localization decreased to a moderate level ( 23% ) after deletion of the Ubx H1 enhancer , and down to background level ( ∼9%: the two transgenes are only 4 . 1 Mb apart ) after deletion of one or both Mcps ( χ2 test , p<0 . 0001 ) . The 3C assay was also used to confirm that the interaction between the two transgenes decreases after enhancer deletion and is not detected after Mcp deletion from one or both transgenes ( Figure 4D ) . We conclude that different enhancers can promote co-localization mediated by Mcp . Similar but weaker results were obtained with Mcp-UAS , in which the eye enhancer of Mcp-Eye-A was replaced by five copies of the GAL4 binding site ( 5×UAS ) and activated by the arm-GAL4 driver ( Figure S4 ) . To look at interactions between transgenes containing different enhancers we used combinations of Mcp-Eye ( eye enhancer , active in the eye ) and Mcp-Ubx ( Ubx H1 enhancer , active in eye and wing ) , both inserted in chromosome 3 . As shown in Figure 4E the two transgenes do not co-localize in wing cells where one is active and the other silent , and have the normal level ( ∼10% ) but not the high level of co-localization in the eye , where both are active . We suppose that when the Mcp-Eye and Mcp-Ubx transgenes are in different transcription states , they are directed into different nuclear domains . When both transgenes are active , the fact that they interact only at the basal level suggests that , while they may chance to land in the same active compartment , they lack high frequency co-localization , which probably requires sharing transcriptional activators . So far , our results have shown that two Mcp-containing transgenes co-localize with one another when they are both repressed and , much more frequently , when they are both activated by the same enhancer . In an earlier paper we showed that they can also interact with the endogenous Mcp element [17] . The endogenous Antp gene of ANT-C has been reported to co-localize in a Polycomb body with the Abd-B gene of the Bithorax Complex ( BX-C ) when both genes are PcG-silenced , but not when one is active and the other silenced [12] . Next we asked if similar rules govern interactions between a Mcp transgene and other endogenous Hox genes . Since the eye enhancer is active only in a subset of cells of the eye-antenna disc , we turned to the Mcp-Ubx constructs containing the Ubx imaginal enhancer , active in all wing and eye disc cells , as are also ANT-C genes , unless repressed . To determine associations with endogenous genes , we used 3C analysis of the Mcp-Ubx line containing an intact transgene Mcp-Ubx B4 ( Figure 4A ) . We tested the interaction of our transgene with Antp and Dfd , both Hox genes belonging to the ANT-C . Antp is active in the wing and silenced in the eye , while Dfd is silenced in the wing and active in the eye . To determine the interactions in the different combinations of states , we isolated eye imaginal discs separately from wing discs of the intact Mcp-Ubx line and performed the 3C assay on each . As shown in Figure 5 , the active transgene Mcp-Ubx-B4 interacts only with active homeotic genes , specifically , with Antp in the wing and with Dfd in the eye disc cells . The associations detected with the various Mcp transgenes are not a peculiarity of Mcp or of the transgene constructs . We tested endogenous PcG target genes Abd-B , pnt , lbe and C15 , all on chromosome 3R , for their ability to associate in the repressed or active state using the 3C approach . We used two different cultured cell lines , BG3 and Sg4 , in which some of the genes are in different states of activity , and we treated the cells with RNAi against CTCF , trx , ash1 or lacZ as a control ( see Figure S5 ) . The 3C products were analysed by qPCR standardized in each case by the products for the ligation between adjacent control fragments and are shown in Figure 6 in terms of fold enrichment relative to the result in BG3 cells ( LacZ control RNAi ) . The first panel displays the results for interactions between pnt and C15 . These genes are both active in BG3 cells and show significant interaction . The interaction decreases upon knockdown of CTCF , TRX or ASH1 . In Sg4 cells pnt is still active but C15 is off . The interaction in control Sg4 cells ( LacZ RNAi ) is greatly decreased relative to that in BG3 cells . It is equally low upon CTCF knockdown but increases when TRX or ASH1 are knocked down: in their absence the pnt gene is again repressed by Polycomb and is able to interact with the repressed C15 gene . Interactions between Abd-B and lbe follow a similar pattern: in BG3 cells where both are PcG-repressed they show CTCF-dependent interaction not affected by trx or ash1 knockdown . They do not interact in Sg4 cells where one is active and the other repressed but , in the absence of TRX or ASH1 , we presume that the active gene becomes at least partially repressed and interacts with the repressed gene . These interactions are all between different genes with different enhancers and are therefore analogous to those represented in Figure 4E and Figure 5 . Overall , the four panels show that interactions are high between genes when they are both PcG-repressed or both in the active state but decrease when one is active and one repressed ( Figure 6 ) . All interactions are affected by CTCF RNAi . Knockdown of trx or ash1 affects interactions between active genes but not those between repressed genes . These results imply that Polycomb bodies formed by the association of remote PcG targets fall apart in the absence of CTCF . Of course , a significant number of Polycomb bodies are structurally determined by the genomic clustering of many PcG target genes ( this argument is developed in ref . 32 ) . Egregious examples are the two Drosophila Hox clusters: the Antennapedia Complex ( ANT-C ) and the Bithorax Complex ( BX-C ) . In the absence of CTCF the ANT-C genes would continue to be clustered and so would the BX-C genes but the two clusters would no longer associate ( Figure S6 ) . To see if this effect could be visualized , we immunostained the cells treated with CTCF RNAi with anti-PSC or with anti-RNA pol II to illuminate respectively Polycomb bodies ( Figure 7A ) or transcription factories ( Figure S3 ) . Compared to the cells treated with the control lacZ RNAi , the CTCF RNAi cells appear to have fewer and brighter Polycomb bodies suggesting that numerous smaller bodies have been lost . A quantitative analysis was made to determine the number of foci above threshold and their mean and maximum intensities ( Figure 7A ) . The results confirm that , in the absence of CTCF , the number of visible foci decreases due to loss of the weaker foci . The high intensity foci are not affected . These are expected to be the structural clusters because they are associated 100% of the time while genes interacting through an insulator associate only part of the time . We therefore interpret these results as consistent with our expectation of the role of CTCF . The RNA pol II images are more difficult to evaluate but clearly many transcription factories have not dissociated and we cannot say whether CTCF plays a general role ( Figure S7 ) .
Some basic rules can be deduced from our results . A background level of interactions is proximity-dependent and insulator-independent . When two transgenes both contain an insulator , interaction frequency rises to a level of 5–20% . Interactions are more frequent between sites on the same chromosome arm , as has also been reported by others [10] , [32] but they can be observed also between sites on different chromosomes [3] , [32] , [34] . Interactions beyond the chromosome arm have also been detected by genome-wide 3C methods but appear to be underrepresented in these approaches , probably because these methods are ligation-dependent and detection of individual interactions is in competition with that of the much more abundant proximity-dependent associations . Whether a PRE makes any contribution to the basal level is unclear but we detected none when we tested specifically for it [17] . The addition of an enhancer to both transgenes produces a dramatic increase in the frequency of co-localization ( 50–90% ) that requires the presence of a PRE/TRE , as well as an insulator , and implies a remarkably stable association . The nature of the enhancer factors probably plays a role since co-localization between transgenes with different enhancers occurs in tissues where both are active but remains at the 5–20% level . In most cases , interactions between endogenous PcG target genes fall into this category: they interact when both are active or when both are PcG-repressed but not at the highest levels seen between genes activated by the same enhancer . The results of a large number of experiments with different transgenes and different fly lines [17] , [18] , [32] , [34] are consistent and are not attributable to the peculiarities of a few specific insertion sites although co-localization levels are probably influenced by the genomic environment . Our results show that these conclusions are also true for endogenous PcG target genes . Both CTCF and CP190 insulator proteins bind to the Mcp insulator . Loss of CTCF function has the same effect as deletion of the Mcp insulator , reducing co-localization to generally less than 1% , which we take to be the level due to chance encounters . It is highly unlikely that this effect is due to misregulation of some unknown gene that affects our transgenes because a ) it mimics the effect of deleting the insulator , b ) it affects both localization to Polycomb bodies and to transcription factories , c ) similar effects are observed for several endogenous genes , d ) genome-wide studies in cultured cells show that the loss of CTCF does not have major global effects on gene expression [35] . Surprisingly , loss of function CP190 mutations have a more nuanced effect suggesting that CP190 is only required for co-localization in the active state or that loss of CP190 does not abolish insulator action as completely as loss of CTCF . A major implication of these experiments is that co-localizations between different repressed PcG target genes [10] , [11] are mediated by insulator-binding proteins rather than by interactions between bound PcG complexes . Earlier results indicate that the gypsy insulator , which binds SU ( HW ) , CP190 and MOD ( MDG4 ) proteins , has an effect similar to that of the Mcp insulator , which binds CTCF and CP190 , on the interaction between remote transgenes [34] . CTCF function in mammalian genomes is tightly linked to cohesion [23] , [36] . In Drosophila , however , there is no apparent relationship between cohesin and CTCF in chromatin binding or in insulator function . Consistently , co-localization is not affected by mutations in the major cohesin components Smc1 or Rad21 although these mutations abolish the pairing-dependent silencing effect ( results not shown ) seen typically with transgenes containing Mcp and other PREs [32] , [37] . Lei and Corces [21] first reported that insulator functions are affected by mutations in the RNAi machinery . We find in fact that the co-localization is affected by the same mutations in the RNAi machinery . Here too , however , the effects are complex , with some RNAi proteins affecting high-level co-localization but not the basal level . These results suggest that the action of the insulator element involves an RNA component whose nature and role remain unknown . Interestingly , loss of AGO2 has the strongest effects on the high level enhancer-promoted co-localization but no effects on the basic co-localization in the wing disc and its role does not require its enzymatic activity , as previously reported for insulator function [22] . The powerful effect of TRX mutations , perhaps the most intriguing feature of the high-level co-localization , suggests that TRX provides an additional level of stability to the association with a common transcription factory . Loss of co-localization when TRX levels are reduced is not because the transgene is no longer active . Transcription is only slightly reduced , as shown by the eye color of the transgenic flies and by the expression of endogenous genes [29] , [30] . When PcG target genes become transcriptionally active , they form a chromatin domain that binds the TRX N-ter moiety and ASH1 [25] . Genetic evidence shows that the role of these two proteins is to antagonize PcG repression and maintain a “cellular memory” of the derepressed state [29] , [30] , a role that is not shared by Set1 , the methyltransferase that targets H3K4 in the promoter region of most active genes . The effect of TRX is therefore not likely to be due to H3K4 methylation . Although we do not yet understand the molecular bases of this epigenetic memory , the powerful effect of TRX suggests ( but does not prove ) that co-localization may play a part . It may be relevant to this role that MLL1 , the mammalian TRX homologue , remains associated with promoters in mitotic chromosomes [38] . It is possible therefore that TRX and ASH1 mediate a more stable association of the derepressed gene with a transcription factory that specializes in TRX-dependent transcription and that the epigenetic memory is more dependent on nuclear localization than on the classical histone modifications . Together with recent work in flies and in mammals , our results indicate that insulator binding proteins have much broader functions than blocking inappropriate action of enhancers and silencers . Our results assign to them a key role in the association of genes repressed by the PcG machinery and in the congress of these genes when they are in the active state . We have not determined directly whether the genes tested are in Polycomb bodies or in transcription factories when they co-localize and , in fact , in any one nucleus a gene may be located in one compartment at one time and another compartment at a later time . However , we have shown that all co-localization of our transgenes is dependent on CTCF , which must therefore be responsible for targeting in both the repressed and active state . In the model shown in Figure 7B , the insulator protein targets a PcG target gene to a Polycomb body when the gene is PcG-repressed . The binding of activators to the enhancer switches the gene to the active mode characterized by the activation of TRX , recruitment of ASH1 [25] and targeting of the gene to a transcription factory . The role of TRX is crucial for high level targeting , implying that it makes a major contribution to the stability of the association with the transcription factory and suggesting that a subset of transcription factories specializes in TRX-dependent genes . We have not looked for a role of insulators in targeting to transcription factories genes that are not PcG targets . How could the same insulator-binding proteins direct a gene to PcG bodies when it is PcG-repressed but to transcription factories when it is in the transcriptionally active state ? Although our experiments give no answers to this question , we have proposed that insulator complexes might be post-translationally modified depending on adjacent silencing activity or transcriptional activity and that such modifications might select the appropriate nuclear compartment [33] . For example , sumoylation of mammalian CTCF has been reported and related to the SUMO-E3 ligase activity of PC2h [39] , [40] . Is there a functional advantage to co-localization in either case ? This is difficult to evaluate but the following arguments suggest that there is . PcG repression in Drosophila displays well-known pairing-dependent effects . A transgene containing a PRE that is partially repressed when present in one copy , usually becomes much more strongly repressed when the transgene insertion is homozygous [37] . Since homologous chromosomes are paired during interphase in Drosophila , this implies that physical proximity of two ( or more ) PREs increases the degree or stability of silencing . In the case of an active gene , it has been shown that a PcG target gene such as Ubx is more strongly expressed when the two homologous copies are paired than when pairing is prevented by chromosome rearrangements [41] . It could be argued that the functional advantages of co-localization constitute one of the reasons why many PcG target genes are found in genomic clusters [33] .
The LacO-Mcp construct was described in [17] . To create the related Mcp-Eye plasmids , the eye enhancer and 820 bp Mcp fragments were PCR amplified using primers with appropriate restriction sites . The eye enhancer used is a 1110 bp HincII-BanHI fragment described in [13] , and was PCR-amplified from Drosophila larva genomic DNA . The 820 bp Mcp was the widely used SalI-XbaI fragment , amplified from BX-C clone BAC R24L18 ( obtained from BACPAC Resources Center , http://bacpac . chori . org/ ) . The amplified fragments were ligated into plasmids containing LoxP and FRT cassettes and the resulting plasmids were sequenced to verify the inserted sequence . The resulting plasmids were cut with KpnI , releasing the Lox-Eye En-Lox-FRT-Mcp-FRT fragment , which was inserted in either orientation into the acceptor plasmid containing the mini-white gene , yielding products with different arrangements of Mcp and eye enhancer relative to mini-white . The tandem array of 128 copies of LacO was isolated from pAFS150 ( a gift from J . Vazquez ) and inserted into the pC4Yellow vector [34] and the resulting plasmid was used to accept the LoxP-flanked Mcp insulator part , the FRT-flanked Mcp PRE part and mini-white gene . The plasmids McpΔPRE-Eye , Mcp-UAS and Mcp-Ubx were similarly constructed , except using different PCR-amplified fragments . The insulator part of Mcp used in McpΔPRE-Eye was amplified from BX-C clone BAC R24L18 , the 5×UAS fragment was amplified from pUASTattB ( GenBank: EF362409 . 1 ) , and the 2250 bp Ubx imaginal enhancer fragment was previously described [31] . Transgenic fly lines were made according to standard procedures [42] . Southern blot hybridization was used to verify that the lines contained a single insert and inverse PCR was used to identify the exact insertion sites . The various deletion derivatives were established with the help of Flipase and Cre recombinase-producing stocks [43] and were verified by PCR analysis . For co-localization studies , two transgene lines on different chromosomes were crossed together using double balancers . Insertions on the same chromosome were recombined to obtain a cis-arrangement . PCR was used to verify the presence of both transgenes . The fly line expressing EGFP-LacI from the ubiquitous Ubiquitin promoter was described in [17] . The mRFP-LacI fly line was kindly provided by Dr . A . Csink [44] . Mutations Pc3 and trxE2 are loss of function mutations ( FlyBase , http://flybase . org/ ) . Mutants CTCFy+2 , CP190p11 , CP1904-1 , CP190H31-2 , AGO2v966m , AGO251B , piwi1 , piwi2 , aubQC42 , aubp-3a , Rm62sh ( 3 ) 029 , Rm6201086 were generously provided by Drs . E . P . Lei and V . G . Corces . Mutants Smc17-13a , Smc1ex46 , Rad21ex15 , and Rad2136RipP were kindly provided by Dr . D . Dorsett . Experiments with mutations other than trx and Pc were done using trans-heterozygous allele combinations . 3C experiments were done as previously described [45] , [46] with few modifications , using chromatin isolated from eye and/or wing imaginal discs dissected from 100∼150 third instar larvae in 1×PBS buffer ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 , pH 7 . 4 ) containing 10% fetal calf serum . The tissue was fixed 10 min in 2% paraformaldehyde/PBS at room temperature . The cells were lysed in lysis buffer ( 10 mM Tris-HCl , 10 mM NaCl , 0 . 2% NP-40 , pH 8 . 0 , with Roche protease inhibitor cocktail freshly added ) on ice for 10 min , followed by 20 strokes of a Dounce homogenizer . The nuclei were recovered , washed and resuspended in 400 µl 1 . 2×NEB3 buffer ( 120 mM NaCl , 60 mM Tris-HCl , 12 mM MgCl2 , 1 . 2 mM Dithiothreitol , pH 7 . 9 ) with 0 . 3% Sodium dodecyl sulfate ( SDS ) . After shaking for 1 hr at 37°C , Triton X-100 was added to 1 . 8% , and shaking was continued for another 1 hr at 37°C . After digestion with EcoRI ( 200 units ) overnight , the enzyme was inactivated with 1 . 5% SDS at 65°C for 25 min . 1% Triton X-100 was used to neutralize the SDS at 37°C for 1 hr . The DNA was ligated in 2 . 4 ml with 20 µl ligase ( 400 U/µl , NEB ) at 16°C for 4 . 5 hrs , then 1 hr at room temperature . The 3C template DNA was then un-crosslinked overnight at 65°C , extracted with phenol-chloroform , and dissolved in 100 µl Tris buffer ( 10 mM Tris·Cl ) . 3C primers were designed for the regions flanking the religated restriction sites , close to the insertion sites of transgenes . As a control for the crosslinking and ligation procedure we used Primers K1 and K2 , close to adjacent EcoRI fragments in the Brk gene ( X chromosome ) and pointing in the same direction . All 3C primers are listed in Table S1 . The 3C PCR reactions were done using the following cycles: denature at 95°C for 8 min , then 40 cycles of 95°C 15 s , 55°C 20 s , 72°C 20 s , finally 72°C extension for 10 min . For the K1/K2 control reactions , 36 cycles of PCR were used . To quantify the 3C interactions in BG3 and Sg4 cells , Taqman Probes ( from Integrated DNA Technologies , Inc . ; sequences listed in Table S1 ) were used for qPCRs . All 3C PCRs were repeated independently 2 or 3 times . The live-imaging was done as described previously [17] , [18] . To visualize the transgene tagged with 128 copies of LacO repeats inserted in the genome , the transgenic flies were crossed to LacI-EGFP flies , and the resultant embryos grown at 18°C in medium supplemented with active dry yeast . Third instar larvae were rinsed and dissected in Gibco Schneider's Drosophila medium ( Invitrogen Co . ) . The dissected eye and wing imaginal discs were aligned on a coverslip bottom dish ( MatTek Co . ) with a drop of Drosophila medium and then covered with a coverslip . In similar imaging conditions , tissue cells have been found to stay alive for up to 12 hours . Usually , the dissected tissues were immediately subjected to direct microscopy , which finished within 1 hour . Z-stack images across at least one layer of cells were taken with a DeltaVision Image Restoration Microscope system ( Applied Precision Instrument , LLC Issaquah , WA ) using a 100×/1 . 35 UplanApo objective , deconvoluted and processed with the SoftWoRx software ( Applied Precision Instruments ) . Each tissue in the dish was imaged no more than twice to avoid photo-bleaching . We mainly imaged eye imaginal discs and wing imaginal discs . For eye discs , only the region posterior to the morphogenetic furrow was examined . The dots in each nucleus were manually scored by moving the 3D images up and down along z-axis , one dot as co-localization and two non-overlapping dots ( center-to-center distance greater than 0 . 3 µm ) as no co-localization . Since the expression of LacI-EGFP was driven by the Ubiquitin promoter , all the cells in all tissues under investigation showed one bright GFP dot per cell containing a single inserted transgene , showing that transgene detection is 100% . Since the LacI-EGFP contains a nuclear localization signal , a weak diffuse GFP signal demarcates each nucleus . In lines lacking Mcp , more than 99% of the nuclei have two dots , arguing that we are not likely to overlook one of the two transgenes [17] . In addition , when two dots are seen , the intensity of each is always lower than when a single dot is seen , indicating that the single dot is the sum of two transgene signals . The percentage of one-dot cells over total cells was used to measure the frequency of co-localization for each individual fly line . More than a dozen eye or wing discs were counted for each specific fly line . Variation in the percentages among eye discs or among wing discs in a given line was less than 2% . We therefore added the counts from a given tissue to represent each fly line by more than 500 cells for the eye discs or wing discs . χ2 tests were used for comparison of each fly line and its derivative lines or mutant backgrounds to calculate the probability that the differences might be due to chance . All statistical analysis was done with the software JMP ( SAS Institute Inc . ) and the results are tabulated in Table S2 . Analysis of the immunofluorescence results is described under Immunofluorescence . Eye and wing imaginal discs were dissected from third instar larvae and fixed with 2% para-formaldehyde for 20 minutes at room temperature . The fixed tissues were subjected to extensive washing with PBTr ( 1×PBS , 0 . 3% Triton-X100 ) , then incubated with blocking buffer and mouse monoclonal antibody against RNA Pol II large subunit , clone 3 ( generously provided by H . Saumweber ) or with mouse monoclonal anti-PSC ( Santa Cruz ) . After extensive wash , the tissue was stained with anti-mouse Cy3 secondary antibody . The slides were sealed with VECTASHIELD mounting medium ( Vector Laboratories ) and image acquisition was done at 100× magnification using the DeltaVision Image Restoration Microscope system . NIH ImageJ software was used to analyse the images . The 3D stack was first projected , and the background fluorescence was subtracted by adjusting the threshold . The particle size was set at >2 µm2 , the number of bodies and the mean intensities of the bodies were computed by the software . The maximum intensities in each nucleus were also measured . The Pooled t-test ( assuming equal variances ) , and Satterthwaite t-test ( assuming unequal variances ) were used to compare the difference in the number of bodies , the mean intensities , and the maximum intensities of the two sets of data ( LacZ control and CTCF RNAi cells ) using SAS software ( SAS Institute Inc . ) . The two t-tests gave similar results and box plots were used to present the data , with the median shown as a line across the box and the mean indicated by “+” . Drosophila ML-DmBG3-c2 cells ( abbreviated: BG3 ) cells and Sg4 cells were cultured in Schneider's Drosophila Medium ( Invitrogen ) supplemented with 10% heat-inactivated fetal bovine serum . Double-stranded RNAs for CTCF and for lacZ as a negative control were prepared according to the user's manual using the RiboMAX Large Scale RNA Production System—T7 ( Promega ) . Genomic DNA was used to amplify the template for dsRNA synthesis . The primers are listed in Table S1 . The RNAi procedure exactly followed ref . [47] . After RNAi treatments , the harvested cells were lysed and subjected to western blots ( Figure S5 ) to verify the efficacy with mouse anti-CTCF antibody ( generously provided by V . G . Corces ) . The RNAi knockdown cells were fixed with 2% paraformaldehyde , and then either subjected to the 3C procedure with 107 cells , or used for the immunofluorescence experiments as described above . | We have studied the nuclear localization of genes that are regulated by Polycomb mechanisms . The genomes of higher eukaryotes contain hundreds of genes that are regulated by Polycomb mechanisms . Once repressed by Polycomb complexes , they tend to stay repressed; but , when activated , they bind Trithorax protein and tend to maintain the active state epigenetically . Polycomb repression has been reported to make these genes associate in the nucleus to form “Polycomb bodies . ” We find that this association is not caused by Polycomb complexes but by insulator elements accompanying the genes . We show that , when these genes are in the active state , the binding of Trithorax targets them to other nuclear regions where transcription occurs , so-called transcription factories . In these nuclear re-positionings the insulator provides the associative power while the state of activity determines whether a gene goes to a Polycomb body or to a transcription factory . The strong effect of Trithorax suggests the possibility that the stable association with a transcription factory it produces may account for the epigenetic memory of the active state . | [
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"function"
] | 2013 | Insulators Target Active Genes to Transcription Factories and Polycomb-Repressed Genes to Polycomb Bodies |
During the first trimester of pregnancy the uterus is massively infiltrated by decidual natural killer cells ( dNK ) . These cells are not killers , but they rather provide a microenvironment that is propitious to healthy placentation . Human cytomegalovirus ( HCMV ) is the most common cause of intrauterine viral infections and a known cause of severe birth defects or fetal death . The rate of HCMV congenital infection is often low in the first trimester of pregnancy . The mechanisms controlling HCMV spreading during pregnancy are not yet fully revealed , but evidence indicating that the innate immune system plays a role in controlling HCMV infection in healthy adults exists . In this study , we investigated whether dNK cells could be involved in controlling viral spreading and in protecting the fetus against congenital HCMV infection . We found that freshly isolated dNK cells acquire major functional and phenotypic changes when they are exposed to HCMV-infected decidual autologous fibroblasts . Functional studies revealed that dNK cells , which are mainly cytokines and chemokines producers during normal pregnancy , become cytotoxic effectors upon their exposure to HCMV-infected autologous decidual fibroblasts . Both the NKG2D and the CD94/NKG2C or 2E activating receptors are involved in the acquired cytotoxic function . Moreover , we demonstrate that CD56pos dNK cells are able to infiltrate HCMV-infected trophoblast organ culture ex-vivo and to co-localize with infected cells in situ in HCMV-infected placenta . Taken together , our results present the first evidence suggesting the involvement of dNK cells in controlling HCMV intrauterine infection and provide insights into the mechanisms through which these cells may operate to limit the spreading of viral infection to fetal tissues .
Human cytomegalovirus ( HCMV ) infection is mostly asymptomatic in healthy adults and results in the establishment of long term latency . On the contrary , life threatening diseases may occur in immunocompromised patients after viral reactivation or primary HCMV infections . HCMV is the most common cause of intra-uterine viral infections and a leading cause of congenital infection [1] , [2] . Even though maternal-fetal transmission is not systematic [3] , the prevalence of HCMV transmission is about 30% in the first trimester of pregnancy and can reach up to 72% in the third trimester [4] . It is believed that the first steps of infection and amplification take place in the decidua where both maternal and fetal cells are in close contact [5] . Human placentation is associated with a large increase of decidual NK cells ( dNK ) . During the first trimester of pregnancy , dNK cells are the major population of maternal immune cells as they count for 70% of total immune cells present in the decidua in the first trimester of pregnancy [6] , [7] , whereas other immune cells , macrophages , T cells ( including CD8 , CD4 and γδ T cells ) and dendritic cells count for 20 , 10 and 2% respectively . The role of dNK cells during pregnancy is not yet fully understood . Their contribution to successful placentation versus their potential ability to exert cytotoxicity remains a major paradox [8] , [9] . By secreting a unique profile of cytokines/chemokines and angiogenic factors , dNK cells are thought to be crucial for successful placentation and materno-fetal immune tolerance [9]–[15] . dNK cells exhibit different phenotypic and functional characteristics from other peripheral blood NK cells ( pNK ) . The majority of dNK cells are CD56brightCD16neg and they express a repertoire of activating and inhibitory receptors ( NKRs ) that resembles that of early differentiation stages of pNK cells [9] , [16]–[19] . The lack of dNK cell cytotoxicity has been attributed to defects in the formation of the immunological synapse and/or failure of 2B4 receptor to convey activating signals [8] , [20] , [21] . In contrast to the clearly defined role of human and mouse pNK cells in controlling viral infections [22]–[31] , little is known about the ability of dNK cells to control viral infections during pregnancy [17] , [32] , [33] . dNK cells represent the major decidual lymphoid population in the first trimester of pregnancy [7] , [6] and vertical transmission of HCMV to the fetus is quite low during this trimester , therefore it is conceivable that dNK cells might be involved in limiting HCMV viral spreading to fetal tissues . To test this possibility , we have conducted detailed analysis of functional and phenotypic changes of first trimester of pregnancy dNK cells after their exposure to infected target autologous fibroblasts . We found that dNK cells acquire cytotoxic effector function that is associated with phenotypic alterations in their receptor repertoire expression and involves key receptor-ligand pairs . Furthermore , we found that dNK cells were able to sense HCMV infection , migrate and infiltrate placental tissues both in tissue organ culture and in situ in HCMV-infected placenta . These results suggest that dNK cells control HCMV spreading across mucosal tissues probably through the acquisition of cytotoxic profile .
Our previous study provided evidence indicating that cytolytic function of dNK cells during normal pregnancy is partially controlled by negative signals that involve NKG2A receptor [9] suggesting that such function might be modulated upon viral infections . Therefore , to test the possible involvement of dNK cells in controlling HCMV infection we examined their cytotoxic effector function against HCMV-infected autologous decidual fibroblasts . dNK cells and decidual fibroblasts ( Figure S1A ) were purified from the same decidua basalis . High purity fibroblasts ( Vimentinpos and Cytokeratin-7neg , Figure S1A ) were infected with two strains of HCMV; the VHLE clinical isolate and the laboratory strain AD169 . Decidual fibroblasts were efficiently infected by both strains as evidenced by staining for HCMV-IE nuclear protein ( Figure S1B and data not shown ) where more than 60%±3 ( mean ± S . D . ) of cells were infected after 48 h ( Figure S1C ) . Through co-cultures in autologous settings , we then investigated the cytotoxicity of dNK cells by conventional chromium release assay . Neither dNK cells nor pNK cells killed efficiently HCMV-infected decidual fibroblasts after 4 h of contact ( Figure S2A & B ) . However , after 18 h dNK cells efficiently killed VHLE- or AD169-infected fibroblasts ( Figure 1A & B ) . With both strains up to 75% of killing was reached at the effector to target ratio of 50 and no killing of autologous uninfected fibroblasts was observed indicating the specificity of the cytotoxic function against HCMV-infected targets . Significant increases were also observed in pNK cell lysis of HCMV-infected autologous fibroblasts after 18 h of contact ( Figure S2C ) . Given that no major differences were observed between VHLE or AD169 strains , we extended the analysis of dNK cell cytotoxicity to a cohort of 10 decidua basalis and confirmed that dNK cells can specifically kill AD169-infected fibroblasts , although with some variable efficiency ( Figure 1C ) . Taken together , these data suggest that under HCMV infection dNK cells become cytotoxic against infected autologous fibroblasts . To further confirm the cytotoxic function of dNK cells , we next investigated lytic capacities of dNK cells in an MHC mismatched ( heterologous ) setting ( Figure S2D–E ) . dNK cells were purified from one decidua basalis and their killing activity was tested against either uninfected or HCMV-infected heterologous decidual fibroblasts . While very little killing was observed after 4 h of contact ( Figure S2D ) , up to 60% of uninfected and HCMV-infected heterologous fibroblasts were killed after 18 h of contact ( Figure S2E ) . To exclude any external bias that could be responsible for initiating dNK cell cytotoxicity against heterologous fibroblasts , we tested the ability of dNK cells to kill K562 classical NK cell targets ( Figure S2F ) . In agreement with previous studies [21] , very little lysis was observed in the presence of dNK cells while pNK cells killed up to 75% of K562 cells ( Figure S2F ) . Further analyses demonstrate that while dNK cells killed more than 55% of HCMV-infected autologous fibroblasts after 18 h of contact , they did not kill semi-allogeneic fetal trophoblasts ( Figure S2G ) . In the same manner pNK cells did not kill semi-allogeneic trophoblasts ( data not shown ) . These observations suggest that dNK cells that are tolerant both in vivo and in vitro to semi-allogeneic fetal trophoblasts become activated when there is a danger signal such as HCMV-infection . NK cells achieve target cell killing either through delivery of soluble mediators or by triggering death receptor-ligand pathways such as Fas ligand ( FasL ) or the tumor necrosis factor-related apoptosis-inducing ligand ( TRAIL ) . To provide insights into the mechanisms involved in dNK cell killing of HCMV-infected fibroblasts , we investigated the involvement of the death receptor-ligand pathway ( Figure 1D–E ) . We used neutralizing antibodies to either TRAIL or FasL that are expressed on dNK cells , to block their interaction with cognate death receptors expressed on target cells . After 18 h of co-culture , the blockade of either FasL ( Figure 1D ) or TRAIL ( Figure 1E ) did not affect dNK cell cytotoxicity against HCMV-infected autologous fibroblasts . The blocking ability of both mAbs was confirmed since they prevented TRAIL- or FasL-induced killing of Jurkat cell line ( see Figure S2H ) . These data strongly suggest that dNK cell killing of HCMV-infected fibroblasts proceeds through mechanisms independent of the death receptor-ligand pathways . The delivery of perforin/granzyme lethal hits is a highly regulated multistep mechanism that involves the formation of a dynamic structure , namely immunological synapse ( IS ) , between NK cell and its target [34] . We undertook a stepwise approach to dissect the involvement of perforin-induced killing mechanisms . First , we analyzed the capacity of dNK cells to form IS with autologous targets . Conjugates formation between dNK cells and uninfected/HCMV-infected autologous decidual fibroblasts was analyzed after 20 min of interaction by monitoring F-actin remodeling and confocal microscopy . Although dNK cells recognized both uninfected and HCMV-infected target cells , as evidenced by their actin-enriched flattened shape ( Figure S3A ) , only 17% uninfected cells were engaged in conjugates with dNK cells while more than 55% AD169-infected fibroblasts were recognized by dNK cells ( Figure S3B ) . Thus , dNK cells form conjugates preferentially with HCMV-infected fibroblasts and reorganize their F-actin cytoskeleton at 20 min . Being critical for the trafficking and delivery of lytic granules to the IS in NK cells [35] , [36] , we then analyzed the microtubule organizing center ( MTOC ) ( Figure 2A ) and the Golgi apparatus polarization ( Figure S3A ) in fixed conjugates after 20 min of interaction . dNK cells in contact with uninfected cells displayed a random localization of the MTOC ( Figure 2A ) . In contrast , the majority of conjugates formed with HCMV-infected targets displayed a reoriented dNK cell MTOC towards the immune synapse ( Figure 2A ) . We then finely defined the MTOC reorientation by measuring the distance between dNK cell MTOC and the center of the IS for each conjugate ( defined as the center of the interaction zone dNK cell-target , see scheme Figure 2B ) . The distance between the MTOC and the center of IS showed a quite compact distribution in dNK cells that contacted AD169-infected fibroblasts with a mean distance of 4 . 6±1 . 25 µm ( mean ± S . D . ) ( Figure 2B ) . In contrast , the distance from the MTOC to the center of the contact zone was very variable in dNK cell that formed conjugates with uninfected cells ( Figure 2B ) with a mean distance of 9 . 1±3 . 4 µm ( mean ± S . D . ) . We next analyzed the distribution of lytic granules containing perforin after 20 min of conjugation ( Figure 2 ) . Similar to the MTOC , perforin containing granules were localized in a random manner , but upon recognition of HCMV-infected cells , dNK cells polarized their perforin containing granules with the MTOC close to the contact zone ( Figure 2A ) . Quantification of perforin polarization in a large number of immune synapses , demonstrated that while the majority of dNK cells that formed immune synapse with AD169-infected fibroblasts showed polarization of their lytic granules ( 83±4% ) , only 28% of dNK cells showed polarization towards uninfected targets ( Figure 2C ) . Interestingly , when using a mixture of infected and non infected cells ( one to one ratio ) , dNK cells polarize their MTOC and secretory machinery preferentially towards HCMV-infected fibroblasts ( data not shown ) . Consistent with the MTOC and lytic granules , the Golgi apparatus was also distributed in clusters close to the MTOC only in dNK cells that formed immune synapses with AD169-infected fibroblasts ( Figures S3A ) , but not in those that formed conjugates with uninfected targets ( Figure S3A see right enlargement panels ) . One of the critical step in the NK-IS formation includes the clustering of specific receptors that contribute to NK cell activation [34] . Despite the fact that CD9 would have been a better choice as it is mainly expressed by dNK cells but not pNK , decidual fibroblasts ( data not shown ) and other human fibroblasts express substantial amounts of this receptor [37] , [38] we choose to analyze the localization of CD2 receptor for two main reasons . CD2 is expressed on the majority of dNK [9] , [21] and it has been shown to rapidly cluster at the NK-IS [34] , [39] , [40] . Confocal analyses revealed that CD2 receptor microclusters were concentrated at the intercellular contact zone only in dNK cells that formed conjugates with infected fibroblasts ( Figure S3C ) . We did not observe any changes in CD56 localization ( data not shown ) . Thus , dNK cells engage mature immune synapse with HCMV-infected autologous fibroblasts that is characterized by polarization of the MTOC , the secretory machinery and clustering of CD2 activating receptor at the intercellular contact zone . We next examined whether dNK cells were able to degranulate upon recognition of HMCV-infected fibroblasts by analyzing the cell surface expression of the Lysosomal-associated membrane protein 1 ( LAMP1/CD107a ) ( Figure 2D ) . The kinetics of CD107a cell surface expression by dNK cells in contact with HCMV-infected autologous fibroblasts was carried out for 8 hours . Very little variations were observed within the first four hours of contact . After six hours of contact , a significant increase of CD107a expression was observed in dNK cells that are in contact with HCMV-infected autologous fibroblasts . The degranulation reached maximal level by 8 hours of contact ( Figure 2D ) . The significant increase in CD107a cell surface expression indicates that IS formation is accompanied by efficient release of lytic granules and that dNK cells cytotoxicity is perforin-dependent but only after six hours of contact . Collectively , these findings indicate that dNK display cytotoxic activity towards HCMV-infected autologous decidual fibroblasts but also emphasize the unique properties of dNK cells cytotoxicity . Even if dNK cells can form mature IS within normal range of time they do need extended time frame in order to release their lytic granules and perform efficient killing of HCMV-infected autologous fibroblasts . The repertoire of NK activating and inhibitory receptors ( NKRs ) plays a critical role in cytotoxic activity of pNK cells and modulation of NKRs expression by these cells is often associated with their response to HCMV [30] . Thus , to provide further insights into the mechanisms involved in dNK cell cytolytic activity against HCMV , we analyzed whether these cells modulate their NKRs repertoire upon recognition of infected fibroblasts ( Figure 3 ) . Similar to freshly isolated dNK cells ( data not shown ) , more than 76 . 3±5% ( mean ± S . D . ) of dNK cells co-cultured with uninfected autologous fibroblasts were CD56bright ( Figure 3 ) . Exposure to HCMV-infected fibroblasts significantly decreased the percentage of CD56bright dNK cells ( 48±6 . 3% ) , but significantly increased the percentage of CD56dim cells ( 40±4% ) . The dampening down of CD56 expression was observed even after 18 hours of contact ( Figure S4B ) consistent with the acquisition of the cytotoxic profile . The changes in CD56 expression profile is always associated with the acquisition of CD16 expression ( 41% compared to 4 . 3% ) . There was a slight decrease in the mean fluorescence intensity of CD69 but the absolute number of CD69pos dNK cells ( 85±5% ) did not vary after contact with HCMV-infected fibroblasts . Although optimal changes were reached by 48 h , our data demonstrate that HCMV infection orchestrate dampening of CD56 and increase of CD16 expression observed as early as 18 h of contact ( Figure S4B ) which is consistent with acquisition of a cytotoxic profile . To further characterize phenotypic changes in dNK cell receptor repertoire , we analyzed the expression of natural cytotoxicity receptors ( NCRs ) ( NKp30 , NKp44 , and NKp46 ) , NKG2D that recognize viral or stress induced ligands and NKG2A or C receptors that are expressed by a large fraction of dNK cells and recognize HLA-E molecules ( Figure 3 ) . The frequency of dNK cells expressing NKp44 activating receptor was significantly increased in dNK cells that were exposed to HCMV-infected fibroblasts as compared to those exposed to uninfected fibroblasts ( 90% versus 46% ) . Furthermore , co-culture with infected cells also induced major changes in the expression of NKp46 receptor . A significant shift in the fluorescence intensity towards an NKp46low profile with a complete loss of the bimodal NKp46hi and NKp46low expression pattern was observed when dNK cells were exposed to HCMV-infected cells . More than 80% of dNK cells become NKG2C+ after their exposure to HCMV-infected fibroblasts , while only minor yet reproducible decreases in the percentage of NKG2A+ cells was observed . Exposure to HCMV-infected cells induced significant decrease in the percentage of cells expressing KIR2DL1 , KIR2DL4 and ILT-2 , while no changes were observed with those expressing KIR2DL2/3 ( Figure S4A ) . Some of the changes in the expression of dNK cell repertoire were observed after 18 hours of contact ( Figure S4B ) while only discrete changes were observed for pNK cells ( Figure S4C ) further highlighting the originality of dNK cells and stretching their differences compared to pNK cells . Altogether , our data indicate that HCMV infection induces major changes in dNK cell receptor repertoire with increases in NKp44 , NKG2C and decreases in NKp46 , KIR2DL1 , KIR2DL4 and ILT2 expression . It has been suggested that de novo expression of MHC-II by NK cells and their acquisition of an APC-like phenotype could regulate the activation of numbering immune cells in particular T cells . Therefore , to further examine the modulation in dNK cells properties and phenotype upon exposure to HCMV-infected fibroblasts we examined the expression of HLA-DR in dNK cells co-cultured with infected and non-infected cells ( Figure 3 ) . A great fraction of dNK cells exposed to HCMV-infected fibroblasts , but not uninfected cells , acquired significant de novo expression of MHC-II DR at their cell surface ( 48% ) displaying a bimodal distribution of fluorescence with a prevalence of positive cells expressing intermediate levels of these cell surface molecules . The acquisition of HLA-DR expression was effective even after 18 hours of contact ( Figure S4B ) . Increases of HLA-DR expression were also observed in pNK cells that were in contact with HCMV-infected autologous fibroblasts ( Figure S4C ) . Taken together , these data show that exposure to HCMV-infected fibroblasts not only modulates dNK cell receptor repertoire but also increases the expression of key elements of adaptive response ( HLA-DR ) . Cytotoxic function of NK cells could involve several NKRs . To provide insights to their possible involvement in dNK cell cytotoxicity against HCMV-infected fibroblasts , we took advantage of Fc-chimeras to analyze NKR ligands expression in uninfected , AD169-infected ( Figure 4A ) , or VHLE-infected ( Figure S5A ) decidual fibroblasts . Uninfected fibroblasts expressed low levels of NKp30L . Similar to human fetal foreskin fibroblasts ( HFFF ) [41] , HCMV infection led to an increase in NKp30L expression by decidual fibroblasts ( Figure 4A , S5A ) . Decidual fibroblasts expressed low levels of NKp46L that was further decreased after HCMV infection . By contrast to NKp30L , ligands for NKp44 , and NKG2D were highly expressed in uninfected decidual fibroblasts ( Figure 4A , S5A ) . Both HCMV strain induced significant decreases in the expression of NKp44L and NKG2DL ( Figure 4A , S5A ) . We then investigated whether HCMV infection affected the expression level of HLA-E cell surface molecules . As shown in figure 4A ( and S5A ) , decidual fibroblasts expressed both the nonclassical HLA-E and the classical HLA-A , -B , -C molecules at their surface . While infection with HCMV resulted in a significant decrease in HLA-E expression , only small effect was observed for the expression of classical HLA-A , -B , -C . This striking observation of HLA-E downregulation by HCMV prompted us to perform further analyses comparing the impact of HCMV infection in additional decidual fibroblasts and in other cells ( Figure S5B ) . Consistently , we observed downregulation of cell surface expression of molecules HLA-E in HCMV-infected decidual fibroblasts ( Figure 4 , S5A , S5B ) . Consistent with previous studies using HFFF cells [42] , [43] and in contrast to decidual fibroblasts , HCMV resulted in upregulation of cell surface HLA-E in MRC-5 fibroblasts and in HEK293T cells ( Figure S5B and data not shown ) . We also observed a small decrease in the level of HLA-A , -B , -C in these cell lines ( Figure S5B and data not shown ) . Western blot analyses of total amount of HLA-E molecules demonstrated that HCMV-infection did not affect total amount of HLA-E proteins in decidual fibroblasts while increased levels were observed in MRC-5 cells expression ( Figure S5C ) . The CD94/NKG2X ( -A , -C or -E ) family members recognize HLA-E molecule but these receptors can transmit opposing signals [23] , [44] , [45] . The differences between the two systems imply that HCMV infection of decidual fibroblasts might trigger their recognition and promote their killing through engagement of CD94/NKG2C/E activating receptors . This is in line with observed up-regulation of NKG2C on dNK upon their recognition of infected decidual fibroblast ( Figure 3 ) and the high levels of NKG2E on dNK cells [12] . Using Fc-chimeric proteins to block specific receptor/ligand interactions , we found that neither blockade of NKp30 ( Figure 4B ) nor of NKp46 ( Figure 4C ) , both modulated upon HCMV infection , interaction with their putative ligand ( s ) had an effect on dNK cell killing of autologous HCMV-infected fibroblasts . Blocking the interaction of NKp44 activating receptor with its ligand resulted in 50% increased killing of infected autologous fibroblasts ( Figure 4D ) . In contrast , interference with NKG2D receptor ligation induced a significant decrease in dNK cell cytotoxicity; the mean lysis of HCMV-infected fibroblasts was 50% whereas only 20% of infected cells were lysed in the presence of NKG2D-Fc chimeric protein ( Figure 4E ) . The decrease in cytotoxicity after blockade of NKG2D ligation to its cognate ligands expressed on HCMV-infected fibroblasts underscored a role for NKG2D receptor in dNK cell cytotoxicity . Since neither NKp30-Fc nor NKp46-Fc had an effect on dNK cells lysis , we tested the ability of these chimeras to block pNK cell cytotoxicity . The binding of either chimera significantly decreased the killing of K562 cell line by pNK cells ( Figure S6A ) indicating that both chimeras are functionally active . Since HLA-E is a ligand for both inhibitory CD94/NKG2A and activating CD94/NKG2C/E receptors , we explored its involvement in dNK cell cytotoxic response against HCMV-infected fibroblasts . To this end , we performed lysis assay in the presence of an anti-HLA-E blocking monoclonal antibody . Blockade of HLA-E ligation with its cognate receptor resulted in a two-fold decrease of the sensitivity to dNK cell lysis ( 36% compared to 75% for IgG1 isotype control ) ( Figure 4F ) . This inhibitory effect suggests that in our system model , HLA-E on infected-fibroblasts binds to the CD94/NKG2C or -E activating receptors rather than to CD94/NKG2A inhibitory receptor and such binding could mediate the cytotoxic effect of dNK . Examination of pNK cell cytotoxicity shows that even though some minor changes were constantly observed with NKp30 , NKp46 and NKp44 receptors ( Figure S6B–D ) , only the NKG2D receptor played a major role in the killing of HCMV-infected autologous decidual fibroblasts ( Figure S6E ) as its blockade resulted in significant decrease in pNK cell cytotoxicity against autologous fibroblasts . The blockade of HLA-E did not impair pNK cell cytotoxicity ( Figure S6F ) . Taken together , our data uncover a crucial role of NKG2D and CD94/NKG2C or -E activating receptors in dNK cell cytotoxic response against HCMV-infected fibroblasts , while neither NKp30 nor NKp46 are implicated in dNK cell response . By contrast to its activating role in peripheral blood NK [24] , NKp44 have an inhibitory effect in the control of dNK cell cytotoxic function . In normal pregnancy , dNK cells are known to secrete great amount of soluble factors that play a key role in trophoblast attraction and vasculature remodeling . Since some of dNK cell soluble factors have also been found in HCMV secretome [46] , we first analyzed the secretion profile of uninfected and HCMV-infected decidual fibroblasts , using a 42-multiplexed cytokine/chemokine/growth factor Luminex assay ( Figure S7 ) . Decidual fibroblasts produced GRO-α/CXCL-1 , sICAM-1 , IL-6 , IL-8 , IP10 , MCP-1 , MIP1β , MIP1β , and VEGF-A . After HCMV-infection , mild variations were observed for IL-8 , MIP-1β and VEGF-A without reaching statistical significance and only IL-6 secretion was significantly increased in AD169-infected decidual fibroblasts ( 1 . 7 fold increase ) ( Figure S7 ) . To examine whether HCMV infection modulates dNK cell secretion profile , we analyzed specific dNK cell secretion in co-cultures either with uninfected or HCMV-infected autologous decidual fibroblasts ( Figure 5 ) . Although large variations were observed amongst the donors , only a limited number of secreted cytokines , chemokines and growth factors varied after 24 h of co-culture with HCMV-infected autologous targets ( Figure 5 ) . Similar to freshly isolated dNK cells ( [9] and data not shown ) , dNK cells that were in contact with autologous uninfected decidual fibroblasts produced VEGF-A , sICAM-1 , GRO-α/CXCL-1 , IL-6 , Granzyme B ( GZ-B ) ( Figure 5A ) , MIP-1β/CCL4 , IL-8/CXCL8 and IP-10/CXCL10 ( Figure 5B ) . They also produced substantial amounts of GM-CSF , RANTES/CCL5 , MIP-1α/CCL3 and low amounts of MCP-1/CCL2 ( Figure 5C ) . Stimulation of dNK cells with HCMV-infected fibroblasts led to significant increased secretion of VEGF-A ( 1 . 6-fold ) , sICAM-1 ( 1 . 7-fold ) , GRO-α/CXCL-1 ( 2-fold ) , IL-6 ( 1 . 5-fold ) , GZ-B ( 2 . 1-fold ) ( Figure 5A ) and MCP-1/CCL-2 ( 3 . 5-fold ) ( Figure 5C ) . On the other hand , the production of MIP-1β , IL-8 , IP10 ( Figure 5B ) , GM-CSF , RANTES , MIP-1α ( Figure 5C ) was significantly decreased after stimulation with HCMV-infected cells . Finally , all other cytokines and chemokines tested were either below cut-off levels ( IFN-γ , IFN-ω , TGF-α , TNF-α/β , IL-1β , IL-2 , IL2RA , IL-4 , IL-5 , IL-10 , IL-12 , IL-15 , IL-17A/F , EGF , E-Selectin and Leptin ) or did not vary after exposure to HCMV-infected fibroblasts ( basic FGF , IFN-α2 , IFN-β , IL-1α , IL-1RA , IL-22 , SDF-1 , sFas , sFasL , TRAIL , Eotaxin-3/CCL26 , Fractalkine/CX3CL1 ) ( data not shown ) . Overall , these data demonstrate that HCMV-infection modulates the secretory profile of dNK cells , with increased production of cytotoxic factors that may constitute virus-specific immune response . The maternal decidua is the main fetal-maternal interface where maternal dNK cells are in close contact with invasive fetal trophoblast . HCMV virions are believed to disseminate from decidual cells to the invasive trophoblasts and in floating and anchoring villous trophoblasts [5] . To support the relevance of our results , we developed an organ culture model of trophoblastic villi explants to assess the ability of dNK cells to infiltrate infected tissues . Villous explants were isolated , infected ( 48 h ) or not and cultured for 2 h with autologous dNK cells that were labeled with CellTraker Red . As shown in Figure 6A and supplementary movies very few dNK cells were able to establish contact with autologous uninfected trophoblast . However , large number of dNK cells was able to infiltrate and establish close cellular contacts within HCMV-infected organ explants . We were able to analyze organ culture over 250 µm deep section and demonstrate that dNK cells were able to formed synapse like structures with infected cells throughout the section ( see 3D-reconstitution movie ) . These data demonstrate that dNK cells are able to sense and migrate within the infected tissues . We then investigated the ability of dNK cells to interact with infected tissues in vivo; we analyze biopsies of placental samples from 24–26 weeks HCMV+ termination of pregnancy ( Figure 6B ) . Thin sections of placental samples were analyzed by IHC for the presence of NK cells using anti-CD56 marker and anti-CMV-IE antibodies . Analysis of infected placenta showed that CD56pos cells were present at the vicinity of infected HCMV positive cells ( Figure 6B ) while no CD56pos cells were present in the HCMV negative tissue . Together these results clearly demonstrate that dNK ( CD56pos ) cells are able to infiltrate HCMV-infected tissue both in vitro in organ culture model and in situ within HCMV+ placentas , providing thus solid evidence for the implication of dNK cells in controlling HCMV infection and spreading .
Despite their importance in maintaining healthy pregnancy , the control of maternal HCMV infection and spreading by dNK cells is not yet fully understood . Our study is the first to assign a critical role to dNK cells in controlling maternal HCMV infection and in limiting its spreading to fetal tissues through their capacity to acquire potent cytotoxic activity when in contact with infected decidual cells . During normal pregnancy , the majority of dNK cells are CD56brightCD16neg . They secrete a large panel of cytokines and chemokines that are necessary for placental development . We demonstrate that dNK cells undergo phenotypic and cellular changes that allow them to recognize and kill autologous HCMV-infected cells in a FasL- and TRAIL-independent manner . Immunological synapse formation is a crucial step for the delivery of lethal hits by effector cells . Rapid re-localization of the MTOC is needed for the trafficking and the polarization of lytic granules to the IS [34] , [47]–[50] . We show that although dNK cells recognize and engage IS with HCMV-infected cells very rapidly , they require longer exposure time in order to degranulate and exert the cytotoxic effector function . The delay to unleash dNK cell cytotoxic effector function might correspond to the time necessary for dNK cells to mature and acquire necessary functional changes to exert cytotoxicity . However , we cannot exclude that HCMV-infected fibroblasts provide weak signal to induce fast degranulation or that decidual fibroblasts have an inherent resistance to cytotoxic granule mediated cell death . Mechanisms that prevent dNK cell cytotoxicity are not completely understood . Even though dNK and pNK cells exhibit similar expression levels of cytotoxicity encoding genes [12] , under healthy conditions dNK cells are tolerant to semi-allogeneic fetal trophoblasts . Although mechanisms that control cytotoxicity are not well established , they may include strong interactions of inhibitory receptors with their cognate ligands expressed by fetal trophoblast , production of VEGF-C by dNK cells and/or expression of anti-apoptotic proteins ( XIAP ) by target cells [51] , [52] . The lack of dNK cell cytotoxicity can be reversed , at least in vitro , after exposure to cytokines such as IL-5 and IL-18 or upon engagement of specific activating receptors [9] , [21] . Here we show that HCMV infection provides the necessary activating signals to trigger dNK cell cytotoxicity . The fact that dNK cells killed heterologous targets from a different donor further emphasizes the intrinsic ability of these cells to kill when they are exposed to the right activating signals . Our observation that dNK cells did not kill semi-allogeneic trophoblasts but killed HCMV-infected autologous fibroblasts highlights their plasticity and their specific ability to respond to HCMV infection . In contrast to pNK cells , very little is known about dNK cell cytotoxicity as these cells are mainly cytokine and chemokine producers [9] , [10] , [13] , [21] . We demonstrated that under HCMV-infectious conditions , a significant fraction of dNK cells that are CD56bright and CD16neg rapidly dampened down their CD56 expression level and acquired CD16 expression . These changes are most probably due to the acquisition of cytotoxic function . Several NKRs have been involved in pNK cell cytotoxicity [53] . For instance , efficient control of HCMV infection involves NKG2D receptor and can be associated with the emergence of NKG2C+ subset that contribute to long term protective immune response [30] . Exposure of dNK cells to HCMV-infected fibroblasts resulted in an increased NKG2C+ expression without major changes in NKG2A expression . The role of other receptors in NK cell response to HCMV is not completely understood . HCMV is able to decrease a plethora of key receptor-ligand interactions that are involved in NK-cell response . By contrast to changes in pNK cell repertoire [22] , opposite effects were observed for NKp44 and NKp46 receptors while no changes were observed for NKp30 receptor . These observations further highlight differences between dNK and pNK cells modi operandi during HCMV infection . Since the nature of HCMV-induced cellular ligands is not known , we took advantage of NKR-Fc chimeric receptors to analyze the expression of NKR ligands on decidual fibroblasts . Although some variations were observed amongst different decidua basalis , we found that decidual fibroblasts constitutively express ligands for NKp44 and NKG2D while they barely express ligands for NKp30 or NKp46 . HCMV infection induced NKp30L and resulted in significant decreases of NKp44L and NKG2DL but did not affect the expression of NKp46L . These findings suggest that HCMV infection interferes with the expression level of activating receptor ligands even if some of them are of cellular rather than virally induced . Using chimeric proteins , we demonstrated that NKp44 receptor plays an inhibitory function in dNK cell cytotoxicity . dNK cells might express an inhibitory isoform of NKp44 receptor as a result of NCR2 alternative splicing as it has been recently demonstrated for NCR3 ( NKp30 ) [54] . Alternatively , NKp44L expressed on decidual fibroblasts might participate to uncoupling of activating adaptor molecules thus promoting an inhibitory profile . However , the expression of an inhibitory isoform is the most likely explanation since dNK cells constitutively express the NKp44 receptor . It has been clearly demonstrated that HCMV maintains an inhibitory status either by preventing the cell-surface expression of NKG2D activating ligands [55] , [56] or by UL40-mediated up-regulation of HLA-E or MHC-I like surrogates molecules expression . Although , there are some discrepancies between our two observations , namely decreases of NKG2DL and acquired cytotoxicity through NKG2D receptor , it is possible that decreases in NKG2DL are selective resulting in the expression of high affinity ligands . Alternatively , co-engagement of other activating receptors is sufficient even if there is less NKG2D ligands . Further studies are needed to identify NKG2DL that are expressed on decidual cells . Discovery of such ligand and the characterization of specific receptor-ligand interactions that mediates dNK cellular cytotoxicity will help uncover potential therapeutic target that , when activated in vivo , can limit viral spreading and/or prevent congenital disease . Previous investigations demonstrated that both classical and non-classical MHC-I molecules have been targeted by HCMV evasion strategies . By contrast to human fetal foreskin fibroblasts and fibroblastic cell lines [42] , [43] , HCMV-infection resulted in decreased cell surface HLA-E molecules without affecting the total amount of proteins in decidual fibroblasts . The difference between the two cellular systems might reside in the fact that decidual fibroblasts express substantial amounts of HLA-E at the steady state . In decidual fibroblasts , HCMV might interfere with the stability of cell surface HLA-E molecules by impairing rapid protein export or by increasing intra-cellular retention . The inhibitory profile observed upon blockade of HLA-E in HCMV-infected fibroblasts further support the involvement of CD94/NKG2C or possibly CD94/NKG2E activating receptors , both greatly expressed by dNK cells [22]–[31] . In this context , HCMV peptides might play a critical role in promoting the recognition of HLA-E by activating members of CD94/NKG2C and CD94/NKG2E receptors thus increasing susceptibility of decidual fibroblasts to dNK cell cytotoxicity at early times of infection as it has been shown previously for pNK cells [57] . It will be very interesting to investigate whether late HCMV infection is responsible for similar changes and whether specific HCMV peptides play roles in the sequential changes in dNK cell function . In parallel to these changes in NK cell receptor , dNK cells acquire de novo expression of MHC-II DR molecules . This potential acquisition of an APC-like phenotype during the course of HCMV immune response might play a crucial role in initiating a cross-talk with neighboring immune cells , including CD4+ T cells . Indeed , within the fetal-maternal interface , dNK cells are in close proximity with decidual CD4+ T cells . Expression of MHC-II DR antigens might be necessary for dNK cell activation and for shaping up the adaptive immunity [58] , [59] . However , further investigations are needed to demonstrate whether the expression of MHC-II molecules is associated with the acquisition of APC capabilities and HCMV antigen presentation . It is very intriguing that only few cytokines and chemokines varied in the presence of HCMV infected fibroblasts . HCMV infection induces IL-6 secretion most probably through the expression of the viral-encoded chemokine receptor US28 and the activation of the IL6/STAT3 signaling pathway [60] . Interestingly , IL-6 was further increased when dNK cells were in contact with HCMV-infected fibroblasts , most probably through a paracrine effect on dNK cells . sICAM-1 was also increased under HCMV conditions . Previous reports suggested that IL-6 down-regulates the production of several soluble factors [61] , while sICAM-1 increases have been correlated to HCMV reactivation [62] . Both IL-8 and IP-10 are necessary for trophoblast migration as these cells express a panel of receptors allowing them to respond to these chemokines [10] . By lowering the level of IL-8 and IP-10 , dNK cells might reduce trophoblast invasion and prevent viral spreading from decidual stroma to fetal tissue or be partially responsible for fetal damages . Remarkably and in sharp contrast with pNK cell response to viral infection [63] , [64] , there were no changes in secretion levels of cytokines such IL-12 , IL15 , type I IFN , TNFα or IFN-γ that are all known to regulate NK cell function . Moreover , it is possible that changes in dNK cell secretome create the necessary inflammatory environment that will favor the recruitment and the initiation of anti-HCMV adaptive immune response . We demonstrate that during HCMV infection , there is a bias of the inflammatory environment in the decidua basalis . dNK cells seem to lose their “decidual status” and become killers in order to limit viral infection . Exposure to HCMV infection can imprint dNK cell receptor repertoire towards killer activity . We demonstrate that NKG2D , NKG2C/E activating receptors play a crucial role in dNK cell cytotoxic response against HCMV-infected fibroblasts . The fact that dNK cells are able to infiltrate HCMV-infected tissue in vitro and engage immunological synapse-like structures within the infected placentas in situ strongly suggest that dNK cells are key players in controlling HCMV infection and spreading during pregnancy . To our knowledge , we provide for the first time evidence for the involvement of dNK cells in clearing HCMV infection . In fact , we clearly show that dNK cells that are present only in the decidua basalis during healthy pregnancy are in contact with HCMV-infected fetal tissue in vivo . It is possible that upon activation there is an increased dynamic of dNK cells allowing them to rich fetal site , which is normally devoid of maternal immune cells , and kill HCMV-infected cells . Recent reports have clearly linked the ability of NK cells in controlling HCV replication and liver fibrosis to specific soluble factor secretion and/or specific activating receptor expression [65] , [66] . Future studies , with large cohort of placentas from medical termination of pregnancy due to congenital HCMV infection , will be necessary to clarify the dynamic of dNK cell activation in vivo as well as the pivotal role of soluble factor secretion in mounting proper anti-HCMV responses and limiting virus spreading . In conclusion , our data shed new light on the plasticity of dNK cells and provide evidence for a correlation between phenotypic changes and functional anti-viral response . We have demonstrated the ability of dNK cells to exert anti-viral effector functions in vitro and to infiltrate HCMV infected tissues both ex-vivo and form immune synapse like-structures in vivo . Careful investigations of dNK cell status in vivo in larger cohorts of HCMV+ termination of pregnancy will be required to see whether this predicts clinical outcome . Understanding mechanisms that regulate switch in dNK cell immune tolerance will help us discover key factors/pathways that are involved in the immunopathology of HCMV infection during pregnancy and design strategies to limit congenital infection .
dNK cells were purified from first-trimester decidua basalis ( 8–12 wk of pregnancy ) obtained after elective pregnancy terminations as previously described [9] . Briefly , decidua samples were minced , collagenase IV treated . Cell suspension is then allowed to adhere in tissue culture plates . dNK cells were purified from the non adherent cell fraction using MACS negative selection kits according to the manufacturer procedure ( Miltenyi Biotech ) . dNK cells were kept at 4°C in conditioned media containing 20% heat-inactivated fetal calf serum ( FCS ) . Autologous fibroblasts were purified from the adherent mononuclear cell fraction by successive round of mild trypsin treatment . The purity of both dNK cells and decidual fibroblasts was assessed using a cocktail of antibodies . dNK cells were CD3neg and CD56pos . Decidual fibroblasts purity was confirmed by immunostaining with an anti-cytokeratin and anti-vimentin antibodies , fibroblasts are cytokeratin 7neg ( NM_001047870 ) and Vimentinpos ( NM_003380 ) . Decidual fibroblasts were maintained in RPMI-1640 medium ( GIBCO ) supplemented with 10% ( v/v ) FCS and penicillin-streptomycin 100 U/ml each , under a 5% CO2 atmosphere at 37°C . Two HCMV strains were used , AD169 laboratory strain ( ATCC strain , a gift from S . Michelson , Paris , France ) , and VHLE clinical isolate ( a gift from C . Sinzger , Tubingen , Germany ) . Viral stocks were prepared from cell-released virions , using MRC-5 cells as previously described [67] . High titer virus stocks were stored in single use aliquots for up to six months at −80°C . Adherent cell monolayers of decidual fibroblasts or MRC-5 cell line were infected with HCMV particles ( MOI 3–5 ) for 48–72 hours . Trophoblastic villous explants were infected under the same conditions . Fibroblasts were cultured with dNK cells at 1∶1 ratio . After 48 h of co-culture , conjugates were disrupted mechanically by repeated pipeting and dNK cells were collected and washed twice in PBS . dNK cell suspension were then liquoted in 100 µl containing 1×105 cells and labeled with fluorophore-conjugated antibodies . The following mAbs were used: CD56-APC , CD3-PE-Cy7 , CD16-PE , CD69-PE , CD2-PE , NKG2D-PE , NKG2A-PE , HLA-DR-FITC , KIR2DL1-PE and 2B4-PE ( BD Pharmingen , France ) ; NKp30-PE , NKp44-PE , NKp46-PE , KIR2DL2/3-PE ( Beckman Coulter , France ) ; NKG2C-PE , KIR2DL4 clone 181703 ( R&D Systems , France ) ; ILT2-PE ( Biolegend ) ; CD107a-PE , anti-human HLA-I ( HLA-A , -B , -C BC ) -PE ( BD Pharmingen ) and matched isotype controls . Histograms shown were obtained by applying a gate on CD56pos CD3neg dNK cells . Fibroblasts were detached using 0 . 05% trypsin-EDTA , washed twice in buffer containing 1% FCS . Cells ( 5×105 to 106 ) were resuspended in 100 µl of FCS containing buffer and incubated either with primary specific Ab or isotype matched control followed by mouse anti-human IgG1 FITC coupled Ab . The expression of NCR-ligands on fibroblasts was analyzed by binding of NCR-Fc chimera followed immunostaining with FITC-coupled mouse anti-human IgG1 secondary Ab ( Southern Biotec ) . The following chimeras were used: NKp30-Fc , NKp44-Fc , NKp46-Fc and NKG2D-Fc , CD99-Fc ( R&D Systems , France ) . Non specific binding was blocked by preincubating the cells for 30 min in 2% FCS and 1% BSA containing buffer . Data were analyzed using CellQuest ( Becton Dickinson ) . For conjugation , fibroblasts were seeded onto 24-well plates containing glass coverslips . After 16 h adhesion , dNK cells were added at a 1 to 2 ratio and incubated at 37°C . Cells were washed briefly with PBS and fixed with 4% paraformaldehyde for 20 min and washed in PBS . Intracellular staining was in the presence of 0 . 5% Saponin . Cells were incubated in PBS containing 1% heat-inactivated calf serum for 30 min and stained with primary antibodies followed by incubation with Alexa fluor conjugated secondary antibody ( Invitrogen ) as previously described [68] . Filamentous actin cytoskeleton was visualized with Alexa fluor conjugated phalloidin . After extensive washing , coverslips were mounted with vectashield mounting medium ( DAKO ) . Fluorescence was analyzed using Zeiss LSM710 confocal microscope using a 63x oil objective ( Carl Zeiss AG , Jena , Germany ) . Cell morphology was analyzed by examining the phalloidin-stained conjugates as an indicator of F-actin distribution . Images correspond to maximum intensity projection along the z-axis ( Zen software ) . The distance between the MTOC and the center of the IS was measured from single plane of unprocessed images using the single line function of the Imaris ( Biplane Scientific Software ) . The following antibodies were used to analyze microtubules and MTOC , perforin , CMV infection: anti-human alpha tubulin polyclonal Ab ( Sigma-Aldrich , UK ) , anti-human golgin-97 ( Invitrogen ) , anti-human perforin and anti-human CD2 ( BD Pharmingen ) , anti-HCMV-IE ( Argene ) , anti-human vimentin and anti-cytokeratin 7 ( Dako ) . The F-actin cytoskeleton was analyzed using phalloidin coupled to either to Alexa fluor 488 or Alexa fluor 747 ( Invitrogen ) . For degranulation assay , fibroblasts were harvested and incubated at 37°C with dNK cells at a 1 to 1 ratio for different time points . Reactions were stopped on ice , cells were stained with fluorochrome-conjugated anti-human CD107a ( BD Pharmingen ) or isotype matched control Ab in staining buffer containing 1% FCS . Fluorescence was analyzed by Flow Cytometry . Target cells ( 1×106cells ) were labeled with 100 µCi 51Chromium ( Sodium chromate , 1 mCi/ml , Perkin Elmer , Courtaboeuf , France ) . After 1 h incubation at 37°C , cells were washed 3 times in HEPES-buffered RPMI . dNK effector cells were added to 51Cr labeled target cells ( 5×103 ) in replicate at various effector to target ratios in a total volume of 200 µl RPMI containing 5% FCS per well of 96-well round-bottomed microtiter plates . Microtiter plates were centrifuged at 1200 rpm for 5 min and incubated at 37°C . After 4 h or 18 h of culture , 50 µl cell free supernatants were transferred to Lumaplate ( Perkin-Elmer ) and the radioactivity was measured on a TopCount ( Perkin-Elmer ) . The specific cytotoxicity was calculated . Spontaneous release was determined from wells containing target cells alone . Maximum release was determined from wells containing target cells lysed in 1% Triton X-100 . The data were expressed as follows:% specific cytotoxicity = 100×[Sample mean ( cpm ) - Spontaneous mean ( cpm ) / ( Maximum mean ( cpm ) − Spontaneous mean ( cpm ) ] . To block cell lysis due to the engagement of specific activating receptor engagement or specific pathway , 51Cr-labeled target cells were incubated for 20 min on ice with various soluble receptor-Fc IgG1 chimeric protein ( 0 . 2 µg/ml ) , anti-HLA-E mAb ( clone MEM-E/08 , Exbio ) or an isotype match control ( mouse IgG1 ) at the final concentration of 1 . 0 µg/ml then included as targets in cytotoxicity assay with dNK effector cells . dNK cells were incubated with an anti-TRAIL and -FasL antibodies ( 10 µg/ml ) ( R&D Systems , France ) prior to cytotoxicity assay . Recombinant TRAIL and FasL proteins ( gifts from A . Quillet-Mary , Toulouse , France ) were used at the final concentration of 10 µg/ml . dNK cells were co-cultured with uninfected or HCMV-infected autologous decidual fibroblasts in complete medium in 96 microtiter plate . Controls experiments were performed using dNK cells , uninfected fibroblasts , HCMV-infected autologous fibroblasts that were cultured alone in the same conditions . Cleared supernatants replicates from 4 different experiments were collected after 24 hours of culture and stored at −80°C . Cytokines , chemokines and growth factors levels were measured using a 42-multiplexed Affymetrix cytokine assay according to the manufacturer protocol ( Procarta/Ozyme ) . The following cytokines and chemokines were analyzed: IL-1α ( NM_000575 ) , IL-1RA ( NM_000577 ) , IL-1β ( NM_000576 ) , IL-2 ( NM_000586 ) , IL-2RA ( NM_000417 ) , IL-4 ( NM_000589 ) , IL-5 ( NM_000879 ) , IL-6 ( NM_000600 ) , IL-8/CXCL8 ( NM_0005843 ) , IL-10 ( NM_000572 ) , IL-12 ( NM_002187 ) , IL-15 ( NM_000585 ) , IL-17A ( NM_002190 ) , IL-17F ( NM_052872 ) , IL-22 ( NM_020525 . 4 ) , IP10/CXCL10 ( NM_001565 ) , Basic-FGF/FGF2 ( NM_002006 ) , EGF ( NM_005429 . 2 ) , Eotaxin-3/CCL26 ( NM_006072 ) , E-selectin/CD62E ( NM_000450 ) , sFas ( NM_000043 ) , sFasL ( NM_000639 ) , Fractalkine/CX3CL1 ( NM_002996 ) , GM-CSF ( NM_000758 ) , Granzyme B ( NM_004131 ) , GROα/CXCL-1 ( NM_001511 ) , sICAM-1 ( NM_000201 ) , Leptin ( NM_000230 ) , IFN-α2 ( NM_000605 ) , IFN-β ( NM_002176 ) , IFN-γ ( NM_000619 ) , IFN-ω ( NM_002177 ) , MCP-1/CCL2 ( NM_002982 ) , MIP-1α/CCL3 ( NM_002983 ) , MIP-1β/CCL4 ( NM_002984 ) , RANTES/CCL5 ( NM_002985 ) , SDF-1/CXCL12 ( NM_000609 ) , TGF-α ( NM_001099691 ) , TRAIL ( NM_003810 ) , TNF-α ( NM_000594 ) , TNF-β ( NM_001159740 ) , VEGF-A ( NM_001025366 ) . Measurement and analysis were performed using the BioRad Bio-Plex System ( BioRad , France ) . Data points are expressed as follows: Specific dNK cell cytokine-chemokine secretion = [Total concentration of dNK cells cultured in the presence of fibroblasts - Fibroblasts secretion] . Trophoblastic villous explants established from first trimester elective termination of pregnancy samples . Tissue was minced ( 1 to 2 mm ) and placed in 24 well tissue culture plates in complete tissue culture media ( PromoCell , France ) . After four hours of culture at 37°C and two changes of culture media , explants were at either left uninfected or infected with HCMV AD169 for two days . For tissue invasion , trophoblast organ culture nuclei were stained with 4 pM DAPI for 5 min , autologous dNK cells were labeled with 1 µM Cell Tracker Red ( Invitrogen ) for 15 min . All staining procedures were performed at 37°C and quenched with 10 ml of tissue culture media containing 10% FCS . Each explant ( 1–2 mm ) was incubated with 5×105 dNK cells at 37°C . After two hours of contact , organ explants were gently washed with excess of complete media ( 4 washes ) , fixed in 4% paraformaldehyde for 20 min , washed twice in PBS and mounted for two-photon microscopy analysis . Images were taken using Zeiss two-photon microscopy at 900 nm laser excitation . Fluorescence emission was collected using dichroic mirrors to split fluorescence into three channels ( blue , green and red ) . Z stacks were taken at 10 µm slice intervals using Zeiss Zen software . Imaris software was used to analyze the acquired data . HCMV+ whole placental biopsies were obtained from two pathological termination of pregnancy ( 24 . 5 weeks and 25 weeks of pregnancy ) . Tissues were fixed in 10% formalin , embedded in paraffin and processed for IHC as previously described [69] Briefly , 6-µm-thick sections of paraffin-embedded samples were immunostained with an anti-CD56 mAb ( 1B6 clone ) and an anti-HCMV-IE mAb ( Argene ) . Photographs were taken with 40X objective of Leica microscope . Unpaired Student t test was calculated using GraphPad Prism 4 . 0 ( GraphPad Software ) . Unless otherwise indicated , data represent the mean ± S . D . from at least three independent experiments . CD69 ( NM_001781 ) , NKp30/NCR3 ( NM_001145466 ) , NKp44/NCR2 ( NM_001199509 ) , NKp46/NCR1 ( NM_001145457 ) , NKG2D/KLRK1 ( NM_007360 ) , KIR2DL1/CD158A ( NM_014218 ) , KIR2DL2/CD158B1 ( NM_014219 ) , KIR2DL3/CD158B2 ( NM_015868 ) , KIR2DL4/CD158D ( NM_001080770 ) , ILT2/LILRB1/CD85j ( NM_001081637 ) , NKG2C/KLRC2 ( NM_002260 ) , HLA-A , -B , -C ( NM_001242758 , NM_005514 , NM_001243042 ) , HLA-E ( NM_005516 ) , HLA-DR ( NM_002124 ) . | Human cytomegalovirus ( HCMV ) is a herpes virus that can establish persisting infection in immunocompetent hosts . HCMV primary infection during pregnancy is devastating; it can result in up to 75% of congenital infections and it is a known cause of fetal death . The immune system and particularly natural killer cells ( NK ) are known to play a key role in the clearance of several viruses in healthy adults . Whether decidual NK cells ( dNK ) , present in the pregnant uterus , have a role during HCMV infection is not known . We analyze changes in dNK cell function and phenotype in the presence of HCMV-infected targets in an autologous setting . We demonstrate the acquisition of cytotoxic profile which is associated with changes in dNK cell receptor repertoire and cytokine production . Finally , we find that dNK cells are able to sense HCMV infection , migrate and infiltrate infected tissues both in tissular organ culture and in situ in infected placenta . Together our results present the first report demonstrating the involvement of dNK cells in controlling HCMV infection . | [
"Abstract",
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] | [
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] | 2013 | Human Cytomegalovirus Infection Elicits New Decidual Natural Killer Cell Effector Functions |
Scrub typhus , a febrile illness of substantial incidence and mortality , is caused by infection with the obligately intracellular bacterium Orientia tsutsugamushi . It is estimated that there are more than one million cases annually transmitted by the parasitic larval stage of trombiculid mites in the Asia-Pacific region . The antigenic and genetic diversity of the multiple strains of O . tsutsugamushi hinders the advancement of laboratory diagnosis , development of long-lasting vaccine-induced protection , and interpretation of clinical infection . Despite the life-threatening severity of the illness in hundreds of thousands of cases annually , 85–93% of patients survive , often without anti-rickettsial treatment . To more completely understand the disease caused by Orientia infection , animal models which closely correlate with the clinical manifestations , target cells , organ involvement , and histopathologic lesions of human cases of scrub typhus should be employed . Previously , our laboratory has extensively characterized two relevant C57BL/6 mouse models using O . tsutsugamushi Karp strain: a route-specific intradermal model of infection and persistence and a hematogenously disseminated dose-dependent lethal model . To complement the lethal model , here we illustrate a sublethal model in the same mouse strain using the O . tsutsugamushi Gilliam strain , which resulted in dose-dependent severity of illness , weight loss , and systemic dissemination to endothelial cells of the microcirculation and mononuclear phagocytic cells . Histopathologic lesions included expansion of the pulmonary interstitium by inflammatory cell infiltrates and multifocal hepatic lesions with mononuclear cellular infiltrates , renal interstitial lymphohistiocytic inflammation , mild meningoencephalitis , and characteristic typhus nodules . These models parallel characteristics of human cases of scrub typhus , and will be used in concert to understand differences in severity which lead to lethality or host control of the infection and to address the explanation for short duration of heterologous immunity in Orientia infection .
Scrub typhus is a potentially fatal febrile illness caused by infection with the obligately intracellular bacterium Orientia tsutsugamushi . The geographic range of confirmed cases includes Asia , islands of the Pacific and Indian Ocean , and northern Australia; areas home to more than one-third of the world’s population [1] . Moreover , growing evidence implicates a range of Orientia infection outside of the known endemic region , including a case transmitted in the United Arab Emirates , serological and molecular data from Africa and South America and molecular evidence which has suggested Orientia species are present in Europe [2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10] . Individuals are infected with the bacteria transmitted to humans during feeding by infected larval trombiculid mites . Foci of transmission correspond to the distribution of the chigger mite vectors whose habitat consists of secondary or transitional forms of vegetation that exist after environmental modification such as removal of primary forests , practice of shifting cultivation , abandonment of fields , plantations and village sites during conflict , and neglect of urban and suburban garden plots [11 , 12 , 13 , 14] . The prospect of increasing vector habitat and the wide geographic distribution stress the importance and widespread impact of this disease , emphasizing the need for an effective vaccine . Scrub typhus presents one to two weeks after exposure with a not-always-observed bite-site eschar and regional lymphadenopathy , followed by fever and rash accompanied by non-specific flu-like symptoms , requiring empirical treatment based on presumptive etiology . If prompt and appropriate antibiotic therapy is not administered , multi-organ failure and death can follow [15 , 16 , 17 , 18 , 19 , 20 , 21 , 22] . Fatal scrub typhus is characterized by disseminated endothelial infection , diffuse interstitial pneumonia , hepatic lesions , acute renal failure , and meningoencephalitis [23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31] . In scrub typhus autopsy or eschar samples , Orientia have been observed intracellularly in endothelial cells , macrophages , dendritic cells , and cardiac myocytes [24 , 28 , 32] . Understanding the systemic immune and pathophysiological mechanisms of scrub typhus in humans early in the course or in non-fatal cases is limited by sample size , diagnostic acuity , and invasiveness of sampling . Employing an appropriate animal model , which produces disease severity , pathology and systemic endothelial infection resembling human infection , may be used to overcome this impediment to understanding scrub typhus disease progression and the host immune mechanisms necessary for effective vaccine development . The adaptive immune response against O . tsutsugamushi is not well characterized , is short-lived , complicated by strain diversity , and does not afford sterile protection . Studies of naturally acquired O . tsutsugamushi and vaccine studies in humans using live organisms have provided evidence of strain-specific protection , persistent infection with the immunizing strain , short-lived heterologous immunity , and simultaneous infection with multiple strains of Orientia [33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43] . Numerous animal models have shown that inoculation with live- , fixed- , or replication-deficient-O . tsutsugamushi have afforded partial , time-dependent protection against the homologous strain and poor , waning protection against heterologous strains of O . tsutsugamushi [44 , 45 , 46 , 47 , 48 , 49 , 50 , 51] . The mouse models used for these protection studies have employed intraperitoneal challenge , resulting in severe and lethal peritoneal infection and inflammation , which are not a characteristic of natural infection [52 , 53 , 54 , 55] . Although the data from previous murine studies confer valuable information , conclusions about the events of long-term , cell-mediated immunity against O . tsutsugamushi still need to be elucidated from more appropriate animal models . This well-characterized model of O . tsutsugamushi Gilliam strain infection is a necessary addition to the murine model repertoire for future studies of immunity to scrub typhus . Herein , we report the dose- and route-specific kinetics of bacterial dissemination and disease progression of a model of sublethal scrub typhus utilizing the O . tsutsugamushi Gilliam strain . Recent advances to utilizing inbred murine models with more relevant routes and characterization of the pathogenic features of human scrub typhus have been achieved using the Karp strain [55 , 56 , 57] . This sublethal model of disseminated Gilliam strain infection is a crucial addition to research efforts to understand host-pathogen interactions influencing sublethal versus lethal outcomes and heterologous strain immunity dynamics .
All experiments and procedures were approved by the Institutional Animal Care and Use Committee ( protocol # 1302003 ) of the University of Texas Medical Branch-Galveston . Mice were used according to the guidelines in the Guide for the Care and Use of Laboratory Animals and comply with the USDA Animal Welfare Act ( Public Law 89–544 ) , the Health Research Extension Act of 1985 ( Public Law 99–158 ) , the Public Health Service Policy on Humane Care and Use of Laboratory Animals , and the NAS Guide for the Care and Use of Laboratory Animals ( ISBN-13 ) . L929 and Vero cells ( ATCC , Manassas VA ) were maintained in Dulbecco’s Modified Eagle Medium ( DMEM , Gibco Life Technologies , Grand Island , NY ) supplemented with 5% fetal bovine serum ( FBS , HyClone Laboratories , Logan UT ) and 1% HEPES buffer ( Cellgro , Manassas , VA ) at 37°C with 5% CO2 in a humidified incubator . Orientia tsutsugamushi Gilliam strain ( unknown passage history ) was obtained from the Rickettsial and Ehrlichial Species Collection at the University of Texas Medical Branch . Identification of the strain was confirmed by sequencing of the Orientia 47 kDa gene ( accession number L31933 ) . Orientia was propagated 3 passages in L929 cells from yolk sac seed stock and stored at -80°C in sucrose-phosphate-glutamate ( SPG ) buffer ( 218 mM sucrose , 3 . 8 mM KH2PO4 , 7 . 1 mM K2HPO4 , 4 . 9 mM monosodium L-glutamic acid , pH 7 . 0 ) until further use . An Orientia quantitative viability assay was utilized to enumerate viable Orientia as previously described [56] . Briefly , confluent 6-well plates of Vero cells were inoculated in triplicate with serial 10-fold dilutions of Orientia stocks prepared in Dulbecco’s phosphate buffered saline ( DPBS , Cellgro , Manassas , VA ) . The plates were centrifuged for 5 minutes at 700 x g to enhance oriential contact with cells and incubated for two hours at 34°C with 5% CO2 . After two hours , the wells were triple rinsed with warm DPBS with calcium and magnesium to remove extracellular bacteria . DNA was extracted from each well using a DNeasy Blood and Tissue Kit ( Qiagen , Valencia , CA ) according to the manufacturer’s instructions , and the bacterial load determined by quantitative real-time PCR ( qPCR ) to determine the quantity of viable Orientia that had attached and actively entered Vero cells . The single copy gene for the 56-kDa protein was amplified with primers [OtG729 ( 5′- TCGTGATGTGGGGGTTGATAC-3′ ) and OtG873 ( 5′- ATTCTGAGGATCTGGGACCATATAG-3′ ) ( IDT , Coralville , IA ) ] to determine Orientia copy numbers . qPCR was accomplished using iQ SYBR-green supermix ( Bio-Rad , Hercules CA ) with a Bio-Rad CFX96 thermal cycler according to the protocol: one cycle at 94°C for 5 minutes followed by 40 cycles of two-step amplification at 94°C for 5 seconds and 61 . 8°C for 30 seconds . Serial 10-fold dilutions of a known concentration of a plasmid that contained a single copy of the 56-kDa gene were utilized to produce a standard curve to determine copy numbers . Bacterial loads and dissemination to selected organs were assessed by qPCR . DNA was extracted using a DNeasy Blood and Tissue Kit ( Qiagen , Valencia , CA ) from bead homogenized tissue samples according to the manufacturer’s instructions . Tissues samples were normalized using tissue wet weight and were expressed as the number of O . tsutsugamushi Gilliam strain 56 kDa copies per milligram ( mg ) of tissue . Female C57BL/6 ( B6 ) mice , 6–8 weeks of age , purchased from Harlan Laboratories ( Indianapolis , IN ) were housed in an animal biosafety level 3 facility ( ABSL3 ) under specific pathogen-free conditions . The mice were allowed to acclimate for 7 days prior to experimental use and then were inoculated i . v . by the tail-vein with 3 doses: high ( 7 . 5 x 106 ) , mid ( 7 . 5 x 105 ) , or low ( 7 . 5 x 103 ) or intradermally in the lateral ear with 2 . 5 x 105 O . tsutsugamushi organisms as determined by viability assay and monitored twice daily for signs of illness . For the i . v . infected animals , one group ( N = 5 ) of mice from each dose was sacrificed every three days for a period of fifteen days , and one group ( N = 5 ) of i . d . inoculated mice was sacrificed every six days for a period of 30 days . Mice were necropsied , and their tissues were tested for bacterial burden and prepared for histology . The remaining animals were observed for veterinary-approved signs of illness ( ruffled fur , hunched posture , erythema , lethargy , conjunctivitis , and weight loss ) . All animal experiments were conducted twice . At the designated sacrifice time points , blood samples ( 500 μL ) were collected in K2EDTA-coated BD microtainer tubes ( Becton , Dickinson and Company , Franklin Lakes , NJ ) and blood cell counts performed using a calibrated 950FS HemaVet apparatus ( Drew Scientific , Waterbury CT ) . The blood samples were analyzed using the FS-Pak reagent kit and were measured for the following parameters: white blood cell count ( WBC ) , differential leukocyte ( % ) count , hemoglobin concentration ( HGB ) , hematocrit ( HCT ) , red blood cell count ( RBC ) , mean corpuscular volume ( MCV ) , mean corpuscular hemoglobin ( MCH ) , mean corpuscular hemoglobin concentration ( MCHC ) , red cell distribution width ( RDW ) , platelet count ( PLT ) , and mean platelet volume ( MPV ) . Orientia tsutsugamushi Gilliam strain antigen-coated , acetone-permeabilized 12-well slides were equilibrated to room temperature in phosphate buffered saline ( PBS ) and then blocked in PBS containing 1% bovine serum albumin ( BSA ) and 0 . 01% sodium azide for 10 minutes at room temperature . Sera were diluted 2-fold starting at 1:64 and , if reactive , extended to final end-point titers in a solution of PBS containing 1% BSA , 0 . 1% Tween 20 , and 0 . 01% sodium azide . Dilutions of sera were added to individual antigen-coated wells and incubated at 37°C for 30 minutes in a humidified chamber . Slides were rinsed and washed twice for 10 minutes with PBS containing 0 . 1% Tween-20 . Secondary antibody , either DyLight 488-conjugated anti-mouse IgG ( 1:15000 ) , Fluorescein ( FITC ) -conjugated AffiniPure anti-mouse IgG Fcγ subclass 1specific ( 1:600 ) , FITC-conjugated AffiniPure anti-mouse IgG , Fcγ subclass 2c specific ( 1:1000 , Jackson Immunoresearch , West Grove , PA ) or FITC-conjugated anti-mouse IgM antibody , mu chain specific ( 1:500 , Vector Labs , Burlingame , CA ) , was incubated for 30 minutes at 37°C in a humidified chamber . Slides were subsequently rinsed and washed twice as before with the final wash containing 1% Evans blue solution , mounted with DAPI fluoromount-G ( SouthernBiotech , Birmingham , AL ) and coverslipped . Slides were observed under a fluorescence microscope at 400X magnification ( Olympus Scientific , Waltham , MA ) . Serum was unavailable for one mouse from the i . d . route group on 18 dpi ( n = 4 ) , otherwise n = 5 for all groups and time points . Mice that had a positive IFA result at a 1:64 dilution were considered to have seroconverted , whereas mice with non-reactive serum at this titer were assigned a value of zero . Tissue samples were fixed in 10% neutral buffered formalin ( NBF ) and embedded in paraffin . Tissue sections ( 5 μm thickness ) were stained with hematoxylin and eosin or processed for immunohistochemistry ( IHC ) . For IHC , all reagents were from Vector Laboratories ( Burlingame , CA ) unless specified otherwise . Slides were deparaffinized , rehydrated and processed . Antigen retrieval was performed by incubation in 1x citrate buffer ( Labvision , Fremont , CA ) . Sections were sequentially blocked with 1x casein , BLOXALL blocking solution , avidin and biotin solution and 5% normal goat serum . Sections were incubated with polyclonal rabbit anti-O . tsutsugamushi antibody ( 1:12000 , produced in-house ) at 4°C overnight , followed by incubation with biotinylated anti-rabbit IgG ( 1:200 ) for 30 minutes . Signals were developed with Vector Red Alkaline Phosphatase substrate kit . Slides were counterstained with hematoxylin , dehydrated , mounted and cover slipped with VectaMount and examined with an Olympus BX51 microscope ( Olympus Scientific , Waltham , MA ) . Sections were examined to assess the histopathology and establish grading scales . The slides were then examined blindly , without knowledge of dpi or bacterial loads , and scores were determined independently based on the grading systems described below . The grading scale for the lung histopathology was based on the spectrum of lesions throughout the entire course of infection ( S1 Fig ) . Grade 1 was defined as scattered inflammatory cells in focal areas of pulmonary parenchyma and around bronchovascular bundles . A score of 2 was assigned to tissues with widening of alveolar septa and inflammatory cell infiltrates present multifocally in the pulmonary parenchyma and around bronchovascular bundles . Grade 3 was defined as similar to grade 2 but present more diffusely in the pulmonary parenchyma and around bronchovascular bundles and Grade 4 was assigned to tissues presenting with the description of Grade 3 plus areas of atelectasis . The diameters of hepatic clusters of inflammatory infiltrates were measured , and the average lesion size and number of lesions per four typical ( 100X ) fields of liver were determined for each time point . The liver inflammatory index was calculated as number of lesions per four medium-power fields ( MPF ) multiplied by the mean diameter ( μm ) of mononuclear cellular infiltrative clusters . Quantitative assessment of the renal histopathology was based on the extent of mononuclear cellular infiltrate . Digital images of four to six randomly selected medium power fields ( 100X ) of the renal cortex were captured using Olympus DP controller software . Semi-automated counting was performed using ImageJ ( National Institutes of Health , Bethesda , MD , USA ) after converting the image to 8-bit grayscale . Cells contributing to the total mononuclear cell count were identified using a manual threshold and pixel-based area measurement . The number of pixels was counted and presented as a proportion of the total number of pixels in the area under analysis . Values are reported as mean ± standard deviation ( SD ) . The data were analyzed using an one-way ANOVA with Tukey’s multiple comparison as post-hoc analysis ( GraphPad Prism , San Diego , CA ) at a statistical significance level of * , p < 0 . 05; ** , p < 0 . 01 , *** , p < 0 . 001 .
The dose responses of C57BL/6 mice to intravenous inoculation with O . tsutsugamushi Gilliam strain were observed as differences in incubation period prior to onset , duration of illness , magnitude of signs of illness and percent body weight loss . Mice infected intravenously ( i . v . ) with a high dose of O . tsutsugamushi Gilliam strain developed decreased activity ( 7–12 dpi ) , ruffled fur and erythema ( 6–12 dpi ) , labored breathing ( 9–11 dpi ) , conjunctivitis ( 11–12 dpi ) and began to lose weight at day 8 pi , with a nadir mean percent body weight ( 16% loss ) by day 12 pi ( Fig 1A ) . The animals inoculated i . v . with the mid-dose developed signs of illness including decreased activity , ruffled fur labored , breathing and skin erythema on 10–12 dpi followed by weight loss delayed by 3 days with mean nadir percent body weight loss ( 11% ) observed at 13 dpi . The mean weights for the group receiving the low dose i . v . did not decrease below the mean starting weight; however , it was below the mean weight of the uninfected controls at 14 dpi followed by perceptible labored breathing at 15 dpi . After intradermal inoculation , mice were monitored through 30 dpi , during which mean percent body weight did not deviate below uninfected controls . However , decreased activity was observed from 12–16 dpi , ruffled fur during 13–18 dpi , and conjunctivitis on 13 dpi . Onset and duration of splenomegaly , significant increase of whole spleen wet weight above uninfected controls , was observed in a dose- and route-dependent manner . Splenomegaly was observed in mice inoculated i . v . with the high dose from 9 dpi until the end of study of these animals on day 15 , in mice inoculated i . v . with the mid-dose from 12 dpi to the end of the observations on day 15 , and in mice inoculated i . v . with the low dose at 15 dpi ( Fig 1B ) . Intradermal inoculation resulted in splenomegaly during 12–24 dpi , with the peak at 18 dpi comparable to that of high dose i . v . inoculation . Peripheral blood parameters of infected mice were compared to uninfected controls and veterinary accepted normal ranges for mice . While circulating lymphocyte counts remained within the normal range , the mean lymphocyte count for mice infected with high dose i . v . was elevated above the mean of uninfected controls ( 1 . 74 K/μL ) at 12–15 dpi ( 3 . 09–4 . 37 K/μL ) and was elevated on 15 dpi ( 4 . 01 K/μL ) for i . v . mid-dose ( Fig 2A ) . A steady mean increase in absolute neutrophil concentrations was observed during 9–15 dpi for high dose i . v . infected mice , reaching statistical significance by 15 dpi ( Fig 2B ) . A significant increase in neutrophil concentration was also observed at 15 dpi in mice infected with the mid-dose i . v . Less consistent elevation of neutrophils occurred after low dose i . v . inoculation or after i . d . inoculation . Decreases in hematocrit and platelet count below the normal range were observed regardless of dose or route of inoculation ( Fig 2C and 2D ) . Seroconversion was first observed at day 3 after high dose ( Fig 3A ) i . v . inoculation with 80% and 40% ( n = 5 ) having IgM and IgG anti-O . tsutsugamushi antibody reactivity , respectively , followed by 100% IgM and IgG seroconversion on 6 ( Table 1 , Fig 3A ) . Mid-dose i . v . infection induced 80% IgM and 40% IgG seroconversion by 3 dpi followed by 100% seroconversion at 6 dpi and 9 dpi for IgM and IgG , respectively ( Table 1 , Fig 3B ) . The mice which received low dose i . v . inoculation ( Fig 3C ) had seroconversion of 40% IgM at 3 dpi , followed by 40% of animals seroreactive for IgM and IgG at 6 dpi , and while 100% had IgM antibodies , only 80% had IgG seroconversion by 15 dpi , the last experimental time point ( Table 1 , Fig 3C ) . Animals infected by intradermal inoculation had IgM seroconversion in 80% of mice and IgG seroconversion in 40% of mice at 6 dpi with all mice seroconverted by 18 dpi and a continual increase in antibody titer through the final time point , 30 dpi ( Table 1 , Fig 3D ) . At the final experimental time point , the reciprocal endpoint titer of IgG2c antibodies was higher than IgG1 for high dose i . v . ( medians 2048 vs . 512 ) , mid-dose i . v . ( medians 4096 vs . 1024 ) , low dose i . v . ( medians 1024 vs . 0 ) , and i . d . route ( medians 16384 vs . 512 ) ( Fig 3E and 3F ) . Intravenous inoculation resulted in dose-dependent , self-limited systemic infection . The earliest peak of bacterial burden after i . v . inoculation was observed in the spleen on 6 dpi after high dose , 9 dpi after mid-dose , and from 12–15 dpi after low dose inoculation ( Fig 4A ) . Of the tissues examined , the lungs had the highest bacterial load starting at 3 dpi and reached a peak at 9 dpi for the high dose i . v . group ( Fig 4B ) , which was the day of onset of weight loss ( Fig 1A ) . The peak of bacterial load for the i . v . mid-dose recipient mice was observed later , at 12 dpi ( Fig 4B ) . In contrast , a sustained peak was observed at 12 and 15 dpi in the i . v . low dose inoculated mice . The same trend was observed for hepatic bacterial loads but with a lower bacterial load per milligram of tissue ( Fig 4C ) . Intravenous inoculation resulted in higher bacterial loads in the kidney than i . d . inoculation ( Fig 4D ) . A steady increase of bacterial loads was observed in the kidney after i . v . high and mid-dose inoculation , reaching a sustained peak at 9–15 dpi with the high dose and a peak at 12 dpi with the mid-dose . Renal bacterial loads after i . v . low dose inoculation were detected inconsistently . The histopathology of the liver in this model was characterized by multifocal oriential vascular infection and persistent multifocal lesions typified by multifocal lymphohistiocytic and polymorphonuclear cellular infiltrates , vasculitis , and dose-dependent portal triaditis ( S2C Fig ) . Initially , the magnitude of lymphohistiocytic cellular infiltration coincided with increased bacterial loads; however , it continued to intensify as the bacterial loads were controlled . Intravenous high dose inoculation resulted in periportal lymphohistiocytic infiltrates at 3 dpi and focal lesions in the sinusoids at 6 dpi . The lobular lesions were more numerous and encompassed greater tissue area by 6 dpi , as indicated by the increased liver inflammatory index , and were unresolved at the experimental endpoint of 15 dpi ( Fig 5A ) . The peak liver inflammatory index occurred at 12–15 dpi for mid and low dose i . v . route ( Fig 5B and 5C ) . Following i . d . inoculation , the peak liver inflammatory index occurred at 12–18 dpi , and although the lesions decreased in diameter and quantity , they were still present at the experimental endpoint of 30 dpi ( Fig 5D ) . Nephritis , characterized by interstitial cellular infiltrates in the renal cortical parenchyma , which were localized between renal tubules and commonly at the corticomedullary boundary , was observed in the kidneys of mice inoculated with O . tsutsugamushi Gilliam strain regardless of route or dose of inoculum ( Fig 6A ) . The kidney inflammatory ratio continued to intensify through the experimental endpoint , 15 dpi after i . v . high and mid-dose inoculation ( Fig 6B ) . After i . v . low dose inoculation , the kidney inflammatory ratio plateaued at 6 dpi and remained elevated through 15 dpi . The peak of the kidney inflammatory ratio after i . d . inoculation occurred at 18 dpi and was unresolved at day 30 pi . Lung pathology during O . tsutsugamushi Gilliam strain infection progressed throughout the time course observed , and the onset of significant pathology scores correlated with the route of inoculation . Mild , isolated peribronchovascular inflammation was observed at 3 dpi in i . v . high dose infected mice followed by widening of the alveolar septa by 6 dpi . Beginning at 9 dpi patchy frank interstitial cellular inflammation was observed with mild vasculitis . At subsequent time points after high dose i . v . inoculation , from 9 dpi through day 15 pi , peribronchial infiltrates continued to be evident , and the interstitial inflammation was more diffuse and encompassed larger portions of the tissue . After mid-dose inoculation , the peak of pulmonary inflammation occurred at 9–15 dpi and was significantly elevated , and greater than after i . v . low dose inoculation at those time points . Pulmonary inflammation after i . d . inoculation was statistically significant at 12 dpi with the peak lung pathology score observed at 18–30 dpi ( Fig 6C ) . Mild myocarditis was observed after infection ( S2A Fig ) . Mononuclear cellular infiltrate was observed in the pericardium and between cardiac myocytes . Mild meningoencephalitis and characteristic typhus nodules ( clusters of perivascular microglial cells , macrophages and lymphocytes ) were observed in mice inoculated both i . v . and i . d . ( Fig 7D , S2D Fig ) . Expansion of the splenic marginal zone and lymphoid activation in periarteriolar lymphoid sheaths were observed after Gilliam strain inoculation ( S2B Fig ) . Orientia identified by IHC on 9 dpi , the peak of bacterial burden after high dose i . v . inoculation , was located in endothelial cells of the alveolar capillaries ( Fig 7A ) , and in splenic and hepatic mononuclear phagocytes ( Fig 7B and 7C ) . Orientia antigen was also observed in the endothelial cells of cardiac microcapillaries between bundles of cardiac myocytes . At the peak of bacterial load after i . d . inoculation at 18 dpi , Orientia antigen was identified in pulmonary and renal endothelial cells as well as in characteristic typhus nodules ( Fig 7D ) .
Several mouse models of O . tsutsugamushi infection have been developed , but few are extensively characterized in an inbred mouse model to allow for elucidation of the mechanisms of pathogenesis and immunity correlating with human infection [58 , 59 , 60] . The use of inbred C57BL/6 mice to characterize O . tsutsugamushi infection will allow for consistent animal-to-animal responses required for the further testing of hypotheses and vaccine evaluation to a level of statistical significance necessary for interpretation of results . Utilizing the abundance of conditional and gene knockout mice available on this background will enable elucidation of the mechanisms of immunity to O . tsutsugamushi . Studies exploring inbred mouse susceptibility to O . tsutsugamushi Gilliam strain have reported resistance of C57BL/6 and C57BL/6J to high inoculum i . p . challenges [61 , 62] . We have characterized , in more detail , scrub typhus animal models employing the more relevant i . v . and i . d . routes of inoculation of O . tsutsugamushi Gilliam strain in C57BL/6 mice with hematogenous dissemination to pulmonary and systemic endothelial cells of the microcirculation , which results in disseminated self-limited disease mimicking the mite-transmitted infection in many persons . This mouse strain , in contrast , succumbs to challenge with O . tsutsugamushi Karp strain inoculated i . v . and i . p [55 , 61] . Lethal and sublethal endothelial cell target models using i . v . inoculation or i . d . inoculation , respectively , of O . tsutsugamushi Karp strain in C57BL/6 mouse strain have already been extensively characterized [55 , 56] . These well characterized scrub typhus animal models with divergent lethality utilizing the same inbred mouse strain may be employed for studies on virulence mechanisms of the bacterial strains and the protective host immune mechanisms . Although more than 70 strains of O . tsutsugamushi genetic variants have been identified , strains genetically related to the Karp and Gilliam strains have been reported with overlapping geographical distribution and are implicated as prevalent types causing scrub typhus illness , important considerations for designing experiments relevant to human disease and immunity [63] . To model scrub typhus , it would be ideal to study bacterial dissemination characteristics and host-pathogen interactions after natural chigger-bite infection . However , limited access to the infected Leptotrombidium vector colonies , the burden of colony maintenance , and inability to standardize the dose of bacterial transmission by the vector favors the utility of needle-inoculated animal models . Intradermal inoculation is the needle inoculation route which most closely mimics the natural vector transmission during cutaneous feeding . The i . v . route of infection results in a hematogenously disseminated systemic infection as occurs in human scrub typhus , but bypasses the events of early cutaneous infection and the initial spreading steps . The kinetics of infection following the i . v . and i . d . administration routes allows for optimized experimental design focusing on critical disease course time points . In contrast to what has been reported after subcutaneous footpad inoculation of O . tsutsugamushi Karp strain , characteristics of infection of i . v . and i . d . infection in these models including bacteremia , target organs and infected cell types are analogous with only varying kinetics [57 , 64] . In these models , we characterized the clinical signs including splenomegaly and the degree of weight loss , as well as kinetics of bacterial spread . We also analyzed the hematologic response to infection , rate of IgM and IgG antibody production , and antibody isotype . The histopathologic events in response to Gilliam strain challenge included development of interstitial pneumonitis , interstitial nephritis , mild meningoencephalitis , cerebral typhus nodules , and perivascular lymphohistiocytic inflammation in the lung , liver , heart , and kidney . Statistically significant lung inflammation was observed concurrently irrespective of dose or route and was sustained after the peak of infection , suggesting elicitation by a host-mediated inflammatory response . This model recapitulates the pathology that has been observed in humans with scrub typhus , which may be correlated with documented clinical manifestations including labored breathing , pulmonary edema , cardiac dysfunction , hepatomegaly , elevations of serum hepatic aminotransferases , and acute renal failure [22 , 23 , 24 , 28 , 65 , 66] . The cellular tropism demonstrated by antigen location in pulmonary and systemic endothelial cells , splenic mononuclear phagocytes and cardiac myocytes has been described in scrub typhus autopsies [28] . Relevant animal models , including this sublethal mouse model , advance the understanding of scrub typhus disease kinetics and cellular tropism , which cannot be achieved solely from the limited availability of human patient samples from sublethal infections and lethal outcomes . The ability to study various stages of infection confirms observations of multi-organ involvement and systemic endothelial infection during the acute phase in a sublethal model , substantiating the principal features of scrub typhus disease , which are independent of a lethal outcome . These clinical and pathologic features of scrub typhus should be considered for interpretation of experiments exploring immunological and pathogenic mechanisms of the disease . Infection of C57Bl/6 mice with O . tsutsugamushi Gilliam strain , including high , 7 . 5x106 , and mid-dose , 7 . 5x105 , both in excess of the i . v . LD50 of 1 . 25x105 of O . tsutsugamushi Karp strain , results in sublethal disease evidenced by weight loss and replication of the bacteria in target organs [55] . In the i . v . model , onset of weight loss was preceded by the peak in splenic bacterial burden and coincides with the peak of lung bacterial burden , and these observations were dose-dependent . The peak of lung bacterial load after i . d . inoculation of mice reached the same magnitude as that of the i . v . high and mid-dose; however , it occurred nine days later in the infection at 18 dpi . Hematologic responses to infection were characterized by increased circulating lymphocytes and neutrophils as well as thrombocytopenia , which have been described in human clinical disease presentation and progression [18 , 67 , 68] . IgG antibody titers increase substantially during the first two weeks after onset in human cases [69] . In these murine models , detection of IgG seroconversion by IFA was earliest and most consistent in high dose i . v . infected mice; however , at later time points in the i . d . inoculation model , mice had continually increasing titers . The rate of seroconversion after low dose i . v . challenge was not as consistent as after the higher doses , with a delayed response reaching 100% IgM reactivity by 12 dpi and only 80% IgG seroconversion by 15 dpi . It is unknown whether the i . v . inoculation would trend the same way as the i . d . route , since only the acute disease was characterized after i . v . inoculation . The antibody response was dominated by the IgG2c isotype , suggesting the importance of Th1 responses in this model . However , IgG1 was detected in all groups except after low dose i . v . infection , which is indicative of Th2 immune response involvement as well . Further experimental time points beyond 15 dpi would need to be assessed after low dose i . v . infection to understand whether a lack of IgG1 reactivity is a difference in immune responses or merely delayed kinetics . We have previously observed a balanced Th1/Th2 response in our sublethal i . d . model of O . tsutsugamushi Karp infection [56] . In contrast , our lethal i . v . model of O . tsutsugamushi Karp revealed impairment of select Th2 related immune response molecules [70] . These studies combined suggest that contributions by Th2 responses improve immune homeostasis and result in sublethal outcomes after Orientia infection . In human cases , Orientia infects endothelial cells , macrophages and cardiac myocytes in disseminated lethal infection and dendritic cells and macrophages in the human mite inoculation site cutaneous eschar [24 , 28 , 29 , 32] . In contrast to models utilizing non-human primates , no eschar was formed after i . d . inoculation in our murine model [71 , 72 , 73] . Location of Orientia antigen in the i . v . Gilliam model recapitulates many hallmarks of the human scrub typhus cases . At the peak of bacterial burden after high dose i . v . inoculation , Orientia antigen colocalized with endothelial cells of the pulmonary and cardiac microcapillaries , and in splenic and hepatic mononuclear phagocytes . At 18 dpi , the peak of bacterial load after i . d . inoculation , Orientia antigen was observed in pulmonary and renal endothelial cells and colocalized with typhus nodules in the brain . The distribution of Orientia is directly influenced by the route of inoculation . It has been shown that intramuscular and subcutaneous inoculation results in a disseminated infection involving Kupffer cells and macrophages [57 , 58] . Intracerebral inoculation and intraperitoneal inoculation have been shown to result in infections initially limited to the central nervous system and peritoneal lining , respectively [55 , 58] . Dissemination characteristics and the subsequent target cells are relevant to accurately interpret immunologic conclusions , and therefore route is an important consideration in experimental design . In summary , we present the first comprehensive murine model of consistently sublethal scrub typhus in C57/BL6 mice utilizing the O . tsutsugamushi Gilliam strain . Infection with O . tsutsugamushi Gilliam in this model results in dose- and route-dependent kinetics , perceptible clinical signs , and measurable histopathologic lesions , and recapitulates human scrub typhus . These models can be utilized to elucidate the progression and pathogenesis of the majority of scrub typhus cases , which result in untreated non-lethal outcomes [74] . Although the magnitude and persistence of lymphohistiocytic infiltrates during this sublethal infection were unforeseen , we hypothesize that this is indicative of a robust immune response and its capacity to control the infection . This feature of these models highlights the necessity to study the host immune regulation involved in sublethal infection and how it differs from the dysregulation we have reported in the lethal model utilizing O . tsutsugamushi Karp strain [70] . We will focus future studies on the contributions of immune cell subsets to protection to establish the immunologic foundation necessary for development of an effective vaccine . This model complements the available lethal murine model of scrub typhus and will allow for in-depth mechanistic studies related to cross-protection , lethality , and pathogenesis . | Scrub typhus is an acute febrile illness with considerable mortality , and no available vaccine , caused by the obligately intracellular bacterium , Orientia tsutsugamushi . Despite the life-threatening severity of the illness in approximately one million cases annually , 85–93% of patients survive . The lack of appropriate animal models of scrub typhus has left a void in the fundamental knowledge necessary to develop a vaccine , such as mechanisms which contribute to disease severity and immunity . Here , we report a sublethal inbred murine model for scrub typhus using the intradermal and intravenous routes of inoculation , which are comparable to the natural route of chigger-bite transmission and subsequent hematogenous spread . This model , infection of mice with O . tsutsugamushi Gilliam strain , can be employed in conjunction with the lethal model of O . tsutsugamushi Karp strain to perform in-depth mechanistic studies related to strain cross-protection , lethality , pathogenesis and specific immunological investigations of the host immune response . | [
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"bacterial... | 2017 | Murine models of scrub typhus associated with host control of Orientia tsutsugamushi infection |
Measuring the impact of capacity strengthening support is a priority for the international development community . Several frameworks exist for monitoring and evaluating funding results and modalities . Based on its long history of support , we report on the impact of individual and institutional capacity strengthening programmes conducted by the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases ( TDR ) and on the factors that influenced the outcome of its Research Capacity Strengthening ( RCS ) activities . A mix of qualitative and quantitative methods ( questionnaires and in-depth interviews ) was applied to a selected group of 128 individual and 20 institutional capacity development grant recipients that completed their training/projects between 2000 and 2008 . A semi-structured interview was also conducted on site with scientists from four institutions . Most of the grantees , both individual and institutional , reported beneficial results from the grant . However , glaring inequities stemming from gender imbalances and a language bias towards English were identified . The study showed that skills improvement through training contributed to better formulation of research proposals , but not necessarily to improved project implementation or communication of results . Appreciation of the institutional grants' impact varied among recipient countries . The least developed countries saw the programmes as essential for supporting basic infrastructure and activities . Advanced developing countries perceived the research grants as complementary to available resources , and particularly suitable for junior researchers who were not yet able to compete for major international grants . The study highlights the need for a more equitable process to improve the effectiveness of health research capacity strengthening activities . Support should be tailored to the existing research capacity in disease endemic countries and should focus on strengthening national health research systems , particularly in the least developing countries . The engagement of stakeholders at country level would facilitate the design of more specific and comprehensive strategies based on local needs .
Health research capacity is unanimously recognized as contributing to the overall development of low-and middle-income countries and is a critical precondition for achieving the Millennium Development Goals [1] , [2] . Research capacity strengthening ( RCS ) is defined as “the process by which individuals , organizations and societies develop ability ( individually and collectively ) to perform functions effectively , efficiently and in a sustainable manner to define objectives and priorities , build sustainable institutions and bring solutions to key national problems” [3] . Health research capacity strengthening programmes have been identified as a driver for the support of international development agencies [4] . Although these programmes created a large number of well-trained health researchers and institutions , and despite the remarkable progress made by some low- and middle-income countries in engaging in their own capacity building , health research capacity strengthening remains a challenge , particularly in sub-Saharan Africa [5] . This can be attributed to the limited ability of development agencies to identify , target and influence necessary factors that lead to an effective , efficient and relevant RCS programme in health , despite the availability of several frameworks for monitoring and evaluating RCS results and modalities of funding [4] , [6]–[9] . Indeed , evaluating health RCS initiatives is quite complex , since achieving the objectives could take several years ( often more than 10 years ) . However , evaluation is necessary to provide information to justify the ( dis ) continuation of programmes and to highlight the areas that need improvement [4] , [10] . Among the organizations with extensive RCS experience is the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases ( TDR ) . Created in 1975 to support the research and capacity building in the fight against infectious diseases of the poor , TDR goal is to improve health and remove diseases as barriers to social and economic development . For more than 30 years , TDR has built health research capacities in developing countries by supporting individuals' education and training through fellowships or scholarships; implementing learning by doing programmes for specific skills; employing mentorship programmes to complement academic programmes; establishing national and international training and research centres of excellence; and developing networks and collaborative research projects . Regular reviews of its research capacity strengthening programmes have led TDR to reorient its strategy as required: shifting focus from institutional strengthening in the 80 s to human resources strengthening in the 90 s [11] and identifying the need to move beyond the idea of RCS as being primarily related to individual researchers to a more demand driven model of national health research systems [12] . TDR models of capacity building and particularly the merit of short-term trainings in social sciences have also been evaluated [13] but still , there has been no systematic and comprehensive data of the lessons learnt and benefits of the different TDR RCS approaches i . e . individual and institutional . Thus , we conducted an evaluation of TDR's contribution to career strengthening of a selected group of individuals and institutional capacity development grantees with a record of project completion between 2000–2008 . The main objective was to identify factors that positively influenced and improved the research capacity and career development of grant recipients for identifying opportunities that are of broader relevance to the objectives and goals of international development and aid agencies .
Between 2000 and 2008 , TDR supported 128 individual grants -including 88 research training grants ( RTG ) , 40 re-entry grants ( REG ) and 20 institution strengthening grants ( ISGs ) that were completed during the same period . RTGs are awarded to individuals in developing countries to pursue studies leading to acquisition of a postgraduate degree ( MSc or PhD ) at institutions in their home countries , or in other developing or developed countries . REGs are intended to facilitate the career development of young scientists returning to their home institutions within 24 months following completion of a graduate degree ( MSc or PhD ) or a post doctoral fellowship . ISGs are designed to provide up to three years support to an institution or research group to improve infrastructure and the research environment . Activities may include improving training opportunities , advancing scientific expertise in biomedical and social sciences , establishing/improving information and communication systems , and fostering opportunities for scientific collaboration . Information on all individuals and institutions that received TDR grants between 2000 and 2008 was extracted from TDR internal database and tabulated for range and scope of research topics . A mix of quantitative and qualitative methods was applied during data collection and analysis to capitalize on the advantages and minimize the limitations of each approach . The assessment consisted of three standardized questionnaires sent by e-mail to those individuals that completed their project within the 2000–2008 period . The first questionnaire was sent to recipients of research training grants to assess their career progression , the skills acquired during their training , and the impact of the training on the research capacity of their home institution . A second questionnaire was sent to individuals who received a re-entry grant to assess the performance of their research group and the impact of their research on the development of their institutions . A third questionnaire was sent to the principal investigator of institutions that received TDR grants to assess the impact of the grants on institutional performance . Individual questionnaires were designed to obtain information on the current position of each grantee , and assess the research competencies of each individual , both before and after the TDR grant period , for comparison purposes . Additionally , the questionnaires were also designed to self evaluate the following five main indicators prior to , during , and after the grant period: scientific publications; ability to attract additional resources; participation in national and international collaborative activities; human resource development , including staff development and training and provision of research equipment by the home institution . A total of 10 RTG recipients were selected for in-depth interviews . Interviewees were selected so as to achieve gender balance and representation from a variety of research interests and countries of origin . The interviews aimed to collect information about the grantees' perspectives on the factors influencing their careers after the training grants . Opinions on how to improve TDR research capacity strengthening programmes to meet the needs of disease endemic countries and their populations were also collected . Questionnaires for institutions included a self-assessment of the following institutional performance indicators: work space; library; internet and e-mail access; laboratory facilities; purchasing and inventory systems; maintenance and repair facilities and human resources . Taking into account a balance of research topics and regional representation , four institutions were selected for site visits . A semi-structured interview was conducted with the leaders and scientists of each institution . Interviewees were asked about their views on the following issues: the strengths and weaknesses of TDR funded institutions; the contribution of these institutions to formulation of national public health policies; the extent to which these institutions satisfied stakeholders' ongoing requirements for ( access to ) quality goods and services . Data from both individual and institutional questionnaires was analysed using STATA ( version 10 ) , based on a prepared data dictionary . The in-depth interviews were tape-recorded , transcribed and imported into a pre-coded template prepared in Microsoft Word for export to MaxQDA . The main findings are illustrated with selected short narratives .
Analysis of TDR grants by age and sex of recipient; regional and language distribution; and research area is given in Table 1 . The mean age of RTG recipients is 33±4 . 5 years , with women representing 28% of all grantees . Most RTG recipients are from Africa ( 65 . 5% ) , and mostly from Anglophone countries ( 65 . 5% ) . The majority funded projects were on epidemiology and disease control ( 42 . 5% ) , while social sciences represented 15 . 5% of the projects . Of the 88 questionnaires sent to recipients of TDR RTGs , 59 ( 67% ) were returned . Of those that responded , 33 received a PhD , 23 an MSc and three participated in a short training course . The overwhelming majority ( 82% ) of grantees moved abroad for their training with a mean period of 21 months , a minimum of two months and a maximum of 72 months . MSc candidates preferred to study at the University of Witwatersrand in South Africa ( 26% ) ; the London School of Hygiene and Tropical Medicine ( LSHTM ) in the UK ( 12% ) ; or the Liverpool School of Tropical Medicine ( LSTM ) ( 12% ) in the UK . PhD trainees moved to LSHTM ( 8 . 9% ) , the LSTM ( 8 . 9% ) , and the Swiss Tropical and Public Health Institute ( formerly Swiss Tropical Institute ) Institute ( 6 . 6% ) in Switzerland . A few ( 6 . 6% ) of PhD grant recipients received their doctoral training at institutions in their country of origin . One grantee's view on moving abroad for training indicates a gender bias as expressed in the following quote: “I do not want to speak of my case as special but it is still a man's world and this is reality . When a man gets an opportunity such as a training grant and has to go abroad he will not think twice he will just get up and go . But a woman’s reality is different meaning that social responsibilities make her not as flexible as her fellow partner . If TDR capacity strengthening programmes do not have this understanding in their philosophy , then most women will have troubles to join such programs” ( Female grantee ) . The mean age of REG recipients was 36±4 . 2 years with women representing 41% of all grantees . REGs were predominantly awarded to scientists from Africa ( 37 . 5% ) and South America ( 38 . 5% ) and English was the most common language used by the grantees ( 38 . 5% ) . Of the 40 questionnaires sent to recipients of TDR REGs , 25 ( 62 . 5% ) were returned . Of those that responded , 60% ( 15/25 ) were based in universities , 32% ( 8/25 ) in research institutes , and 8% ( 2/25 ) in governmental agencies . Most of the ISGs were awarded to Anglophone countries ( 46 . 5% ) and institutions from sub-Saharan Africa ( 49% ) . A Francophone scientist reported that , “language , in my case English language , was an important barrier preventing me and my fellow scientists from applying to competitive international grants , including those from TDR” ( Male grantee ) . The majority of the projects funded focused on epidemiology and disease control ( 35% ) and basic research ( 25% ) . Of the 20 questionnaires sent to ISG recipients , eight were returned; five from research institutes and three from universities . Three categories of research competencies were identified for self-assessment: The level of each research competency was self -assessed and graded on a scale of 1 to 7 ( 1 being poor or no competency and 7 for very good or high competency ) . Most RTG recipients ( 74% ) rated their research competencies as very good ( grade of 6 or 7 ) , and 24% considered themselves to have medium level skills ( grade of 3 to 5 ) . 58% of the grantees rated themselves very good in conducting situational analysis and 90% of grantees cited the ability to write in English as the most important skill acquired during training . Grantees also assessed the attribution of the acquired skills to TDR funding or to the home institution . Results in Figure 1 show that TDR training is perceived to have contributed to better formulation of a research proposal , but not necessarily to an improved ability to conduct a research project and communicate research results to a broad audience . The following statement reinforces this finding: “When TDR provides research training support to grantees , they assume those candidates have the necessary research skills . TDR gives you money to go collect data analyse and write report , but for me that is not enough . The skills that I would consider most important are how to manage the grant itself , how to implement the research activity , analyse data and write the report once the grant has been successfully managed” ( Male grantee ) . The analysis also shows that the percentage of grantees that received a competitive grant after completing a TDR-funded training increased from 20% to 34% . The average number of competitive grants obtained by each individual increased from 1 . 61 to 2 . Of those who had not received a competitive grant prior to TDR-funded training , 42% obtained research grants post training . Similarly , the number of publications increased from 3 fold , with an average of 30 citations in the post-TDR grant period , based on a Medline search of publications and citations of all grantees . The following grantee narrative highlights issues regarding scientific publications: “Before receiving research training support , it was difficult to publish as a candidate does not have what it takes to publish in a peer reviewed journal…publishing is a different world and requires advanced writing skills , confidence to write , and knowledge about the publishing process , which for many scientists in developing countries is missing” ( Male grantee ) . Analysing the responses from REG recipients allowed us to assess the level of satisfaction with the following services: work place; library; internet access; access to online journals; laboratory facilities; purchasing and inventory system; maintenance and repair of facilities and human resources . The responses also allowed for attribution of improved work place amenities and services to TDR training . Figure 2 shows that TDR grants were seen to have the highest impact on the work place , laboratory facilities , human resources and maintenance and repair of the equipment . TDR was however considered to have only a moderate impact on library services , access to internet , and access to online scientific journals . As with RTGs , the analysis of REGs showed that the number of competitive grants increased: 85% of all research groups received grants after completing TDR REG grant compared to 67% previously . Similarly , an average of three grants per group was received post TDR support , compared to two grants per group before . Regarding the benefits to the home institution , 79% of grantees reported that TDR grants facilitated institutional acquisition of durable equipment , especially laboratory equipment . An average of 11 students ( six undergraduate , three MSc and two PhD students ) , 45% of which were women , were trained through TDR grants and 70% of the research groups reported that they trained or employed at least one technician during TDR training . 78% of the respondents mentioned that they were able to retain at least one technician after TDR training . TDR ISGs were reported to have a positive impact on all of the services analysed , with the greatest impact being on laboratory facilities and human resources . All institutions surveyed mentioned that they were able to acquire durable laboratory equipment . The following quote from an ISG beneficiary illustrates on this finding , “TDR contribution to scientific capacity in tropical disease endemic countries is undeniably visible…TDR financed the purchase of the first PCR machine and continues to make huge investments in our lab , enabling subsequent scientific work to be carried out . Without TDR distinctive support , that work would not have taken place and the malaria treatment policy would have been changed based on politics rather than scientific evidence” ( Male grantee ) . An average of 53 scientists were trained during the three year support of the ISG . Of those 53 , 37% ( 20 positions: nine undergraduate , six MSc and five PhD students ) were directly trained with TDR support . Each institution trained an average of nine technicians and retained at least one after TDR training completion . The four institutions selected for site visits were the Centre d'Etudes sur les Ressources Végétales ( CERVE ) in Brazzaville , Congo; the Faculty of Medicine and Health Sciences , University of Sana'a , Yemen; the Centre for Research in Medical Entomology ( CRME ) in Madurai , India; and the Department of Immunology and Biochemistry , Institute of Biological Sciences , Federal University of Minas Gerais , Belo Horizonte , Brazil . The site visits confirmed that ISG had a substantial impact on human resources development and infrastructure improvements at these institutions . However , appreciation of the ISG's impact varied among countries . Institutions in the least developed countries ( Congo and Yemen ) , saw the ISG as essential to maintaining basic infrastructure and activities , since local authorities do not invest much financial resources in research . Advanced developing countries ( India and Brazil ) , perceived ISGs as complementary to resources received from local authorities , and of particular value to young researchers who were not yet in a position to successfully compete for major international grants .
Some conclusions resulting from the present analysis of individual and institutional TDR capacity building programmes between 2000 and 2008 are relevant for improving and further developing the RCS activities of international development and aid agencies . First , RCS funding agencies should develop specific strategy to address some health research inequities such as gender imbalance and bias towards Anglophone countries' support . Indeed , in the present study , a pronounced disequilibrium in gender balance was made evident by the fact that only 28% of RTGs and 41% of REGs were allocated to women . In addition , the mean age of the grantees is more than 30 years , indicating that TDR training support coincides with women's prime years for tending to children and related family responsibilities , especially in low-income countries . The data confirms that family responsibilities , particularly child bearing and rearing together with structural and cultural barriers , impinge on women's access to good scientific training . This finding is consistent with other research that asserts that many women with doctorate degree do not work as scientists due to societal biases and structural factors [14] , [15] . Thus , to address the under-representation of women in RCS programmes and to promote equity , RCS organizations should develop strategies that are sensitive to the specific situations and needs of women and consequently address overrepresentation of men in the distribution of resources and improve overall research capacity in disease endemic countries . Most of the TDR grantees came from Africa ( 65 . 5% ) and most of the grant recipients were Anglophones: 57% of TDR grantees came from English speaking countries , while 20% from French , 10 . 5% from Portuguese , 8 . 5% from Spanish , 2% from Chinese , and 1 . 5% from Arabic speaking countries . Indeed , English is the dominant language in health research and overshadows other languages as a means of communication thereby inadvertently limiting other linguistic communities' access to essential technical information . Poor English language skills hinder wide dissemination of research results by health researchers from disadvantaged populations . As a result , the work of many health researchers in disease endemic countries does not have tangible impact on national , regional or international health research . To overcome the language barriers , health RCS organizations should develop specific strategies , including making appropriate provisions in their grantee selection criteria , to increase the chances of non-Anglophone countries benefitting from their programmes and promoting collaboration between scientists and institutions in more advanced Anglophone countries and their counterparts in less advanced countries . An example is the case of two intergovernmental health organizations , the Francophone Organisation de Coordination et de Coopération pour la Lutte contre les Grandes Endémies ( OCCGE ) and the Anglophone West African Health Community ( AWAC ) that merged in 1998 , creating the West African Health Organisation ( WAHO ) , an organization committed to transcending linguistic borders to serve all fifteen ECOWAS ( Economic Community of West African States ) . ( http://www . wahooas . org/ ) . Secondly , RCS funding agencies should build and maintain a supportive research environment in DECs in which researchers can develop their scientific career and pursue their research . This strategy includes strong grant management unit in institutions , good communication facilities , career structures and demand from policy markers . For many institutions in low- and middle-income countries , maintaining a high quality research environment remains a challenge due to limited resources . The present study shows that TDR grants had a great impact on strengthening institutional infrastructure , particularly through acquisition of laboratory equipment ( Figure 2 ) but access to internet/e-mail and online journals remains limited in most institutions . Investment in the necessary infrastructure for high speed/high quality internet access is beyond of TDR RCS's support capacity . Consequently , easy sharing of research information among researchers , media , policy makers , and other public and private stakeholders remains difficult and , at times , costly . Thus , support for capacity building should extend beyond the individual to the institution , through support for equipment acquisition and refurbishment of essential infrastructure . One objective of the present survey on TDR capacity building was to identify factors that positively influenced research capacity and career development . Developing skills for advocacy , resource generation and allocation , negotiation and consensus building , and financial management were clearly identified as important factors and should be targeted by RCS organizations . Project management skills that are often omitted from academic curricula , have also been identified as critical to develop health research leadership in developing countries . To address this gap , TDR developed a training course on “Effective Project Planning and Evaluation” ( EPPE ) to help scientists plan , implement , monitor , report and evaluate the progress of research projects in a systematic way . To ensure quality and access to a wider audience , TDR selected and supported a number of institutions to administer the EPPE course locally . These institutions are encouraged to develop national and regional support networks for individual researchers and institutions that serve as mechanisms for sharing ideas and resources . The Centro Internacional de Entrenamiento e Investigaciones Médicas ( CIDEIM ) in Cali , Colombia is a good example of how a centre of excellence formed a regional network that supports the TDR developed planning , monitoring and evaluation activities in universities and research institutions throughout Latin America and the Caribbean [16] . Communication to the scientific community through peer reviewed scientific journals is equally important to the career development of a researcher . A scientist's eligibility for grants and funding , and general career advancement is closely related to the number of publications he or she produces . As such , many RCS programmes support activities to enhance writing skills and encourage publications . However , it is clear that representation of researchers from low- and middle-income countries in scientific publications remains low . This may be a reflection of poor representation of these countries on the boards of international journals in tropical medicine [17] . Thus , research-funding agencies should consider providing resources to promote the expertise of authors , reviewers and editors from low- and middle-income countries to promote health research publications from underrepresented populations . Publishing in peer-reviewed journals is not an end in itself , but rather a means of communicating research-generated knowledge which can be translated into health policies , operational guidelines or health products . Translating research results into policy recommendations , concrete interventions or new tools was identified as a major weakness of RCS organizations [18] , [19] . The result obtained in the present assessment of TDR programmes confirms this issue . One reason could be that most of TDR RCS grants are for basic medical sciences and epidemiology . These subjects are upstream in the research and development pipeline and the immediate translation of the research into a product is often not possible . The knowledge generated , however , contributes to a better understanding of epidemiology , systems or biology of vectors . Although some RCS organizations recognize the need to bridge the gap between research and policy , more can be done to promote research uptake i . e . synthesizing research results to show new knowledge production and promoting the use of evidence in decision-making . Decision makers at various levels should also be trained on for evidence-based decision-making [20] . In conclusion , health RCS programmes should maintain and expand their investment in training activities to 1 ) address inequities in health research by taking into account the social and cultural situation of the grantee , 2 ) introduce criteria that encourage and support the development of research careers within DECs and establish networks and 3 ) develop country-specific programmes in collaboration with national authorities to address the specific needs of each country , and identify how best to strengthen national health research systems . | The UNICEF/UNDP/World Bank /WHO Special Programme for Research and Training in Tropical Diseases ( TDR ) has over the 2000–2008 period supported the development of individual and institutional grants . Although the TDR research capacity development programmes has had a substantial impact on the development of tropical disease research and research capacity in disease endemic countries , a review of the lessons learnt and benefits of this approach has never been completed . A study was conducted to analyse TDR's inputs in research capacity in endemic countries and to assist TDR in the improvement of its future activities . An analysis ( by variables of gender , age , language , country of origin , country of studies , type of grant , scientific interest etc ) of the grantees that have benefited from TDR support in terms of their career development and research capacity , including any important financial implications was conducted . The study identify opportunities that are a broader relevance to objectives to international development agencies such as addressing inequities such as the gender imbalance language bias towards English and building a supportive research environment in DECs in which researchers can develop their scientific career and pursue their research . | [
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"public... | 2011 | Impact of Health Research Capacity Strengthening in Low- and Middle-Income Countries: The Case of WHO/TDR Programmes |
Transcriptional regulatory networks are fundamental to how microbes alter gene expression in response to environmental stimuli , thereby playing a critical role in bacterial pathogenesis . However , understanding how bacterial transcriptional regulatory networks function during host-pathogen interaction is limited . Recent studies in group A Streptococcus ( GAS ) suggested that the transcriptional regulator catabolite control protein A ( CcpA ) influences many of the same genes as the control of virulence ( CovRS ) two-component gene regulatory system . To provide new information about the CcpA and CovRS networks , we compared the CcpA and CovR transcriptomes in a serotype M1 GAS strain . The transcript levels of several of the same genes encoding virulence factors and proteins involved in basic metabolic processes were affected in both ΔccpA and ΔcovR isogenic mutant strains . Recombinant CcpA and CovR bound with high-affinity to the promoter regions of several co-regulated genes , including those encoding proteins involved in carbohydrate and amino acid metabolism . Compared to the wild-type parental strain , ΔccpA and ΔcovRΔccpA isogenic mutant strains were significantly less virulent in a mouse myositis model . Inactivation of CcpA and CovR alone and in combination led to significant alterations in the transcript levels of several key GAS virulence factor encoding genes during infection . Importantly , the transcript level alterations in the ΔccpA and ΔcovRΔccpA isogenic mutant strains observed during infection were distinct from those occurring during growth in laboratory medium . These data provide new knowledge regarding the molecular mechanisms by which pathogenic bacteria respond to environmental signals to regulate virulence factor production and basic metabolic processes during infection .
It has long been recognized that the gene expression profile of bacterial pathogens differs significantly during infection compared to the laboratory environment [1] . For example , a recent study of Listeria monocytogenes found that more than 1 , 000 genes were differentially expressed when comparing bacteria grown in a standard laboratory medium with the same bacteria recovered from mouse intestine [2] . Genes encoding bacterial virulence factors are often upregulated during infection , but the molecular mechanisms governing virulence gene expression in the host are only beginning to be understood [3] , [4] . Specifically , there is a dearth of information available regarding how transcriptional regulatory networks function in response to host environmental stimuli to determine virulence factor production [5] . Group A Streptococcus ( GAS ) causes infections in humans ranging from uncomplicated pharyngeal and skin infections to necrotizing fasciitis and toxic shock-like syndrome [6] . The ability of GAS to cause infection in diverse human niches indicates that GAS has evolved precise mechanisms to alter gene expression depending on the distinct challenges posed by particular disease sites [7] , [8] , [9] . Unlike some other pathogenic bacteria , GAS does not appear to regulate virulence factor production by alternative sigma factors that can associate with core RNA polymerase [10] , [11] . Thus , gene expression in GAS is heavily dependent on transcriptional regulatory networks [12] . GAS encodes two main types of regulatory proteins , namely stand-alone regulators and two-component gene regulatory systems ( TCS ) [12] , [13] . The interaction of stand-alone regulators with DNA changes with alterations in intracellular conditions , such as the presence of an inducing substrate [12] . In contrast , TCS typically consist of a membrane-embedded sensor kinase that controls the phosphorylation state of a cognate cytoplasmic response regulator in response to environmental stimuli [14] . The phosphorylation state of the regulator influences its DNA binding activity thereby affecting gene expression [15] . Although interaction between TCS and stand-alone regulators is clearly critical to bacterial pathogenesis , information regarding how independent bacterial regulators coordinate gene expression during infection is lacking . The best studied GAS transcription factor is the control of virulence regulator ( CovR ) , also known as CsrR for capsule synthesis regulator [7] , [16] , [17] , [18] , [19] . CovR is the response regulator of the CovRS TCS [7] . Studies done over the past decade have revealed several intriguing aspects of the CovRS TCS . First , in contrast to most TCS regulators , CovR mainly serves to negatively affect gene expression , including repressing numerous genes encoding key virulence factors [7] , [19] , [20] . Thus , GAS strains in which CovR has been genetically inactivated are hypervirulent for mice [17] , [20] , [21] , [22] . Second , CovR appears to be able to function independently of CovS as GAS strains with isogenic CovR or CovS mutations have different phenotypes [16] , [23] . Third , phosphorylation of CovR generally leads to an increase in affinity for target DNA in vitro although the degree to which phosphorylation increases CovR binding affinity differs for various promoters [24] , [25] . Finally , spontaneous mutations in covR or covS have been identified in strains from animals with experimental GAS infections and humans with invasive infections indicating that mutations in covRS provide an advantage in select in vivo environments [21] , [26] , [27] , [28] . Although most CovRS-related research has focused on virulence factor regulation , the CovR regulon also includes many genes involved in carbohydrate catabolism and nitrogen utilization [20] , [29] . Inasmuch as CovR regulates genes involved in virulence and basic metabolic processes , CovR appears to have a similar transcriptional profile to catabolite control protein A ( CcpA ) [30] , [31] . CcpA is a stand-alone , global regulatory protein critical to selective carbon source utilization and nitrogen metabolism that has recently been shown to contribute to virulence in GAS and other Gram-positive pathogens [30] , [31] , [32] , [33] , [34] , [35] . Studies in Bacillus species and other non-pathogenic Gram-positive bacteria have found that the binding of CcpA to DNA catabolite response element ( cre ) sites is significantly enhanced by interaction of CcpA with histidine containing phosphoprotein ( HPr ) phosphorylated at serine residue 46 ( HPr-Ser46-P ) [36] , [37] . The HPr phosphorylation state is determined by the action of HPr kinase/phosphorylase ( HPrK/P ) , a bifunctional enzyme whose activity , in turn , is affected by the intracellular concentration of carbohydrate catabolism products [38] . Orthologues of CcpA , HPr , and HPrK/P from Bacillus species are present in all fully sequenced Gram-positive pathogens . However , definitive evidence that the CcpA- ( HPr-Ser46-P ) -HPrK/P axis functions in a similar fashion in pathogenic Gram-positive organisms to that observed in Bacillus species is lacking , and there are limited data available regarding how CcpA influences gene expression during infection [39] . Analysis of the CcpA transcriptome in GAS led to the understanding that many genes influenced by CcpA also are part of the CovR transcriptome [30] , [31] . Moreover , bioinformatic analysis suggests that CcpA and CovR DNA binding sites can be proximally located [30] , [31] . Similarly , studies of CcpA and CovR orthologues in Staphylococcus aureus ( in which the CovR orthologue is known as ArlR for autolysis related locus ) and Streptococcus mutans have suggested significant overlap in genes regulated by these two proteins in non-GAS pathogenic bacteria [34] , [40] , [41] , [42] . Therefore , we designed studies to test the hypothesis that the CcpA and CovRS systems co-regulate expression of a diverse array of GAS genes . Our results indicate that CcpA and CovR combine to shape the expression profile of GAS virulence factor-encoding genes and basic metabolic genes during infection . These data provide new insights into how transcriptional regulatory networks contribute to bacterial gene expression in the host environment and extend understanding of the close links between virulence and basic metabolic processes [43] .
Pharyngeal GAS isolates usually have an intact ( wild-type ) CovRS system whereas GAS isolates recovered from invasive infections may have inactivating mutations in either CovR or CovS [21] , [26] , [27] . The two previous genome-wide studies on the effect of CcpA inactivation in GAS have both used the invasive clinical isolate serotype M1 strain MGAS5005 , which encodes a truncated , functionally inactive CovS protein [16] , [26] , [30] , [31] . To test the hypothesis that the effects of CcpA are dependent on CovRS status , we used non-polar insertional mutagenesis to create a ΔccpA isogenic mutant ( strain 2221ΔccpA , Figure S1 , Table 1 ) from the wild-type parental strain MGAS2221 , a fully-sequenced serotype M1 strain that contains an intact CovRS TCS ( Southern blot shown in Figure S1B ) [26] . We genetically complemented the 2221ΔccpA isogenic mutant strain using a CcpA-encoding plasmid that replicates in GAS to make strain comp2221ΔccpA . The growth characteristic of the three strains in a standard laboratory medium ( Todd-Hewitt broth with yeast extract , THY ) were indistinguishable ( Figure S2 ) . We next determined the transcript level of the gene encoding streptococcal pyrogenic exotoxin B ( SpeB ) in the CovRS wild-type strains MGAS2221 , 2221ΔccpA , and comp2221ΔccpA and in the CovS mutated strains MGAS5005 , 5005ΔccpA , and comp5005ΔccpA . speB encodes a broad-spectrum , extracellular cysteine protease that is a key GAS virulence factor [44] . We analyzed speB because it is negatively regulated by CovR in serotype M1 strains , and a putative cre site is located approximately 100 bps into the speB open reading frame suggesting it could be directly regulated by CcpA [20] ( Figure S3 ) . During growth in THY , there was no significant difference in speB transcript level between strain MGAS5005 and its CcpA inactivated isogenic mutant strain ( Figure 1A ) . In contrast , speB transcript level was significantly increased at the stationary phase of growth in strains MGAS2221 and comp2221ΔccpA compared to strain 2221ΔccpA ( Figure 1A ) . Western immunoblot analysis ( Figure 1B ) and casein hydrolysis assays ( Figure 1C ) demonstrated that the observed transcript level variances translated into differences in immunoreactive SpeB in culture supernatants and in functional SpeB activity . These data demonstrate that , under the conditions tested , CcpA positively contributed to speB expression in the presence of a functional CovRS TCS but did not affect speB expression when CovS was inactive . To determine if CovRS status affected the influence of CcpA on GAS virulence factors other than SpeB , we performed similar experiments to those described for SpeB using streptolysin O ( Slo ) , an actively secreted cytotoxin [45] and Streptococcus pyogenes cell envelope proteinase ( SpyCEP ) , an IL-8 degrading enzyme [46] . Putative cre sites are located in the slo promoter , which is co-transcribed with the nga gene ( herein labeled nga/slo cre ) , and in the early spyCEP intragenic region ( Figure S3 ) , and CovR negatively regulates nga/slo and spyCEP in serotype M1 strains [16] , [20] . As observed for SpeB , CcpA influenced the level of slo and spyCEP transcript in strain MGAS2221 but not strain MGAS5005 ( Figure 1D , 1E ) . In contrast to our findings with speB , slo , and spyCEP , the transcript level of sagA , which encodes the first gene of the operon encoding the potent cytolysin streptolysin S ( SLS ) , was significantly increased by CcpA inactivation in strain MGAS2221 as well as in strain MGAS5005 ( Figure 1F ) . We and others have previously demonstrated binding of CcpA to the sagA promoter [30] , [31] . Taken together , we conclude that the effect of CcpA on the expression of several key GAS virulence encoding genes varies depending on the functional CovRS status of the parental GAS strain . We next tested the hypothesis that CcpA specifically binds to the putative speB , nga/slo , and spyCEP cre sites . For these studies , recombinant GAS CcpA , HPr , and HPrK/P were overexpressed and purified , and HPrK/P was used to produce HPr-Ser46-P as described in Materials and Methods ( Figure 2 ) . CcpA alone bound with high affinity to the speB cre site with a Kd of 100 nM ( Figure 3A ) . As expected for a CcpA DNA binding site , the addition of HPr-Ser46-P significantly increased the affinity of CcpA for the speB cre site to a Kd of 5 nM ( Figure 3A ) . Conversely , analysis of CcpA- ( HPr-Ser46-P ) binding to DNA from the ftsX promoter , a gene whose transcript level was not influenced by CcpA inactivation ( e . g . a negative control ) , produced a non-specific DNA binding pattern ( Figure 3B ) . Similarly , recombinant CcpA and the CcpA- ( HPr-Ser46-P ) complex also bound with high affinity to the nga/slo ( Kd of 674 nM and 42 nM , respectively ) and spyCEP ( Kd of 257 nM and 33 nM , respectively ) cre sites ( Figure 3C and 3D ) . Together with previously published data indicating binding of CcpA to the sagA promoter , these data demonstrate that recombinant CcpA and the CcpA- ( HPr-Ser46-P ) complex bind with high affinity to cre site DNA from multiple virulence factor encoding genes that are directly regulated by CovR . One potential mechanism by which the CovRS status of GAS could affect CcpA function is for CovR to regulate CcpA expression . The consensus GAS CovR binding sequence is ATTARA , where R = A or G [7] . There is one ATTARA motif in the vicinity of the CcpA promoter . We tested whether CovR influenced CcpA transcript level by determining ccpA transcript level in strain 2221ΔcovR [16] . During growth in THY there was no significant difference in ccpA transcript level between strains MGAS2221 , 2221ΔcovR , or MGAS5005 ( the covS mutant strain ) ( Figure S4 ) . There are no putative cre sites in the CovR promoter region . Thus , not surprisingly , there was no significant difference in covR transcript level between strains MGAS2221 and 2221ΔccpA or strains MGAS5005 and 5005ΔccpA ( Figure S4 ) . These data suggest that although CovR and CcpA each influence the expression of several of the same key GAS virulence factors , CovR and CcpA do not influence the transcript level of the other regulator under the conditions studied . To better understand the relationship between CcpA and the CovRS TCS , we created a CcpA/CovR double mutant strain by genetically inactivating ccpA in strain 2221ΔcovR , resulting in strain 2221ΔcovRΔccpA ( Table 1 , Figure S1 ) . There was no significant difference in the doubling time or final density of organisms grown in THY between strain 2221ΔcovRΔccpA and strains MGAS2221 , 2221ΔccpA , and 2221ΔcovR ( Figure S2C , Table S1 ) . Although previous experiments suggested that CcpA and CovR influence expression of many of the same GAS genes , the CcpA and CovR transcriptomes have not been directly compared [20] , [29] , [30] , [31] . Therefore , we next tested the hypothesis that inactivation of CcpA and CovR have similar effects on the GAS transcriptome by performing expression microarray analysis of strains MGAS2221 , 2221ΔccpA , 2221ΔcovR , and 2221ΔcovRΔccpA during the mid-exponential and stationary phases of growth in THY ( see Figure S2C for RNA isolation points and Figure S5 for principal component analyses of the microarray data ) . Quadruplicate replicates were performed for each strain at each time point . At both the mid-exponential and stationary growth points , the percent of total ORFs with a significant difference in transcript levels compared to the wild-type strain was approximately 10% for strain 2221ΔccpA , 15% for strain 2221ΔcovR , and 20% for strain 2221ΔcovRΔccpA ( Tables S2 , S3 and S4 ) . We discovered significant overlap in the CcpA and CovR transcriptomes , primarily in genes encoding proteins known to be or putatively involved in virulence and in the transport and metabolism of carbohydrates and amino acids ( Table 2 ) . Genes encoding proteins known to be or putatively involved in virulence that were affected by both CcpA and CovR included speB , spyCEP , endoS , and the operons encoding SLS and Slo ( Figure 4A ) . EndoS ( endoglycosidase S ) cleaves a glycan side chain from human immunoglobulin G [47] . The effects of CcpA and CovR on sagA , slo , spyCEP , and speB transcript level were confirmed by QRT-PCR ( Figure S6 ) . A casein hydrolysis assay confirmed that strain-to-strain differences in speB transcript levels resulted in altered functional SpeB activity ( Figure S6E ) . All putative virulence factor genes that were affected by CcpA inactivation were also affected by CovR inactivation . However , the CovR transcriptome included several GAS virulence factors not influenced by CcpA such as the genes encoding streptokinase and the immunoglobulin cleaving protease Mac-1/IdeS ( Tables S2 and S3 ) . In addition to finding that CovR and CcpA influenced expression of several of the same virulence factor genes , we also observed alterations in the transcript levels of many of the same metabolic gene operons for the 2221ΔccpA , 2221ΔcovR , and 2221ΔcovRΔccpA strains compared to strain MGAS2221 ( Table 2 , Figure 4B ) . These included operons known to be or putatively involved in carbohydrate metabolism and operons encoding proteins in the arginine deiminase and histidine degradation pathways . For metabolic operons , CcpA inactivation tended to result in more significant alteration in transcript levels compared to CovR inactivation ( e . g . the differences in the transcript of arcA , the first gene in the arginine deminase operon , were ∼20-fold between wild-type and strain 2221ΔccpA vs . ∼3-fold between wild-type and strain 2221ΔcovR , Figure 4B ) . Moreover , CcpA inactivation affected several metabolic operons that were not affected by CovR inactivation ( e . g . the putative mannose/fructose transport system operon ) . Taken together , we conclude that under laboratory conditions CcpA and CovR influence expression of many of the same genes with CovR having a greater impact on virulence gene expression whereas CcpA has a greater influence on the expression of genes involved in basic metabolic processes . The significant overlap between the CovR and CcpA transcriptomes led us to hypothesize that these two proteins bind to the promoter DNA of several of the same genes . Recombinant CovR was overexpressed , purified , and phosphorylated as described in Materials and Methods ( Figure 2 ) . This purified CovR lacks non-native residues , can be readily concentrated , and remains soluble at high concentrations . We used fluorescence polarization to study protein-DNA interaction because this method is equilibrium-based and done in solution , thereby approximating the in vivo environment ( for details on fluorescence polarization see the Materials and Methods section ) [48] . Fluorescence polarization has not been previously used to study CovR-DNA interaction . Therefore , we first tested the binding of recombinant CovR to the hasA promoter for which CovR-DNA interaction has been well characterized [18] , [25] , [49] . Recombinant CovR bound to the hasA promoter DNA with an approximately 4-fold increase in affinity when CovR was phosphorylated ( Kd decreased from 2200 nM to 640 nM , Figure 5A ) , which is consistent with previous reports regarding the effects of phosphorylation on CovR-hasA promoter binding [18] , [25] . Analysis of recombinant CovR with labeled DNA from the promoter region of the non-CovR regulated gene typA ( i . e . a negative control ) produced low polarization changes and linear binding consistent with low affinity , non-specific DNA binding ( Figure 5B ) . These data indicated that we could reliably use fluorescence polarization to investigate CovR-DNA binding . In terms of genes involved in basic metabolic processes , the transcriptome data demonstrated altered transcript levels of arcA , which encodes a protein involved in arginine utilization , and amyA , which encodes an actively secreted carbohydrate-degrading protein , in strains 2221ΔccpA and 2221ΔcovR compared to wild-type ( Figure 4B ) . Thus , we next tested the hypothesis that recombinant CovR and recombinant CcpA- ( HPr-Ser46-P ) bind with high affinity to DNA from the arcA and amyA promoters . Recombinant CovR bound specifically and with reasonably high affinity to the promoter regions of the arcA ( Kd of 637 nM and 230 nM for unphosphorylated and phosphorylated CovR respectively , Figure 5C ) and amyA genes ( Kd of 245 nM and 77 nM for unphosphorylated and phosphorylated CovR respectively , Figure 5D ) . Similarly , recombinant CcpA bound with high affinity to putative cre sites from the arcA ( Kd of 219 and 18 nM without and with HPr-Ser46-P respectively ) and amyA ( Kd of 160 and 32 without and with HPr-Ser46-P respectively ) promoters ( Figure 5E and 5F ) . Together with previous data regarding the binding of CovR and CcpA- ( HPr-Ser46-P ) to DNA from virulence factor encoding genes ( Figure 2 ) [18] , these data provide a mechanism for the extensive overlap observed in the CcpA-CovR transcriptome data . We have previously demonstrated that GAS markedly alters its transcriptome during interaction with human saliva compared with growth in a laboratory medium [30] , [50] . Given that the CcpA- ( HPr-Ser46-P ) complex and CovRS TCS are known to be part of the process by which GAS responds to changes in the environment [30] , [51] , we next tested the hypothesis that CcpA and CovR contribute to how GAS modifies gene expression in response to interaction with human saliva . We determined the transcript levels of six genes known to be directly regulated by CcpA and CovR , four that encode virulence factors and one each encoding a carbohydrate utilization and amino acid utilization protein . For the parental wild-type strain MGAS2221 , the transcript level of speB , spyCEP , slo , sagA , arcA , and amyA was significantly increased during growth in human saliva compared to THY ( Figure 6 ) . In contrast , the transcript level of speB , sagA , and arcA were not significantly different in strain 2221ΔccpA between growth in human saliva and laboratory medium indicating that CcpA was needed for the altered gene expression pattern observed in the wild-type strain in human saliva compared with THY ( Figure 6 ) . Although the transcript level of spyCEP and slo were increased in strain 2221ΔccpA during growth in human saliva , the increase in gene transcript level between the two conditions was significantly less than that observed for strain MGAS2221 . Similarly , the transcript level of each of the six genes tested was increased in human saliva to a lesser degree in strain 2221ΔcovR compared to wild-type ( Figure 6 ) . These data indicate that , for the genes tested , CcpA and CovR participate in the remodeling of GAS gene expression in response to human saliva . Thus far in the work , our data had shown significant overlap in the CcpA and CovR transcriptomes , that CcpA and CovR bind to DNA from several of the same genes , and the CcpA and CovR are key to how GAS remodels its gene expression profile during interaction with human saliva . To study the in vivo relevance of how CcpA and CovR together contribute to GAS pathogenesis , we compared the virulence of strain MGAS2221 to mutant strains 2221ΔccpA , 2221ΔcovR , and 2221ΔcovRΔccpA using a mouse myositis model [52] . As expected for a negative virulence-gene regulator , CovR inactivation significantly decreased mouse survival compared to wild-type infected animals ( Figure 7A , P<0 . 01 ) . Conversely , mice infected with strain MGAS2221 had a significantly increased mortality rate compared to mice infected with mutant strain 2221ΔccpA or mutant strain 2221ΔcovRΔccpA ( Figure 7A ) . We analyzed RNA recovered from GAS in mouse muscle to correlate the GAS gene expression profile with the mortality data . The elevated transcript level of virulence factor encoding genes in strain 2221ΔcovR compared to strain MGAS2221 is consistent with the hypervirulent phenotype of the CovR mutant strain ( Figure 7B ) . However , in contrast to what was observed during growth in THY , there was no significant difference in spyCEP , sagA , and slo transcript level between wild-type and strain 2221ΔccpA during infection ( Figure 7B ) . In terms of metabolic genes , there was no significant difference in arcA transcript level during infection between strain 2221ΔccpA and its parental , wild-type strains whereas amyA transcript level was significantly increased in the CcpA-inactivated strain ( Figure 7B ) . Finally , compared to strain MGAS2221 , speB and hasA transcript levels were significantly decreased in the CcpA-inactivated strains in mouse muscle ( Figure 7B ) , providing a potential explanation for the diminished virulence of the CcpA inactivated strains . To gain further insight into the molecular mechanisms underlying GAS gene expression during invasive infection , we next compared select virulence gene transcript levels in strain MGAS2221 during infection with those observed during growth in THY . We determined the relative transcript levels of five virulence factor encoding genes known to be influenced by CcpA and five genes not known to be influenced by CcpA ( Figure 7C ) . The transcript level of all of the CcpA-influenced genes were increased in strain MGAS2221 during infection compared to growth in THY whereas the transcript level of only one of the non-CcpA-influenced genes was increased during infection ( Figure 7C ) . These data suggest that CcpA may be either repressing gene transcript levels during growth in THY or activating gene expression during infection . By comparing gene transcript levels in strain MGAS2221 and 2221ΔccpA , we found that CcpA repressed spyCEP , sagA , and slo during growth in laboratory medium but not during infection ( Figure 7D ) . Conversely , the transcript level pattern of hasA and speB indicated that CcpA was activating these genes during infection . Comparison of transcript levels during infection versus growth in THY for strain MGAS2221 and 2221ΔcovR demonstrated that CovR inactivation resulted in relatively similar effects on GAS gene expression for the two conditions ( Figure 7E ) . Taken together , we conclude that CcpA and CovR contribute to the virulence gene expression profiles of GAS during infection but that the effect of CcpA on GAS gene expression differs significantly depending on the studied environment . The decreased virulence of the CcpA-inactivated strains in the mouse myositis model suggested there was either a decreased intensity of the local infectious process for the CcpA-inactivated strains or that the CcpA-deficient strains had a diminished rate of bacterial dissemination from the primary infection site . To distinguish between these two possibilities , we determined the number of viable GAS CFUs present in mouse limbs ( local infection site ) and mouse spleens ( disseminated infection site ) 48 hrs after infection . We observed no significant difference among the four strains in the number of viable GAS CFUs present in the infected limbs at 48 hrs post-inoculation ( Figure 7F ) . However , the wild-type and 2221ΔcovR strains were recovered at significantly higher CFUs from mice spleens compared to the CcpA inactivated strains ( Figure 7F ) . Invasive GAS infection can be associated with spontaneous mutations in covRS [21] , [26] , [27] , [28] . Therefore , one explanation for the decreased dissemination rate of the CcpA inactivated strains is that CcpA contributes to the emergence of GAS strains with covRS mutations . To test this hypothesis , we sequenced the covRS operon of GAS isolates from the spleens of mice infected with strain MGAS2221 and strain 2221ΔccpA . covRS mutations were found in 17 of 24 isolates from mice infected with strain MGAS2221 . In contrast , none of the 24 isolates from mice infected with strain 2221ΔccpA had a covRS mutation ( P<0 . 01 by Fisher's exact test ) . Isolates derived from strain MGAS2221 had missense mutations in CovR and nonsense mutations in CovS ( see Table 3 for mutation details ) . These results indicate that interplay between the CcpA and CovRS systems contribute to the pathogenesis of invasive GAS infection .
It has long been recognized that bacteria react to environmental changes by altering expression of genes involved in basic metabolic processes . Indeed , early work on regulation of transcription demonstrated how activity of the lac operon varied in response to lactose concentration [53] . Similarly , for many years it has been recognized that bacterial virulence factor production changes in response to alterations in the environment [54] , [55] . However , the molecular mechanisms underlying the control of bacterial virulence factor expression in particular environmental conditions , such as those encountered during human infection , are just beginning to be fully elucidated [51] , [56] . Specifically , there has been limited investigation into how combinations of transcriptional regulators control gene expression during infection despite the clear importance of regulatory networks to microbial pathogenesis [5] , [57] . The data generated herein demonstrate that the global metabolic gene regulator CcpA and the virulence factor regulator CovR act together to control expression of diverse GAS genes thereby contributing to the critical ability of GAS to remodel its transcriptome in response to distinct environmental cues . Examination of the gene expression and protein binding data generated during this investigation , along with data from previous studies [16] , [51] , [56] , [58] , allows us to generate a model for how CcpA and CovR participate in the alteration of GAS gene expression observed when GAS shifts from growth in laboratory medium to the host ( Figure 8 ) . The different environmental carbohydrate concentrations encountered during infection eventually result in a change in the ratio of kinase/phosphorylase activity of HPrK/P thereby altering the concentration of HPr-Ser46-P . The differences in HPr-Ser46-P levels will affect CcpA cre site interaction ( as demonstrated by the protein binding data in Figures 3 and 5 ) thereby altering transcription of GAS virulence factor , carbohydrate catabolism , and amino acid catabolism encoding genes ( Figure 8 ) . At the same time , CovS responds to changes in environmental ion concentrations , such as Mg2+ , and to the presence of innate immune peptides , by changing the phosphorylation status of CovR [51] , [56] , [58] . Phosphorylation/dephosphorylation of CovR alters its interaction with DNA , again changing the transcription of diverse GAS genes [16] . By having CcpA and CovR regulate expression of many of the same genes , the expression of a broad array of key GAS genes can be varied in response to an array of environmental signals . Appreciation of the potential complexity of the GAS CcpA-CovRS transcriptional network was broadened by a recent finding that CovS can either activate or repress CovR-mediated gene expression depending on the CovR target gene [16] . Our data demonstrate that the effect of CcpA on gene expression during host-pathogen interaction was significantly different from that observed during growth in standard laboratory medium and was dependent on whether the particular gene was activated or repressed by CcpA during growth in THY ( Figure 7D ) . For example , during growth in THY CcpA repressed the transcript level of the key virulence factor encoding genes sagA , spyCEP , and slo . However , the transcript levels of these three genes were not increased in strain 2221ΔccpA compared to strain MGAS2221 during infection Our protein-binding data indicate that binding of CcpA to cre sites in sagA , spyCEP , and slo at physiologic CcpA concentrations requires the presence of HPr-Ser46-P ( Figure 3 ) [30] , [59] . Thus , if GAS is experiencing a low HPr-Ser46-P state during infection , the absence of the CcpA- ( HPr-Ser46-P ) complex will likely release CcpA from cre site interaction thereby resulting in the increased sagA , spyCEP , and slo transcript levels observed in strain MGAS2221 in the host ( Figure 7C ) . In contrast , the transcript levels of genes that were increased during infection in strain MGAS2221 and are activated by CcpA , such as speB , remained decreased in the CcpA inactivated strain compared to wild-type during infection ( Figure 7D ) . Our findings that CcpA positively influences speB transcript level and directly binds to the speB regulatory region are in concert with a recent study examining the role of CcpA in GAS virulence gene expression ( 39 ) . A possible explanation for these data is that recombinant CcpA binds to the speB cre site ( Figure 3A ) in the absence of HPr-Ser46-P with a Kd ( ∼100 nM ) that is within the potential physiologic concentration of CcpA as determined in Bacillus species ( 20–250 nM ) [59] . A previous study of CcpA in B . subtilis found that CcpA-mediated gene activation did not require the presence of HPr-Ser46-P [60] . Such a finding is consistent with our data demonstrating decreased speB transcript level in the CcpA-inactivated strain under conditions where HPr-Ser46-P levels are expected to be low or absent , such as growth in human saliva ( Figure 6 ) . Thus , the effect of CcpA on GAS gene expression in vivo is likely occurring by more than one mechanism . Our conclusions that CcpA affects gene expression during infection via multiple mechanisms and that CcpA-inactivation does not alter sagA transcript levels during host-pathogen interaction are similar to other recently published data despite the fact that a subcutaneous , rather than myositis , mouse model was used in that investigation [39] . Inactivation of CcpA markedly attenuated the virulence of the parental strain MGAS2221 whereas CovR inactivation significantly increased virulence ( Figure 7A ) . A possible explanation for these observations can be derived from the expression microarray data which showed marked increases in the transcript levels of basic metabolic genes in the ΔccpA isogenic mutant strain ( Figure 4B ) . Thus , there are likely to be profound metabolic consequences of CcpA inactivation through inefficient production of proteins involved in carbon source acquisition and catabolism . Although there were also increases in metabolic gene transcript levels in the CovR isogenic mutant , the increases were smaller in comparison to strain 2221ΔccpA ( Figure 4B ) . This finding indicates that there may be less metabolic cost of CovR inactivation compared to CcpA inactivation . Inactivation of CcpA in the ΔcovR background markedly decreased GAS virulence suggesting , in simplistic terms , that the metabolic consequences of CcpA inactivation outweighed the overexpression of virulence factors resulting from CovR inactivation . Such interplay between energy use and virulence factor production may have contributed to the lack of emergence of spontaneous mutations in the covRS operon in strain 2221ΔccpA during murine soft-tissue infection . Our discovery that significant overlap exists between the CovRS and CcpA transcriptional regulatory systems adds to understanding of the molecular mechanisms used by pathogenic human microbes to alter protein production in response to environmental changes . Interestingly , GAS CcpA and CovR binding sites can be proximal , indicating that the spatial organization of GAS promoters may allow for protein-protein interaction between the two regulators . The first description of cooperative DNA binding of a response regulator and an independent transcriptional regulator in a prokaryote was recently made in a study of developmental gene expression in Myxococcus xanthus [61] . We are currently investigating whether direct interaction between CcpA and CovR contributes to the ability of GAS to modulate global gene expression during infection . It has recently been demonstrated that targeting bacterial virulence factor regulation during infection can decrease infection severity [3] , [62] . The data presented herein suggest that the CcpA-CovRS regulatory network is a potential target for the development of novel antimicrobials .
Saliva was collected from human volunteers who gave their written informed consent under an MD Anderson Cancer Center Institutional Review Board approved protocol . Mouse experiments were performed according to protocols approved by the Methodist Hospital Research Institute Institutional Animal Care and Use Committee . The strains and plasmids used in this work are presented in Table 1 , and primers used for isogenic mutant strain creation are listed in Table S5 . The serotype M1 group A streptococcal ( GAS ) strains MGAS2221 and MGAS5005 are genetically representative of the serotype M1 clone responsible for most contemporary ( post-1987 ) human infections , and both genomes have been sequenced [26] . Strain MGAS2221 and MGAS5005 are essentially genetically identical except for a truncation of the CovS protein in strain MGAS5005 [26] . Strain 5005ΔccpA and comp5005ΔccpA were described previously [30] . Strain 2221ΔccpA and comp2221ΔccpA were created using non-polar insertional mutagenesis and plasmid pDCccpA in the same fashion as that described for CcpA isogenic mutant strains created from strain MGAS5005 [30] . pDCccpA was created from plasmid pDC123 , which is a low-copy number plasmid capable of replicating in Gram-positive organisms , by cloning the ccpA gene and promoter region from strain MGAS5005 into the multi-cloning site of pDC123 [63] . Selection for CcpA inactivation was via spectinomycin at 150 µg/mL and selection for the CcpA-complementing plasmid was done with chloramphenicol at 4 µg/mL . Strain 2221ΔcovR was created as described [16] . Strain 2221ΔcovRΔccpA was created by placing the spectinomycin resistance cassette in place of the CcpA gene in the 2221ΔcovR strain with selection via spectinomycin . Strains were grown in a nutrient-rich medium ( Todd-Hewitt broth with 0 . 2% yeast extract ( THY ) ) at 37°C with 5% CO2 . RNA was purified from four biological replicates on two separate occasions using an RNeasy Mini Kit ( Qiagen ) . TaqMan real-time QRT-PCR ( primers and probes listed Table S5 ) was performed with an Applied Biosystems 7500 system using the previously validated tufA gene as an internal control as described [64] . For QRT-PCR , a significant difference in transcript level was defined as having at least a 2-fold difference in the mean transcript level along with a P value of<0 . 05 for a two-sample t-test assuming unequal variance . QRT-PCR data are graphed in a log2 format to facilitate demonstration of either positive or negative regulation by CcpA and/or CovR . Samples for expression microarray analysis were performed in quadruplicate . A custom-made Affymetrix GeneChip® that contains 100% of the ORFs of strain MGAS2221 was used for expression microarray ( transcriptome ) studies as described [30] . To compare gene transcript levels between the wild-type and mutant strains , a two-sample t-test ( unequal variance ) was applied followed by a false discovery rate correction ( Q<0 . 05 ) to account for multiple testing using Partek Genomics Suite version 6 . 4 . Transcript levels were considered significantly different when the corrected P value was <0 . 05 and the mean difference was at least 2-fold . Principal component analysis was performed using the Partek Genomics Suite ( Figure S5 ) . GAS strains were grown to indicated growth phases in THY . SDS-PAGE and immunoblotting were performed using specific anti-SpeB antibody [65] . Functional SpeB protease activity was determined using casein hydrolysis as described [44] . Recombinant GAS CcpA was purified to homogeneity from Escherichia coli as previously described ( Figure 2A ) [30] . Recombinant GAS HPr was obtained using the same cloning strategy as previously described for recombinant GAS CcpA ( Figure 2A ) [30] . To obtain functional HPrK/P , the GAS hprK/P gene was cloned from strain MGAS5005 into plasmid pET21a ( Novagen ) with primers designed such that no His tag was attached to the recombinant HPrK/P protein . An E . coli extract enriched for recombinant GAS HPrK/P was created by growing the BL21-HPrK/P cells in LB/ampicillin with 0 . 5 mM IPTG to an OD600 of 2 . 0 . Cells were centrifuged and washed in 20 mM Tris-HCl pH 7 . 6 with 3 mM DTT , recentrifuged , and lysed via sonication in a buffer containing 0 . 2 mM Tris-HCl pH 7 . 6 , 0 . 03 mM DTT , and 0 . 5 mM PMSF ( a serine protease inhibitor ) . This lysate is enriched for HPrK/P ( Figure 2A ) . Phosphorylation of HPr at serine-46 was accomplished by incubating 500 µl of recombinant HPr for 20 mins at 37°C with 599 µl of HPrK/P extract in 5 mM ATP , 10 mM fructose-1 , 6-bisphosphate , 20 mM Tris-HCl pH 7 . 5 , 1 mM DTT , and 5 mM MgCl2 . To obtain purified HPr-Ser46-P , 100 µL of nickel resin ( Qiagen ) was added and the mixture was rotated for 1 hr at room temperature . The mixture was washed 4 times with 50 mM NaH2PO4 pH 8 . 0 , 300 mM NaCl , and 20 mM imidazole , and HPr-Ser-46P was eluted with the same buffer except that the imidazole concentration was increased to 250 mM . The phosphorylation state of HPr was assayed by running the unphosphorylated and phosphorylated proteins on a native glycine gel ( pH 10 . 4 ) ( Figure 2B ) . Repeated analyses showed that phosphorylation of HPr was stable for at least one week . To maintain CovR in its soluble form and to work with recombinant CovR protein that lacked a tag , we cloned the covR gene from MGAS5005 into plasmid pTXB1 ( New England BioLabs ) which resulted in a fusion protein with an intein tag and a chitin binding domain . Recombinant CovR was obtained following the manufacturer's instructions with release of the intein tag using DTT ( Figure 2A ) . CovR was phosphorylated as described [25] with phosphorylation assessed by running unphosphorylated and phosphorylated CovR protein under non-denaturing conditions as described for HPr-Ser46-P ( Figure 2C ) . Repeated assays showed a CovR phosphorlyation half-life of about 90 minutes , which is consistent with previous reports [66] . Thus , all experiments with phosphorylated CovR were performed immediately following phosphorylation completion . To remove all phosphorylation reagents , CovR was spun through protein desalting columns ( Pierce ) into freshly made DNA binding buffer ( 20 mM Tris , pH 7 . 5 , 50 mM NaCl , 2 mM DTT , and 10 µg/mL of polydI:dC ) . All protein concentrations were assessed using the Bradford assay ( Bio-Rad ) . DNA binding activity of CcpA and CovR was studied using a fluorescence polarization based assay . In brief , fluorescence polarization is an indirect measurement of the rotation of a molecule in solution that employs a fluorescently labeled molecule as a reporter . When two molecules interact , such as a protein binding to DNA that has been labeled with fluorescein , the intrinsic rotation of the DNA is slowed which can be observed as an increase in the polarization of the fluorescein . By titrating known amounts of protein into the binding solution , the equilibrium dissociation constant ( Kd ) can be determined [48] . Fluorescence polarization was used as previously described to determine a series of CcpA-DNA binding constants with and without HPr-Ser46-P [30] . CovR binding affinities were measured using fluorescence polarization by titrating solutions of CovR ( unphosphorylated or phosphorylated ) into 200 µl of solution containing labeled DNA ( 1 nM ) in 20 mM Tris , pH 7 . 5 , 50 mM NaCl , and 2 mM DTT , and 10 µg/mL of polydI:dC . Polarization was measured at 25°C on a Beacon 2000 fluorescence polarization instrument ( PanVera , Madison , WI ) . Data were analyzed assuming a 1:1 binding stoichiometry between functional protein unit and labeled DNA . Binding parameters were determined via non-linear regression using the equation Y = ( ( Bmax • X ) / ( Kd • X ) ) + NS • X where Bmax is the polarization value at maximum specific binding , Kd is the equilibrium dissociation constant and NS is the slope of non-specific binding . Goodness of fit ( R2 ) values for each of the binding assays was >0 . 98 . Twenty female outbred CD-1 Swiss mice ( Harlan-Sprague-Dawley ) were injected intramuscularly in the right hind limb with 2 . 5×107 GAS CFU using an established model of GAS intramuscular infection [52] . Comparison of mortality rates was performed by Kaplan-Meier survival analysis . Differences in mortality rates were considered significant for a P value of <0 . 05 after accounting for multiple comparisons . For quantitation of inoculation site CFUs , four mouse limbs per strain treatment group were homogenized in phosphate buffered saline and plated onto sheep blood agar , incubated for 24 hrs , and CFU counted . For quantitation of GAS dissemination , the same protocol was employed using mouse spleens instead of limbs . To compare rates of spontaneous covRS mutations , GAS colonies from spleens of mice that had been infected with strain MGAS2221 and 2221ΔccpA were randomly selected for sequencing of the entire covRS operon . Six GAS colonies per mouse ( 4 mice were inoculated with each strain ) were selected for sequencing for a total of 24 colonies per strain . For transcript level measurement during infection , mice limbs were immediately placed into RNAlater ( Qiagen ) and then snap frozen with liquid nitrogen . GAS RNA was isolated from mouse limbs as previously described [22] . In brief , the frozen limbs were subjected to vigorous mechanical lysis with a series of sharp blows using a three pound drill hammer and FastPrep Lysing Matrix B ( MP Biomedicals ) . RNA was isolated using a Qiagen RNeasy kit and treated vigorously with Turbo DNAse ( Ambion ) . cDNAs were prepared with and without reverse transcriptase to ensure that TaqMan QRT-PCR signal amplification did not reflect DNA contamination . Mouse limbs inoculated with PBS were also included in the analysis to ensure that the observed signal did not arise from eukaryotic RNA . covR , 3572611; covS , 3572612; ccpA , 3572471; hpr , 3571784; hprK/P , 3572422; speB , 3571136; nga , 3572762; slo , 3572764; spyCEP , 3760194; sagA , 3572347; ftsX , 3572932; endoS , 3571346; hasA , 3571023; typA , 3571645; arcA , 3571626; amyA , 3571845 . Expression microarray data have been deposited at the Gene Expression Omnibus database at National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov/geo ) and are accessible through accession number GSE20212 . | Group A Streptococcus ( GAS ) causes diverse infections in humans ranging from pharyngitis ( strep throat ) to necrotizing fasciitis ( the flesh-eating disease ) . It is well known that GAS secretes a broad array of virulence factors that are critical to its ability to cause human infections , but how GAS coordinates virulence factor production during infection is poorly understood . We discovered that two GAS proteins , catabolite control protein A ( CcpA ) and control of virulence regulator ( CovR ) , regulate production of many of the same virulence factor encoding genes , indicating that GAS uses these two regulatory proteins to modulate virulence factor production in response to environmental stimuli . We determined that CcpA and CovR are able to bind to DNA from co-regulated genes , indicating that the proteins control gene expression by directly interacting with DNA . Using a mouse model of muscle infection , we found that CcpA and CovR , alone and in combination , are critical to the ability of GAS to regulate expression of virulence factor encoding genes during infection . These findings increase understanding regarding the regulatory mechanisms critical to the ability of bacterial pathogens to cause infection . | [
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] | 2010 | A Combination of Independent Transcriptional Regulators Shapes Bacterial Virulence Gene Expression during Infection |
Understanding how axon guidance receptors are activated by their extracellular ligands to regulate growth cone motility is critical to learning how proper wiring is established during development . Roundabout ( Robo ) is one such guidance receptor that mediates repulsion from its ligand Slit in both invertebrates and vertebrates . Here we show that endocytic trafficking of the Robo receptor in response to Slit-binding is necessary for its repulsive signaling output . Dose-dependent genetic interactions and in vitro Robo activation assays support a role for Clathrin-dependent endocytosis , and entry into both the early and late endosomes as positive regulators of Slit-Robo signaling . We identify two conserved motifs in Robo’s cytoplasmic domain that are required for its Clathrin-dependent endocytosis and activation in vitro; gain of function and genetic rescue experiments provide strong evidence that these trafficking events are required for Robo repulsive guidance activity in vivo . Our data support a model in which Robo’s ligand-dependent internalization from the cell surface to the late endosome is essential for receptor activation and proper repulsive guidance at the midline by allowing recruitment of the downstream effector Son of Sevenless in a spatially constrained endocytic trafficking compartment .
The complex wiring patterns of the adult central nervous system are established by the stepwise navigation of growth cones and migrating cells through a series of choice points during development . At each choice point , the complement of guidance receptors expressed on the growth cone’s plasma membrane determines which of the cues in the extracellular environment will inform the cell’s guidance decision as it navigates toward its eventual synaptic partner . Understanding how an individual growth cone deploys its guidance receptors to make specific guidance decisions is critical to learning how proper wiring is established in development . Roundabout ( Robo ) receptors comprise a family of highly conserved axon guidance receptors that mediate repulsion in response to their Slit ligands during neuronal development [1–4] . Robo receptors have also been implicated in genome-wide association studies with the pathogenesis of several human diseases including autism and schizophrenia [5 , 6] , and they are thought to be causatively linked to dyslexia and periventricular nodular heterotopia [7] , suggesting roles in guidance of more diverse axonal projections in the human cortex that are yet to be characterized . In both invertebrates and vertebrates , Slits serve as repulsive cues to their Robo receptors by demarcating regions into which axons cannot maintain their exploratory projections . In the case of the Drosophila embryonic ventral nerve cord ( VNC ) , Slit is expressed by midline glia , which creates a barrier for axonal projection for any growth cones expressing Robo at their surface [2 , 3] . In robo mutants normally ipsilaterally-projecting ( ipsilateral or post-crossing commissural ) axons ignore the presence of this repulsive cue and project into the midline and even circle there in namesake roundabouts [8] . Slit-mediated repulsive guidance can also instruct axonal projections by corralling fascicles into relative valleys of Slit expression- mouse callosal axons project between the indusium griseum and the glial wedge structures [9]- or by directing a 90° turn in bifurcating branches of sensory axons into the dorsal funiculus [10] . Analogously , there exists a relative valley in Slit expression in medio-lateral axis of the Drosophila VNC through which a sizeable set of longitudinal fascicles project [11] . The mechanism by which Slit triggers repulsion at the cellular level is not completely understood , but must involve an initial mis-projection into Slit-expressing regions in order to sense and then respond to the presence of the repulsive cue . One growth cone phenotype resulting from loss of Robo is defective filopodial retraction from the Slit-containing embryonic midline in Drosophila , resulting in stabilization of contralateral filopodial projections [12] . Similarly , loss of robo2 ( astray ) in zebrafish leads to abnormal stabilization of mis-projecting growth cones in the ventral forebrain , ‘errors’ that are normally corrected in wild-type [13] . The error-correction implicit in repulsive guidance from an initially adhesive protein-protein interaction requires some sort of physical severing which has been ascribed to juxtamembrane cleavage , endocytosis , or both [14–19] . Endocytosis in the growth cone has been implicated in the plasma membrane dynamics necessary for such responses as collapse[14 , 20 , 21] , or , when applied asymmetrically , turning [22–24] . Endocytosis has also been implicated in the control over the complement of guidance receptors expressed on the growth cone surface , thereby fine-tuning sensitivity to extracellular cues [25–27] . Endocytic trafficking of Robo by Commissureless has also been demonstrated to negatively regulate delivery to the growth cone surface [28 , 29] . Endocytic trafficking of guidance receptors might serve not only to control surface receptor levels , but also to gate their activation once inside the cell . Evidence for this idea comes from the correlation between a requirement for the RhoGEFs vav2 and vav3 in both Ephrin endocytosis and proper retinogeniculate axon targeting [14] , as well as the correlation between Rac activity in EphA receptor endocytosis and retinocollicular targeting [30] . Whether receptor endocytosis represents a general mechanism to control activation of repulsive guidance receptor signaling and whether the transit of internalized guidance receptors through distinct endocytic compartments is required for in vivo signaling is not known . In this study , we identify Clathrin-dependent endocytosis of the Robo receptor as an obligate step in receptor activation and repulsive signaling . We present evidence that it is trafficking through endocytic compartments—following ligand-binding on the surface of the cell—that is required for receptor activation . We identify—with subcellular resolution–the early and late endosomes as compartments from which Robo signals , and identify the sequence motifs in Robo’s C-terminus that are required for its Slit-dependent internalization . Finally , we show that Slit-dependent endocytosis is required for both in vitro recruitment of the Ras/Rho GEF Son of Sevenless ( Sos ) , a downstream effector of Robo repulsive signaling and for Robo-mediated midline repulsion in vivo .
Based on previous findings suggesting a role for endocytosis in modulating axon guidance receptor activity and signaling , we could envision at least two plausible models for how Robo receptor endocytosis might regulate axon repulsion . If endocytosis modulates the amount of Robo receptor on the surface of the growth cone , a reduction in receptor endocytosis would be predicted to lead to increased levels of surface receptor and more robust repulsive signaling . Alternatively , if Robo receptor endocytosis is an obligate step in receptor activation , preventing or reducing Robo endocytosis would result in impaired repulsive signaling . To test which , if either , of these functions endocytosis might contribute to Slit-Robo signaling , we first sought genetic evidence implicating endocytic trafficking in midline axon repulsion . We examined an ipsilateral subset of axons whose projection patterns depend on Robo’s repulsive response to Slit . In robo mutants the medial-most of the FasII-positive fascicles invariably collapse and circle at the midline . Reducing slit and robo gene dose by half in heterozygous slit , robo/+ embryos results in a partial loss of repulsion , which represents a sensitized background in which we can detect both suppressors and enhancers ( Fig 1 ) . We , and others , have used this sensitized genetic background to uncover additional genes that contribute to midline repulsion [31–34] . In addition to offering a sensitive readout for alterations in midline repulsion , this strategy allows us to detect dominant genetic interactions , which avoids potential complications from removing all endocytic gene function , which would be predicted to have broad and early developmental defects . We screened mutants in known regulators of endocytosis for genetic interactions with slit and robo , including mutations in genes involved in ( 1 ) Clathrin-dependent endocytosis- alpha-adaptin and endophilinA- , ( 2 ) entry into the early endosome–rab5- and ( 3 ) entry into the late endosome- rab7 . Removing one copy of α-adaptin and endophilinA- genes involved in cargo loading and formation of clathrin coated pits [35 , 36]–enhances the number of crossing errors compared to slit , robo/+ heterozygotes ( Fig 1B–1D ) . Removing one copy of either rab5 , which regulates entry into the early endosome , or rab7 , which regulates entry into the late endosome also enhances ectopic crossing ( Fig 1E and 1F ) . In order to corroborate these findings , we tested for genetic interactions between the mutant alleles of endocytic trafficking genes and slit in another , more restricted subset of axons ( Fig 2A ) . Just like the FasII+ subset of axons , the normally ipsilateral Apterous+ ( Ap ) axons are sensitive to partial loss of repulsion; a loss of one copy of slit alone induces ectopic crossing events in 11% of embryonic segments ( Fig 2B ) . Inhibiting Clathrin-dependent endocytosis in this sensitized background by removing one allele of α-adaptin or endophilinA enhances the frequency of ectopic crossing events ( Fig 2F ) . Removing one copy of rab5 or rab7 also enhances ectopic crossing errors . These genetic interactions suggest that trafficking from the plasma membrane , and into the early and late endosome positively regulate repulsive midline guidance . Together these observations are consistent with endocytosis contributing to receptor activation , as opposed to a modulation of surface levels available to bind Slit . To determine whether the endocytic trafficking events relevant for midline guidance are occurring in neurons , we mis-expressed Dominant-Negative ( DN ) transgenes to inhibit components of the endocytosis machinery in the Ap neurons . Ectopic expression of DN forms of shibire , Drosophila Dynamin , ( to block scission of invaginated Clathrin-coated pits [37 , 38] ) , Rab5 and Rab7 ( to prevent entry into early and late endosomes , respectively ) , but not Rab4 and Rab11 ( to prevent entry into the recycling endosome ) , results in enhancement of the ectopic crossing defects that are observed in slit heterozygotes ( Fig 2C–2F ) . These findings are consistent with a model in which endocytic trafficking in neurons is contributing to Slit-Robo mediated repulsion . Further , the ectopic crossing events caused by expressing ShiDN or Rab5DN in the Ap axons in slit heterozygotes are fully rescued by increasing signaling of the Robo pathway by co-expression of a wild type Robo transgene ( S1A Fig ) : an observation that is consistent with a specific requirement for endocytic regulation during Slit/Robo repulsion . Taken together , these data are consistent with a model in which endocytic trafficking from the plasma membrane into the early and late , but not the recycling endosome of neurons positively regulates Robo-mediated midline repulsion . However these interactions alone cannot distinguish between the possibilities of endocytosis positively regulating repulsion from the midline , or negatively regulating attraction to the midline . We directly tested the latter hypothesis by assaying whether reducing the dosage of endocytic trafficking genes could enhance the ectopic crossing errors induced by enhanced midline attraction resulting from ectopic expression of the attractive guidance receptor Frazzled [39] . We detect no statistically significant difference between the observed crossing frequency and the predicted percentage crossing frequency from an additive interaction ( S1B Fig ) , suggesting that endocytosis is not negatively regulating attractive guidance . These observations further support the interpretation that disrupting endocytosis is specifically affecting midline repulsion . Our genetic interaction data are consistent with endocytosis in neurons positively regulating Slit/Robo-mediated repulsive guidance , but they do not provide insight into the cell and molecular mechanism . In order to test whether this positive regulation of repulsive signaling is due to endocytosis of the Robo receptor itself , we assayed whether manipulations to Robo’s capacity to undergo endocytosis would affect its signaling . Using sequence alignment with known binding motifs to AP-2 , the Clathrin adaptor complex expressed specifically on the surface of cells , we identified two tyrosine-based motifs in Robo’s C-terminus that are both conserved in human Robo1 sequence and predicted to be required for loading of Robo into Clathrin-coated pits— ( 1 ) YLQY , of the type YXXФ [40] , and ( 2 ) YQAGL , like the tyrosine containing sorting signals in the epidermal growth factor receptor ( EGFR ) and L1/NgCAM [41 , 42] ( S2J Fig ) . If Robo’s trafficking through the endocytic pathway is required for its repulsive response to Slit binding , then we would predict that both reducing Shibire function , and disrupting Robo’s ability to be loaded into Clathrin-coated pits would disrupt Robo signaling . To explore these possibilities , we developed an in vitro system to determine whether endocytosis of the Robo receptor can occur in response to Slit , and whether this process contributes to receptor signaling . Drosophila embryonic cells transfected with Robo that are bath treated for 10 minutes with Slit-conditioned media ( CM ) exhibit a robust spreading behavior , forming elaborate branched structures ( Fig 3A ) . In contrast , cells transfected with Robo and treated with CM from cells expressing empty vector show no such response ( Figs 3 , S2B , S4A and S4B ) . We have quantified this spreading behavior in two ways- first , we compute the total area of each cells’ processes as a number of pixels , and , using representative cells , compute the Average Process Area as a function of transfected Robo and type of CM treatment ( histogram , Fig 3D ) . To characterize the branching of processes in Slit-treated cells , we also performed Sholl analyses to compute the complexity of individual cell’s process field as a function of its radius starting after the cell cortex . These analyses are graphically displayed as the average Sholl profile of many cells treated with Slit CM ( Fig 3D ) . To assay whether the observed process elaboration behavior is indeed a readout of Robo activation in response to Slit we tested the following negative control variants of Robo: 1 ) deletion of the ectodomain ( RoboΔEcto , S2A’ Fig ) , 2 ) deletion of the first immunoglobulin domain ( RoboΔIg1 , Fig 3B ) , the minimal region that interacts physically with Slit’s D2 domain [43–45] , or 3 ) Robo missing its entire C-terminus ( ΔC , Fig 3C ) , which is required for all signaling output [46] . Each of these mutated forms of Robo show a loss of process elaboration in response to Slit . We also noted that expression of Robo∆C results in variable increase in the size of the cell cortex even in the absence of Slit treatment; however , since the Slit-dependent branch elaboration that we observed and quantified is independent of effects on the cell cortex , we did not explore this phenomenon further . Robo that is missing its Conserved Cytoplasmic CC2 and CC3 motifs , required for binding of the downstream effectors Ena , Dock , Pak , SOS and therefore Rac activation [32 , 47 , 48] , also display impaired spreading behavior ( S2C Fig ) . These observations support the idea that Robo signaling in response to Slit binding is required for the Rac-dependent spreading behavior seen in WT Robo-expressing cells . Next we wanted to test for a role for Clathrin-dependent endocytosis in Robo’s ability to generate branched processes in response to Slit treatment . We find that inhibiting endocytosis directly by co-transfection with DN Shibire ( Fig 3E ) , or treatment with the Dynamin inhibitor Dynasore ( S2D Fig ) , reduces the complexity of processes generated in response to Slit , as does deleting entirely , or point mutating the tyrosine residues of either of the two putative AP-2 binding motifs in Robo’s C-terminus ( Figs 3F , 3G and S2F–S2H ) . Deleting both motifs at the same time also results in a smaller maximum radius of the process field ( Figs 3H and S2E ) , similar to deleting the entire C-terminus , suggesting that the two AP-2 interacting motifs are each required for , and additively contribute to , Robo signaling . The qualitative and quantitative similarity in the process morphology of Slit-treated cells where Robo endocytosis is prevented , either by global disruption ( Dynasore or DN Shibire ) or by specific Robo mutations , suggests a contribution of receptor internalization to Robo’s activation . In addition , we find that endocytic trafficking , beyond internalization from the surface , through the early and late endosome also positively regulate Slit-dependent process elaboration . Inhibiting entry to the early or late endosome by co-expression of DN-Rab5 ( Fig 3I ) , or DN-Rab7 ( Fig 3J ) , respectively , also reduces branching complexity in Robo expressing Slit CM-treated cells . These data are consistent with a requirement for Clathrin-dependent endocytosis of the Robo receptor and trafficking into the early and late endosome for Slit-dependent process branching and outgrowth . In order to assess whether Robo’s C-terminal putative AP-2 interaction motifs indeed disrupt ligand-dependent endocytosis we directly assayed for a change in surface Robo levels in response to Slit in the same in vitro system . Using pHluorin , a pH sensitive GFP tag , on Robo’s N-terminus to distinguish surface Robo from the Robo protein in the lower pH environment of most cytosolic compartments , we analyzed the Slit-dependent reduction in surface receptor levels in S2R+ cells ( Fig 4A–4F ) . In cells transfected with wild-type pHluorin–tagged Robo , there is a reduction in the fluorescence intensity of pHluorin in Slit-treated , as compared to control treated cells , which we quantified as a percent decrease in average signal intensity across many cells ( Fig 4A , 4B and 4G ) . This Slit-dependent decrease in surface signal is inhibited by deleting Robo’s C-terminus ( Fig 4C and 4D ) , suggesting a requirement for signaling in the Slit-dependent reduction in Robo surface levels . Evidence that our small deletions disrupt Clathrin-dependent endocytosis comes from the similarity of their effect on surface levels to the effect observed by inhibiting Shibire with the Dynamin inhibitor drug Dynasore [49] . In both cases the Slit-dependent decrease in surface Robo is prevented ( Figs 4E , 4F and S3 ) , consistent with Slit stimulating Clathrin-dependent endocytosis of Robo . Analyzing trends in the spatial distribution of surface Robo intensity with reference to anatomical structures reveals clues about the mechanism of Robo internalization and branch formation . Tips of S2R+ processes bear peaks in surface Robo signal ( closed arrowheads in Figs 4A and S3A ) , which is similar to Robo localization on the tips of filopodia in the developing fly embryo [3] and in primary Drosophila neuron cultures [50] . In the cells that have responded to Slit treatment by reducing their Robo surface levels , presumably by Clathrin-dependent internalization from the surface , process branch-points are marked by reduction in surface Robo levels ( open arrowhead in Fig 4B ) . When inhibiting endocytosis , Robo signal stays high on both the processes with enlarged diameters and in the branch points that do exist ( open arrowhead Fig 4F ) , likely due to lack of Slit-dependent internalization . The correlation between the absence of receptor internalization , either by globally inhibiting endocytosis with Dynasore ( S4A and S4B Fig ) , or by deleting or point-mutating AP-2 adaptor motifs in Robo’s C-terminus ( Figs 4E–4G and S3C–S3G ) , and decreased process elaboration ( Figs 3H and S3F–S3H ) suggests that Clathrin-dependent endocytosis of Robo is required for its signaling output . To test whether the link between endocytic trafficking and Robo signaling is also observed in vivo , we analyzed Robo distribution and midline guidance in the embryo . The endogenous expression pattern of Robo throughout the embryonic ventral nerve cord is characterized by commissural exclusion and longitudinal enrichment [3] . If endocytic trafficking of Robo is required for repulsive signaling , we would expect to see a correlation between Robo mislocalization and guidance errors in embryos with defective endocytic trafficking . In fact , when we induce guidance errors by manipulating entry into the early endosome by expressing DN-Rab5 ( asterisks , Fig 4H ) , we see mislocalization of Robo to the ectopically midline projecting segments of normally ipsilateral axons ( open arrowheads , Fig 4I ) . This correlation between Robo mislocalization and guidance errors is specific to endocytic trafficking manipulations; when we induce ectopic crossing events by overexpressing the Frazzled attractive guidance receptor ( asterisk , Fig 4J ) , we find no mislocalized Robo on the crossing portions of axons , despite the similar number of ectopic crossing events ( closed arrowhead , Fig 4L ) . Further , Robo missing its AP-2 adaptor motif is also mislocalized to the commissural segments ( open arrowheads Fig 4M ) of ectopically crossing axons ( asterisks , Fig 4L ) . Finally , Robo is mislocalized to the collapsed Ap axon fascicles in embryos deficient for Slit , and to the ectopically crossing portions of axons in slit , robo/+ double heterozygotes expressing Robo missing its Slit-binding domain ( S3H Fig ) . Taken together these data suggest that Slit stimulates endocytosis of the Robo receptor , and that this decrease in surface signal is required for receptor signaling in the receiving cell as evidenced by the reduction in process elaboration in S2R+ cells and midline guidance errors in vivo . If our receptor manipulations indeed disrupt endocytosis , then we would expect to observe an effect on the intracellular trafficking of internalized Robo in experiments where we track Robo’s C-terminus in vitro following Slit treatment . We find that not only do our C-terminal motif deletions inhibit the Slit-dependent removal of Robo from the surface , but they also reduce Slit-dependent colocalization of Robo with endogenous Rab5 , a marker of the early endosome . Immunostaining for Slit and Rab5 reveals colocalization between Slit and the early endosome in cell processes ( Fig 5A , 5B and 5P ) . In response to Slit treatment , we also observe an induction of colocalization between Robo and Rab5 , specifically in the varicosities and branchpoints of cell processes ( Fig 5C–5E ) , the same structures that showed Slit-dependent surface Robo turnover ( arrowheads in Figs 4B and 5B ) . We have quantified this response as the percentage change in Manders’ overlap coefficient between Slit and Control CM treatment ( Fig 5Q ) . Expression of DN-Shibire ( Fig 5F and 5G ) , or deletion of Robo’s AP-2-binding motifs ( Fig 5K and 5L ) , prevents the Slit-dependent recruitment of Rab5 in cell processes , resulting in less colocalization of Slit with Rab5 ( Fig 5P ) . There is a concomitant reduction in colocalization of Rab5 with the Robo C-terminal tag in the same endocytosis-deficient conditions ( Fig 5H–5O and 5Q ) . These data provide evidence that Slit stimulates the translocation of Robo to the early endosome , and that this process requires Clathrin-dependent endocytosis specifically from the surface of cells . If Robo endocytosis is required for downstream signaling , then we would predict that inhibiting Clathrin-dependent endocytosis of Robo may prevent the recruitment of Son of Sevenless , which has previously been shown to be recruited to Robo in response to Slit-treatment in mammalian cells [48] . First , we assayed the relative contribution of Sos to the spreading behavior in our in vitro activation assay by co-expressing Sos missing its RacGEF domain ( Fig 6B ) . This dominant-negative construct blocks the Slit-dependent spreading behavior so effectively that the morphology of these cells are indistinguishable from those expressing Robo missing its entire C-terminus ( Fig 6A ) , indicating that this in vitro activation assay depends on the ability of Sos to activate Rac . Having shown that Sos is required for Robo-dependent cell spreading , we sought to examine the capacity of Robo to direct the subcellular localization of endogenous Sos in response to Slit treatment . Extracting the feature of endogenous Sos fluorescence intensity in processes reveals an increase in signal in Slit CM ( Fig 6D ) over Control CM-treated Robo-expressing cells ( Fig 6C and 6I ) , consistent with recruitment of Sos to processes in response to Slit treatment . Not only is Sos required for process elaboration in response to Slit , and actively recruited into the processes in cells treated with Slit , but it also it is also localized to regions previously shown to carry hallmarks of endocytic activity ( reduction in surface receptor levels ( Fig 4B ) and receptor colocalization with an early endosomal marker ( Fig 5E ) ) . Peaks in endogenous Sos signal in Slit CM processes occur at varicosities and branchpoints ( arrowheads Fig 6D’ and 6F’ ) , the same structures that are enriched for markers of endocytic activity . Further evidence that Sos recruitment to processes depends on Slit binding comes from the observation that deleting the Ig1 domain or deleting the CC2 and CC3 domains also block Sos recruitment ( Fig 6E–6F’ ) . Finally , inhibiting Clathrin-dependent endocytosis also abrogates the increase in endogenous Sos signal intensity in Slit-CM- treated processes over Control CM-treated processes ( Fig 6G–6H’ ) , consistent with a model in which Sos recruitment depends on , and therefore occurs following , Clathrin-dependent endocytosis of the Robo receptor in response to Slit-binding . Next , to test whether Robo endocytosis is important for its activation in vivo , we assayed these Robo constructs that are defective in Clathrin-dependent endocytosis for their midline guidance activity . First , we overexpressed either wild-type or mutant Robo transgenes in an otherwise wild-type background in two ectopic repulsion assays . All of the transgenes that we used were tagged with an HA epitope , inserted in the same genomic site and were expressed at comparable levels based on immunostaining for their HA epitope tags ( Fig 7I–7K ) . Driving expression of wild-type Robo in all neurons ( Fig 7B ) is sufficient to signal repulsion so strongly that we see 76% of embryonic segments do not form commissures ( Fig 7L ) . In contrast , none of our endocytosis-defective deletion constructs are able to disrupt midline crossing when similarly expressed ( Figs 7C , 7D , 7L and S5E ) . We see a similar requirement for endocytosis motifs in a commissural subset of axons- the EW axons- whose projection pattern is imaged in Fig 7E with GFP and schematized on the right as a crossed fascicle . Overexpressing wild-type Robo specifically in this subset ( Fig 7F and 7I ) causes ectopic repulsion from the midline ( Fig 7M ) . In contrast , Robo missing its endocytosis motifs ( Figs 7G–7K , 7M , S5F and S5G ) does not cause ectopic repulsion , consistent with a requirement for endocytosis of the Robo receptor for its repulsive midline guidance activity in vivo . If these AP-2 interaction motifs are indeed required for repulsive signaling then one would predict that over-expressing them might compete with endogenous receptors for access to ligand , thereby acting as a dominant-negative for midline repulsion . Accordingly , in embryos with reduced Slit dosage , expressing a Robo transgene missing both its AP-2 motifs , like that missing its entire C-terminus , does inhibit midline repulsion causing ectopic crossing of the medial-most FasII fascicles ( S5B–S5D Fig ) . Finally , to further assess the in vivo repulsive function of these receptor variants , we compared the ability of wild-type versus endocytosis-deficient Robo transgenes to rescue the loss of repulsion defects in robo mutant embryos in two normally ipsilateral subsets of axons . The FasII-positive axons project in three ( Fig 8A ) , and the Ap axons project in one fascicle ( Fig 8F ) , on either side of the midline . In robo mutants the medial-most pair of FasII , and both Ap , fascicles collapse onto the midline ( Fig 8B and 8G ) . Adding back wild-type Robo transgene either in all neurons or specifically in the Ap subset ( Fig 8C and 8H ) is sufficient to restore the ipsilateral projection pattern of these axons . In contrast , expressing Robo transgenes missing the AP-2-binding motifs , either singly or together , cannot rescue the midline crossing errors in robo mutants when expressed in all neurons ( Fig 8D and 8E ) or specifically in the Ap ipsilateral subset ( Fig 8I and 8J ) , consistent with a requirement for Robo endocytosis in its repulsive guidance function in vivo . We note that deleting Robo’s AP-2 binding motifs more greatly impairs midline guidance activity than process outgrowth in our in vitro activation assay , suggesting a functional dissociation in the underlying mechanisms of S2R+ process outgrowth . In the future it will be interesting to determine the effect of these small receptor manipulations on dissociated Drosophila growth cone responses to Slit-binding .
In contexts other than axon guidance , endocytic trafficking has been demonstrated to contribute to receptor signaling by allowing receptor recruitment to specific subcellular compartments . In the case of Wingless [51] , Notch [52] , EGFR and PVR [53] and VEGFR2 [54] , receptor activation is regulated by entry into the early endosome in response to ligand-binding at the surface . Regulation of receptor activation by entry into endocytic compartments can occur by gating spatial access to downstream effectors encountered in signaling complexes–such as Rac or CDC42 in the early endosome [55 , 56] , and MEK1 in the late endosome [57] , reviewed in [58] . These observations lend precedent to a model in which endocytic trafficking gates Robo’s spatial access to downstream effectors , such as Sos . The subcellular localization pattern of Slit , Robo , Rab5 and Sos in our in vitro process elaboration assay support this model; Slit and Robo-C terminal tag demarcate- with their peaks in fluorescence intensity- varicosities and nascent branch points along processes at the 2’ early time point ( arrowheads in Figs 5B , 5E and S4B ) which at 10’ become annexes within branch points ( arrowhead in Figs 3A and S4D ) . Within these enlargements occur correlated valleys in surface Robo signal ( arrowhead , Fig 4B ) and peaks in markers of both early endosome , Rab5 ( arrowheads , Figs 4B , 4E and S4B–S4D ) and Sos ( arrowheads , Fig 6D ) . Taking the formation of branchpoints to be the readout of repulsive signaling in the process elaboration assay , we propose that Slit binds to Robo to induce recruitment of both Rab5 and Sos to create what become hubs of endocytosis activity within two minutes , a timepoint previously verified as required for Clathrin-dependent endocytosis in S2R+ cells and in growth cones [59 , 60] . In this model , Slit binding to the cell is instructing the spatial location of Robo internalization to the early endosome and recruitment of its downstream effector Sos . Consistent with this , when Clathrin-dependent endocytosis is inhibited , Slit binding is intact , but fails to induce the recruitment of Rab5 and therefore there is a correlation between loss of both translocation of Robo from the cell surface to the early endosome , and decreased Sos recruitment . Our data are consistent with a model in which endocytic trafficking is mechanistically contributing to Robo’s activation by fully or partially gating access to its downstream effector Sos . Evidence from the literature suggests that Sos recruitment might not occur exclusively at the surface of the cell as we had previously reported [31 , 48] , but also in closely apposed early or late endosomal compartments [61] . Sos encodes a Pleckstrin Homology ( PH ) Domain just C-terminal to the Dbl Homology ( DH ) domain that is required for both its RacGEF function in Slit/Robo midline guidance in the fly [48] , and for in vitro Robo activation ( Fig 6B ) . PH domains bind phosphoinositols ( PI ) of the plasma membrane or small GTPases , and are invariably found adjacent to DH domains , strongly suggesting a functional link between DH and PH activity . In the case of Sos the PH domain has been suggested to act as a mechanical switch to allow initiation of the RacGEF activity of the DH domain upon conversion of a bound PIP2 to PIP3 by PI3Kinase ( PI3K ) [62] . Phosphoinositides have also been linked to early endosome fusion; Rab5 actively recruits PI3K , which in turn is required for Rab5-mediated conversion of plasma membrane to early endosome [63 , 64] . It will be interesting to determine whether Sos activation downstream of Robo is gated by PI3K in concert with recruitment to the Rab5-positive early endosome , as this would provide a mechanism by which Robo activation requires Clathrin-mediated endocytosis and Rab5 activity . At first glance , the ability of Robo to induce elaboration and branching of cell processes in vitro may seem inconsistent with a repulsive output; however , our rescue and gain of function genetic data make a strong case that the signaling output that we observe in vitro is critical for repulsion in vivo . In addition , there is ample precedent for Slit/Robo signaling to induce branching in both in vitro and in vivo contexts . For sensory afferents that bifurcate and send collaterals into iterative segments of the spinal cord , uniform Slit treatment induces branching in vitro either in suspension cultures of Rat DRGs in collagen gels or bath application to rodent trigeminal neurons [4 , 65] . The branched morphology of the peripheral arbor of trigeminal projections to the eye requires Slits and Robos [10] . Interestingly , bath application of Slit is sufficient to induce Robo1-dependent growth and branching of dendritic fields of mouse cortical neurons [66] , similar to our observations of Slit-induced branching and process growth in S2R+ cells . Since Robo is enriched in growth cone filopodia it is likely that during active migration Robo-containing filopodia would mediate adhesive interactions with Slit in the extracellular matrix . Subsequent Slit-induced filopodial retraction likely requires more than the filopodial dynamics provided by Ena- a Robo effector that is known to localize to the distal tips of filopodia [67 , 68] , since Robo missing its CC2 domain is not fully deficient for midline repulsion [47] . A commonality between our in vitro activation assay and previous analyses of growth cone collapse in culture may suggest a possible mechansim . Filopodial contact of a sympathetic growth cone to a retinal neurite is sufficient to initiate an increased rate of growth cone movement- a rapid retraction of an actin-rich structure along the existing axon [69] , all while filopodia stay attached , suggesting the existence of a retrograde cue from the filopodial point of contact to more proximal growth cone structures . Similarly , fixed imaging analysis of S2R+ cells in our assay reveals that bath-treatment of Slit CM stimulates the extension and branching of processes over those observed in Control CM . Given that the process elaboration response we observe requires the RacGEF domain of Sos , it is likely that the increased rate of motility implicit in the growth upon Slit treatment is due to alterations in Rac-dependent actin dynamics . Since the process elaboration and branching behavior also requires endocytic trafficking from the cell surface to the late endosome , we can speculate that Robo endocytosis is required to direct the Sos-induced actin motility required for spreading in vitro . It is the same receptor manipulations that abrogate Clathrin-dependent endocytosis in vitro that lead to impaired repulsive signaling in vivo , strongly supporting the idea that Robo endocytosis is required for proper repulsive output in the growth cone , perhaps by allowing the actin-based motility that leads to filopodial retraction and growth cone repulsion . Sequence analysis reveals putative AP-2 binding motifs in human Robo1 ( S2J Fig ) , suggesting conservation of the mechanistic contribution of endocytosis to growth cone navigation all the way to humans , further strengthening the possibility of the importance of this trafficking event . Might Slit-binding trigger a similar endocytic trafficking cascade in a growth cone , thereby mobilizing Robo so that it could serve as the retrograde cue informing growth cone behavior from the tips of filopodia ? Evidence from others shows that Clathrin-dependent endocytosis exists in the right time and place to play such a role in guidance behavior . First , markers of endocytic compartments , including the early endosome , have been identified in the growth cone [70 , 71] . If endocytosis serves as a general mechanism for expanding the spatial range of an activated receptor after exposure to ligand on filopodial tips , then we would expect to see examples of correlation between guidance cues trafficking retrogradely and guidance behavior . Endocytosis of guidance molecules in the growth cone has been shown to be initiated both from the base of the growth cone central domain and from the tips of filopodia [23 , 72] . Retrograde movement of endocytic compartments has been reported in the growth cone and in the case of internalized L1CAM movement occurs at the rate of F-actin retrograde flow [73 , 74] , suggesting that endocytic trafficking could provide an effective spatial track from which a guidance cue might influence the cytoskeleton to affect growth cone behavior . The timing reported by others of endocytosis in the growth cone also shows correlation with the endocytic trafficking of Robo we characterize here in vitro . At the same two minute timepoint we report Slit induces Robo removal from S2R+ cell surface here , Sema-3A has affected both a reduction in Neuropilin-1 levels [21 , 60]–and growth cone collapse in the Xenopus RGC growth cone , albeit with different ligand concentrations [75] . Finally , Frizzled endocytosis in a migrating growth cone reveals a correlation between filopodial dynamics and Frizzled endocytosis [23] . It remains to be determined whether retrograde Robo movement from the tips of filopodia is required for repulsion in response to Slit . Finally , here we have addressed how an endocytic cascade positively contributes to signaling from the Robo receptor , effectively expanding the spatial range of activated receptor within the growth cone . While allowing exposure to the machinery within the growth cone beyond filopodial tips would be required for behaviors such as growth cone retraction or turning in response to filopodial contact with Slit , allowing a receptor to signal too far from the spatial origin of its cue might ultimately prove confusing to a growth cone . It will be interesting to learn if there is a process that serves to curtail signaling from an endocytosed and activated receptor .
The following Drosophila mutant alleles were used: roboGA285 , roboz1772 , robo5 , slit1 , slit2 , slite158 , endoAEP297 , endoA∆4 , endoA10 , ada1 , ada3 , rab52 , P[lacW]Rab5k08232 , P[EPgy2]Rab7EY10675 , rab7FRT82B ( knock-out ) . The following transgenes were used: P[UAS-Shi . K44A]4–1;UAS[shi . K44A]3–7 , P[UASp-YFP-Rab5 . S43N] , P[UASp-YFP-Rab7 . T22N]06 , P[UASp-YFP-Rab4 . S22N]37 , P[UASp-YFP-Rab11 . S25N]35 . The following transgenic flies were generated by BestGene Inc ( Chino Hills , CA ) using ΦC31-directed site-specific integration into landing sites at cytological position 86F8 ( controlling for expression level effects from chromosomal position ) : P[5xUAS-3xHA-Robo-6xmyc] , P[5xUAS-3xHA-Robo∆YLQY-6xmyc] , P[5xUAS-3xHA-Robo-1xmCherry] , P[5xUAS-3xHA-Robo∆YQAGL-1xmCherry] , P[5xUAS-3xHA-Robo∆YQAGL-6xmyc] , P[5xUAS-3xHA-Robo∆YLQY∆YQAGL -6xmyc] , P[10xUAS-3xHA-Robo∆Ig1] , P[10xUAS-3xHA-Robo∆C-6xmyc] , P[10xUAS-3xHA-Robo∆YLQY∆YQAGL-6xmyc] . Also used were the extant lines P[GAL4-elav . L]3 ( elav-GAL4 ) , egMZ360 ( eg-GAL4 ) , ap-GAL4 . Embryos were genotyped using balancer chromosomes carrying lacZ markers or by the presence of epitope-tagged transgenes . Control and Slit CM were boiled for 10’ in 2X SDS Loading Buffer . Proteins were resolved by SDS Page and transferred to nitrocellulose and incubated with anti-Slit-C ( C555-6D ) 1:100 overnight at 4°C in PBS/0 . 05% Tween-20/5% non-fat dry milk . Blots were incubated with HRP-conjugated anti-mouse secondary antibody for 1 hour at RT and signal was detected using ECL Prime ( Amersham ) . | The formation of sterotyped neuronal connections during embryonic development is essential for animal survival and behavior . In particular , establishing proper connectivity at the midline is critical for the orchestration of rhythmic behaviors . Conserved genetic programs that instruct axon guidance at the midline have been characterized in multiple model systems , but the signaling mechanisms underlying axon guidance are not well understood . Slits and Robos comprise conserved families of axon guidance cues and receptors that control midline guidance by preventing inappropriate midline crossing . Here , we identify a novel mechanism that is required for Robo receptor activation and Robo-dependent axon repulsion in vivo . Using a combination of molecular genetic and cell biological approaches , we define a role for Slit-dependent trafficking of Robo from the plasma membrane to the early and late endosomes that contributes to Robo activation and signaling . In previous work , endocytic trafficking has been shown to modulate axon guidance responses by regulating surface levels of guidance receptors . In contrast , our observations indicate that endocytosis of the Robo receptor itself is required for receptor activation and precedes the recruitment of a key downstream signaling effector to the Robo receptor cytoplasmic domain . | [
"Abstract",
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] | [] | 2015 | Slit-Dependent Endocytic Trafficking of the Robo Receptor Is Required for Son of Sevenless Recruitment and Midline Axon Repulsion |
A key epidemiologic feature of schistosomiasis is its focal distribution , which has important implications for the spatial targeting of preventive chemotherapy programs . We evaluated the diagnostic accuracy of a urine pooling strategy using a point-of-care circulating cathodic antigen ( POC-CCA ) cassette test for detection of Schistosoma mansoni , and employed simulation modeling to test the classification accuracy and efficiency of this strategy in determining where preventive chemotherapy is needed in low-endemicity settings . We performed a cross-sectional study involving 114 children aged 6–15 years in six neighborhoods in Azaguié Ahoua , south Côte d’Ivoire to characterize the sensitivity and specificity of the POC-CCA cassette test with urine samples that were tested individually and in pools of 4 , 8 , and 12 . We used a Bayesian latent class model to estimate test characteristics for individual POC-CCA and quadruplicate Kato-Katz thick smears on stool samples . We then developed a microsimulation model and used lot quality assurance sampling to test the performance , number of tests , and total cost per school for each pooled testing strategy to predict the binary need for school-based preventive chemotherapy using a 10% prevalence threshold for treatment . The sensitivity of the urine pooling strategy for S . mansoni diagnosis using pool sizes of 4 , 8 , and 12 was 85 . 9% , 79 . 5% , and 65 . 4% , respectively , when POC-CCA trace results were considered positive , and 61 . 5% , 47 . 4% , and 30 . 8% when POC-CCA trace results were considered negative . The modeled specificity ranged from 94 . 0–97 . 7% for the urine pooling strategies ( when POC-CCA trace results were considered negative ) . The urine pooling strategy , regardless of the pool size , gave comparable and often superior classification performance to stool microscopy for the same number of tests . The urine pooling strategy with a pool size of 4 reduced the number of tests and total cost compared to classical stool microscopy . This study introduces a method for rapid and efficient S . mansoni prevalence estimation through examining pooled urine samples with POC-CCA as an alternative to widely used stool microscopy .
Schistosomiasis is a disease caused by parasitic worms of the genus Schistosoma , and affects over 250 million people residing in the world’s poorest regions [1] . Historically , treatment programs have focused on control of disease morbidity [2–4] . However , the focus of these programs has recently shifted toward a goal of interrupting disease transmission and local elimination of helminth infections [4] . The World Health Organization ( WHO ) recommends a strategy of preventive chemotherapy ( often known as ‘mass drug administration’ ) for control and elimination of schistosomiasis [2 , 3] . The preventive chemotherapy program provides widespread empiric treatment with praziquantel , and traditionally focuses upon school-aged children [2 , 3] . The frequency of preventive chemotherapy , if necessary , is based upon the infection prevalence [2 , 3] . Most schistosomiasis control programs utilize prevalence estimates from survey samples of selected schools to guide treatment decisions for preventive chemotherapy [2 , 3] . The WHO currently recommends surveying one sentinel site ( i . e . , at least 50 children in one school ) per 200 , 000–300 , 000 children in a homogeneous “ecological zone” [3] . For each child in a sentinel site , a fecal and urine sample is obtained . The stool is prepared using the Kato-Katz thick smear method and examined under a microscope ( referred to as traditional stool microscopy ) for the eggs of intestinal schistosomiasis ( caused by Schistosoma mansoni and S . japonicum ) . The urine is tested for microhematuria or filtered and examined under a light microscope for the eggs of S . haematobium . These strategies are the current standard for prevalence estimation , but require laboratory infrastructure , trained personnel for slide preparation and interpretation , lack adequate sensitivity to detect low intensity infections , and are time- and resource-intensive [3 , 5 , 6] . These barriers have contributed to the challenge of mapping the prevalence of schistosomiasis in endemic countries , which is necessary to inform where preventive chemotherapy should be implemented . Furthermore , a high-resolution understanding of local prevalence is especially important for schistosomiasis compared to other helminth infections , because schistosomiasis is highly geographically focal due to dependence on freshwater for completion of the life cycle [7] . The pooling of urine samples with the point-of-care circulating cathodic antigen ( POC-CCA ) cassette test may provide a cost-efficient alternative to traditional stool microscopy . The pooling of biological specimens ( including feces and urine ) as an efficient methodology for disease screening is common across veterinary parasitology , HIV , and other diseases [8 , 9] . The POC-CCA test is a rapid diagnostic test that uses urine for the binary detection of S . mansoni . Most notably , the POC-CCA test retains high sensitivity at low intensity infections and does not read positive once an infection is resolved making it suitable for application in pooled sampling [5 , 10–12] . However , there are currently no studies that have examined the diagnostic characteristics , optimal pooling size , or cost-efficiencies associated with a urine pooling methodology using the POC-CCA cassette test . Furthermore , since the preventive chemotherapy strategy is based upon a broad prevalence categorization ( WHO groups settings by <10% , 10–50% , or >50% prevalence ) rather than a specific prevalence value , a simplified classification tool can be employed . Lot quality assurance sampling ( LQAS ) is an approach to evaluate the accuracy of classification of unknown entities into binary or multiple groups , according to pre-defined thresholds [13–15] . This tool has been well characterized in the helminthiasis literature [13–15] , and provides an attractive option for estimating the broad prevalence category to reduce number of tests and cost . To address this critical need , we conducted an empirical evaluation of the accuracy of urine pooling with the POC-CCA cassette test , and then applied simulation modeling using a LQAS framework to evaluate classification accuracy and efficiency for informing targeted treatment of schistosomiasis .
This study was approved by the Institutional Review Board for Human Subjects Research at Stanford University School of Medicine ( Stanford , CA , United States of America ) and ethical clearance was obtained from the Ministry of Health and Public Hygiene of Côte d’Ivoire ( CNER , reference no . 037/MSLS/CNER-dkn ) . We obtained written informed consent from parents/guardians and oral assent from children in Azaguié Ahoua , Côte d’Ivoire . All data were coded and treated as confidential personal health information . After completion of the study , all study participants received praziquantel ( single 40 mg/kg oral dose ) and albendazole ( single 400 mg oral dose ) at no cost as per national guidelines [2 , 3] . This cross-sectional study was performed in August 2015 at schools from six neighborhoods in the Azaguié health district ( geographic coordinates: 5° 37' 40" N latitude and 4° 5' 12" W longitude ) of Côte d’Ivoire , located at 40 km north from Abidjan . These settings ranged from moderate to high endemicity for S . mansoni to ensure a sufficient number of positive urine and stool samples were obtained . We selected 180 children between the ages of 6 and 15 years , informed by sample size estimations for diagnostic tests ( S1 Text ) [16] . A detailed census was carried out in early August 2015 to determine the number of school-aged children per neighborhood in Azaguié Ahoua village . Based on that list , 30 children were randomly selected per neighborhood . The purpose and procedures of the study were explained to the village and health authorities . For children who provided oral assent , and whose parent/guardian provided written informed consent , we obtained two stool and two midstream urine samples over two days . Urine was collected between 10:00 and 12:00 hours . For both stool sample collected over consecutive days , duplicate Kato-Katz thick smears were prepared the same day as collection following standard protocol , for a total of four Kato-Katz thick smears per child [17] . Slides were labeled with a de-identified code and read by experienced laboratory technicians using light microscopy . The presence and quantity of helminth eggs was counted on each slide for S . mansoni . For quality control purposes , we randomly selected 10% of the Kato-Katz thick smears , including both positive and negative slides , for re-examination by a senior technician . If discrepancies above the tolerance margin were noted , the results were discussed with the technicians and the slides were read a third time to reach agreement ( S1 Text ) . All of the first day urine samples were tested using the POC-CCA cassette test ( Rapid Medical Diagnostics; Pretoria , South Africa , batch #50174 ) on the same day of sample collection . To perform the test , one drop of urine was placed into the POC-CCA cassette well followed by one drop of the test buffer . Two blinded study personnel experienced with POC-CCA read the tests independently 20 min after the addition of buffer . In cases of disagreement , a third blinded technician read the results and a decision was made based on agreement of at least two out of three individuals . Tests were read as negative , trace positive , 1+ , 2+ , or 3+ according to the color intensity of the test band , and tests that did not develop the control band were repeated . For the pooling of urine samples , we first identified individuals as positive or negative using quadruplicate Kato-Katz thick smears and a single POC-CCA test . Positive samples were from children with a positive Kato-Katz and POC-CCA test , while negative samples were from children with a negative Kato-Katz and POC-CCA test . Hence , we only included samples with concordant results from both tests . One positive urine sample ( ~5 ml ) was then combined with equal volumes of three ( n = 4 ) , seven ( n = 8 ) , or 11 ( n = 12 ) negative urine samples . All samples were poured into a urine collection container , and a brief mixing step was done with the disposable pipette provided with the POC-CCA test kit . We then performed the POC-CCA test as described above . We calculated the sensitivity and specificity of quadruplicate Kato-Katz thick smears and the individual POC-CCA test using latent class analysis [18] . This analytical strategy combines prior knowledge on sensitivity and specificity and the observed data to simultaneously calculate point estimates and 95% credible intervals around the sensitivity and specificity for two or more diagnostics tests without assuming any test as the ‘gold’ standard [18] . We used the Gibbs sampler model for two diagnostic tests with assumption of independence , and derived a prior distribution following consensus from literature using a beta distribution ( S1 Text ) [5 , 10 , 12 , 18 , 19] . Only children with complete data records ( i . e . , quadruplicate Kato-Katz thick smears and POC-CCA test ) were included in the final analysis . We calculated eggs per gram of feces ( EPG ) from the four Kato-Katz thick smears using an arithmetic mean and the conventional 24-fold multiplier [20] . Sensitivity of the pooled samples was calculated assuming that a single positive urine sample classified the entire pool as positive . We used a logistic regression to model the sensitivity of pooled urine samples ( with pool sizes of 4 , 8 , and 12 ) as a function of the EPG of the one infected urine . For each individual , we defined the independent variable as the arithmetic mean EPG from quadruplicate Kato-Katz thick smears and the dependent binary outcome as whether or not the pooled urine was read as positive with the POC-CCA cassette test . All analyses were conducted by considering the POC-CCA trace result as positive ( POC-CCA ( tr+ ) ) and negative ( POC-CCA ( tr- ) ) to examine the impact of this interpretation on results [2 , 3] . We developed an individual-level stochastic decision analytic model ( first order Monte Carlo simulation; microsimulation ) for diagnosis of schistosomiasis to test the performance of the urine pooling strategies using the LQAS classification tool . We compared five strategies: ( i ) duplicate Kato-Katz thick smears ( WHO standard ) ; ( ii ) single POC-CCA test; ( iii ) pooled POC-CCA test ( n = 4 ) ; ( iv ) pooled POC-CCA test ( n = 8 ) ; and ( v ) pooled POC-CCA test ( n = 12 ) . A simulated cohort of 100 , 000 individuals was created for each prevalence value ( 0–20% ) , with assignment of infection status and EPG ( when applicable ) to each person . We assumed a negative binomial statistical distribution , and used a specified prevalence and inferred infection intensity to simulate the distribution of egg counts in the cohort ( S1 Text ) [21] . In the microsimulation , a sample of individuals was randomly selected from the simulated cohort . Each individual was assigned to one of four mutually exclusive states ( true positive , true negative , false positive , or false negative ) using the computed sensitivity and specificity of the respective diagnostic strategy and knowledge of an individual’s “true” infection status ( from simulated egg counts ) . The sensitivity ( in relation to infection intensity ) and specificity for duplicate Kato-Katz thick smears for individual samples was derived from literature [5] , while we used the sensitivity ( irrespective of infection intensity ) and specificity from the latent class analysis for single POC-CCA . For pooled POC-CCA test strategies with more than one positive sample , we conservatively used the total EPG count to relate the infection intensity to our pooling data ( in which only one urine was infected ) and computed sensitivity using the logistic model ( S1 Text; Fig A1–A2 in S1 Text ) . To address the impact of urine pooling on specificity , we used the POC-CCA specificity estimate from the Bayesian latent class analysis ( which assumes no diagnostic ‘gold’ standard ) , and accounted for the increased probability of including a false positive by adding more urine samples that could be false positives . We assumed the effect of diluting a false positive urine sample was comparable to diluting a light-intensity infected urine sample ( S1 Text ) . Ultimately , the dilution effect counteracted the potential for decreased specificity from sample pooling ( S1 Text ) . As preventive chemotherapy strategies are based on classifying regions into a prevalence bin ( i . e . , above or below a predefined prevalence threshold ) , we chose our primary outcome as the probability of correct binary classification around one prevalence threshold . Specifically , we tested the WHO-recommended prevalence threshold for preventive chemotherapy against schistosomiasis ( 10% prevalence ) [2 , 3] . We defined classification certainty as the proportion of correctly categorized settings within 5% of the prevalence threshold , following estimates from prior studies [14] . The decision rule was chosen based on the median number of positive tests from the microsimulation at the prevalence ( 10%; S1 Text ) . The microsimulation was run 10 , 000 times for each strategy using a range of 15 to 500 tests for each strategy . We estimated the number of correct categorical classifications for each strategy at all prevalences . We also tested the potential to reduce the number of tests with the urine pooling strategies compared to stool microscopy , while maintaining the same level of classification accuracy . We compared our results against previous analytical approaches to LQAS [14] , which did not account for imperfect test accuracy . A loess algorithm was applied for visualization and interpolation . The cost of each strategy was estimated from recent literature , incorporating costs for supplies , labor , and pooling . We added an additional US$ 0 . 50 per extra sample in the pooling strategy to account for personnel time and collection container costs ( S1 Text ) [6] . The total cost for POC-CCA pool sizes of 4 , 8 , and 12 urines was estimated at US$ 6 . 63 , US$ 8 . 63 , and US$ 10 . 63 , respectively; Kato-Katz was estimated at US$ 3 . 99 ( S1 Text ) . Data were recorded in a Microsoft Excel spreadsheet , and statistical analysis and data visualization were performed with Python and R 3 . 1 . 1 ( R Foundation for Statistical Computing; Vienna , Austria ) . The authors support the importance of data sharing and transparency in research; hence , full model code and data are available upon request to the corresponding author . We performed a series of sensitivity analyses to assess the robustness of our findings . We conducted one-way sensitivity analyses to examine the effect of individual parameters on the total cost of the urine pooling strategy for a 90% level of certainty in classification . We varied number of tests , cost estimates , sensitivity , specificity , and the dilution effect on light infections . We also tested the robustness of results against setting-specific epidemiologic differences , where the EPG distribution was varied for the same prevalence . We repeated the main analysis using a lower proposed prevalence threshold based on recent cost-effectiveness modeling ( 5% prevalence instead of the current 10% WHO cutoff ) [21 , 22] .
From the 114 school-aged children with complete data , 59 . 6% were positive by quadruplicate Kato-Katz thick smears , 69 . 3% were positive by single POC-CCA ( tr+ ) , and 50 . 0% were positive by single POC-CCA ( tr- ) ( Table 1 ) . Using a latent class model , we estimated the sensitivity and specificity of quadruplicate Kato-Katz thick smears at 78 . 2% ( 95% CI: 71 . 0–84 . 5% ) and 96 . 1% ( 95% CI: 90 . 8–98 . 8% ) , respectively ( Table 2 ) . We estimated the sensitivity of POC-CCA ( tr+ ) test at 95 . 1% ( 95% CI: 89 . 2–98 . 5% ) and POC-CCA ( tr- ) test at 74 . 4% ( 95% CI: 63 . 8–83 . 7% ) . The specificity of POC-CCA ( tr+ ) test was 82 . 9% ( 95% CI: 71 . 5–91 . 6% ) and POC-CCA ( tr- ) test was 94 . 5% ( 95% CI: 86 . 9–98 . 5% ) . The overall sensitivity of the urine pooling strategy ( POC-CCA ( tr+ ) ) was 85 . 9% , 79 . 5% , and 65 . 4% for pool sizes of 4 , 8 , and 12 ( Table 3 ) . For the urine pooling strategy ( POC-CCA ( tr- ) ) , the sensitivity was 61 . 5% , 47 . 4% , and 30 . 8% for pool sizes of 4 , 8 , and 12 . The modeled specificity ranged from 94 . 0–97 . 7% for the urine pooling strategies . The sensitivity of each strategy increased for detection of moderate and heavy infections . The sensitivity of each pooled strategy ( POC-CCA ( tr- ) ) was modeled as a logistic function with respect to infection intensity , measured in EPG ( Fig 1 ) . Sensitivity was strongly positively correlated with EPG and was negatively associated with larger pool size , particularly at lower infection intensities . We used a microsimulation to evaluate the diagnostic performance of three urine pooling strategies ( n = 4 , 8 , and 12 pooled samples ) to give a binary prediction for informing preventive chemotherapy programs in simulated cohorts across a prevalence range from 0 to 20% . The three pooling strategies gave comparable , and often superior , classification performance to traditional stool microscopy for the same number of tests ( Figs 2 and 3 ) . As expected , classification error for all strategies was highest when the true prevalence was near the prevalence threshold , and classification improved further away from the prevalence threshold . For 80% and 90% certainty of correct classification of communities ( ± 5% around the 10% prevalence threshold ) , traditional stool microscopy required 71 and 150 tests , while urine pooling ( n = 4 ) needed 33 and 67 tests , and urine pooling ( n = 8 ) needed 29 and 73 tests , respectively . The urine pooling strategies ( n = 4 , 8 , and 12 ) reduced the number of tests , while achieving the same accuracy as traditional stool microscopy across the full range of certainties in classification ( Fig 3 ) . Only the pooling strategy ( n = 4 ) demonstrated cost savings compared to traditional stool microscopy . The pooling strategies ( n = 8 and 12 ) did not reduce the total cost . This result remained robust when evaluating the number of tests and total cost per correctly classified school ( S1 Text ) . The one-way sensitivity analyses found that our primary finding–urine pooling strategy ( n = 4 ) yielded cost savings when compared to stool microscopy–was robust in the majority of alternative assumption on pooling cost , setting-specific epidemiologic differences , sensitivity , and specificity except on the upper ranges ( Fig 4 ) . Under some assumptions , pooling 8 samples also yielded cost savings . The overall study findings were comparable when using a prevalence threshold of 5% ( S1 Text ) .
This study found that a urine pooling strategy using the commercialized POC-CCA test could be more efficient than individual-based surveys with traditional stool microscopy in informing where preventive chemotherapy against schistosomiasis is needed in low-endemicity settings . We characterized the sensitivity and specificity of urine pooling with the POC-CCA test across multiple dilutions , and used a statistical classification tool ( lot quality assurance sampling ) to operationalize this pooling strategy as a binary predictor of whether or not preventive chemotherapy is needed in low-endemicity settings . While sensitivity declined , as expected , at higher pool sizes , this loss was offset by the efficiency gains in screening multiple samples simultaneously . Through simulation modeling , we found that the pooling strategy reduced the number of tests and total cost , while achieving the same performance as traditional stool microscopy . These findings support the need for further validation of the urine pooling strategy in low-endemicity and near-elimination settings as a rapid , cost-saving alternative to traditional stool microscopy . As the global strategy shifts from morbidity control to a goal of disease elimination and treatment programs are expanded , high-resolution mapping of where schistosomiasis is prevalent is crucial , especially since this disease is highly geographically focal [4 , 23] . Additionally , some settings will reduce their prevalence to below the 10% threshold in the school-aged child population , and hence , preventive chemotherapy would no longer be indicated . Once settings do approach elimination , rigorous monitoring and surveillance will be essential to detect disease rebound . For all these reasons , a rapid and inexpensive approach to inform settings on the need for preventive chemotherapy will be crucial . We evaluated the diagnostic performance and cost of a pooled urine methodology with multiple pool sizes ( n = 4 , 8 , and 12 ) . The use of sample pooling for diagnostic screening of parasitic and other infectious diseases has been previously demonstrated [8 , 9] . We found that the urine pool size of 4 performed optimally with a balance of good sensitivity and specificity and relatively low cost . Across a range of certainties in classification , the urine pooling strategy ( n = 4 ) reduced the number of tests and total cost with the same performance as traditional stool microscopy . The pool sizes of 8 and 12 had lower sensitivity , and ultimately offered limited test or cost savings except under select conditions . In this analysis , we treated POC-CCA trace positive test results as negative , which resulted in lower sensitivity but higher specificity . While treating trace positive results as positive greatly improved sensitivity , this resulted in substantially more false positives , which was compounded by the effect of sample pooling . We also evaluated single POC-CCA with the latent class model , which does not assume any diagnostic ‘gold’ standard test , and found our results for sensitivity and specificity in broad agreement with previous studies [5 , 12 , 19 , 24] . Notably , our POC-CCA specificity estimate was lower than previous estimates making our modeling results conservative [5 , 12 , 24] . The LQAS classification tool–which is used for binary categorization based on a pre-defined threshold–is a useful method to reduce sampling effort and maintain accuracy [13 , 14] . Our study focused on providing a binary classification around the 10% prevalence threshold ( within ±5% prevalence ) for schistosomiasis , which is the threshold that school-based preventive chemotherapy is recommended . We did not assess multiple category-LQAS since the pooling strategy is best suited for binary classification , and the 10% threshold allowed for identification of settings where treatment would be indicated . Rigorous study has been given to the application of LQAS in classification of helminth prevalence [13 , 14] , although previous studies have assumed perfect sensitivity and specificity and estimated a sample size of 15 tests for 80% certainty ( ± 6 . 5% around the 10% prevalence threshold ) . Under these conditions , our microsimulation corroborated the estimated sample size . However , when we incorporated imperfect diagnostics , we found that an increased sample size was needed . For 80% and 90% certainty , traditional stool microscopy required 71 and 150 tests , while urine pooling ( n = 4 ) needed 33 and 67 tests . This suggests the importance of accounting for imperfect test characteristics and random sampling in LQAS calculations , and the utility of urine pooling to decrease the number of necessary tests . Notably , sampling effort for both traditional stool microscopy and urine pooling with the LQAS tool is still lower than current WHO recommendations that suggest 250–500 school-aged children ( 50 per school ) [2 , 3 , 14] . This demonstrates the value of LQAS to decrease sampling effort , total cost , and time necessary to correctly classify a setting . We evaluated a sample pooling strategy with the POC-CCA urine cassette tests , which is designed to detect S . mansoni and performs poorly for detection of S . haematobium [25] , although S . haematobium is often geographically overlapping and can co-infect individuals in sub-Saharan Africa [26] . Since a sample pooling strategy necessitates a test with high sensitivity , we focused our study in settings endemic with S . mansoni . Future work can examine incorporation of the circulating anodic antigen ( CAA ) test or polymerase chain reaction ( PCR ) -based methods that can detect multiple species of Schistosoma to provide a comprehensive strategy [27–31] . Molecular-based ( e . g . , PCR ) techniques are an attractive option for use in pooling strategies because of their high sensitivity ( >90% ) and specificity , although current methods may be too costly and require advanced laboratory infrastructure that are often out of reach in resource-constrained settings where schistosomiasis is endemic [28–31] . The findings of this investigation should be understood within the limitations of the study design and model assumptions . We simulated theoretical settings by deriving a generalized epidemiologic relationship between prevalence , infection intensity , and parasite dispersion within the population based upon real data [21] . We assumed a negative binomial distribution of disease , which is based upon empiric observation and common practice [21 , 22 , 32 , 33] . To calculate the specificity of the urine pooling strategy , we conservatively assumed that false positives would dilute identically to true positives , although future work should investigate this assumption . While the cost of pooling is uncertain , we assumed each extra pooled sample would incur an additional US$ 0 . 50 cost and varied this in a sensitivity analysis with our primary findings remaining robust . We defined certainty as the proportion of correct classification ±5% prevalence around the prevalence threshold following common practice , although this threshold for accuracy may be modified . A pooling strategy is best poised to be used in low prevalence settings since this will optimize reductions in number of tests and total cost , but in our study we obtained samples from moderate and high endemicity setting . This was to ensure a sufficient number of positive samples were collected , and to estimate the intrinsic relationship between sensitivity and EPG . We included samples from a wide range of infection intensities , especially light infections . We then modeled a broad range of prevalences ( and associated EPG distributions ) to capture the epidemiology of a low-endemicity setting . Finally , we did not address the potential for semi-curtailed or curtailed sampling , which is when sampling can be stopped early because of definite classification . However , while this offers an attractive option to reduce testing , often all samples are collected and tested simultaneously making this less practical , although future work could explore this option . As treatment programs for control and elimination of schistosomiasis are expanded , new tools and strategies are needed to support the efficient targeting of preventive chemotherapy . Further field study of a urine pooling strategy employing POC-CCA cassette test for rapid and convenient S . mansoni diagnosis is warranted to validate this approach to support control and elimination of schistosomiasis . | Schistosomiasis is a disease caused by parasitic worms that affects over 250 million people . The global control strategy is regular deworming of school-aged children . Before deworming campaigns can be conducted , one must know where the disease is present . The current method requires collection of individual stool and urine samples that are examined under a microscope by trained laboratory technicians . We present an alternative method that can inform where schistosomiasis is present in above 10% of the population , which is the threshold at which school-based deworming is recommended . The proposed strategy involves pooling multiple urine samples and using a rapid diagnostic test . The goal is to reduce the number of tests , cost , time , and laboratory infrastructure to guide decision-making . We collected data in Côte d’Ivoire to evaluate this new diagnostic procedure of pooling urine , and used computer simulation to predict its performance in classification of communities above or below the 10% threshold . We found that the urine pooling strategy with a pool size of 4 reduced the number of tests and cost compared to the current standard method , while maintaining the same accuracy . Our findings suggest that this strategy may be an effective and cost-saving method compared to traditional microscopy . | [
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... | 2016 | Evaluation of a Urine Pooling Strategy for the Rapid and Cost-Efficient Prevalence Classification of Schistosomiasis |
Genome-wide association studies have mainly relied on common HapMap sequence variations . Recently , sequencing approaches have allowed analysis of low frequency and rare variants in conjunction with common variants , thereby improving the search for functional variants and thus the understanding of the underlying biology of human traits and diseases . Here , we used a large Icelandic whole genome sequence dataset combined with Danish exome sequence data to gain insight into the genetic architecture of serum levels of vitamin B12 ( B12 ) and folate . Up to 22 . 9 million sequence variants were analyzed in combined samples of 45 , 576 and 37 , 341 individuals with serum B12 and folate measurements , respectively . We found six novel loci associating with serum B12 ( CD320 , TCN2 , ABCD4 , MMAA , MMACHC ) or folate levels ( FOLR3 ) and confirmed seven loci for these traits ( TCN1 , FUT6 , FUT2 , CUBN , CLYBL , MUT , MTHFR ) . Conditional analyses established that four loci contain additional independent signals . Interestingly , 13 of the 18 identified variants were coding and 11 of the 13 target genes have known functions related to B12 and folate pathways . Contrary to epidemiological studies we did not find consistent association of the variants with cardiovascular diseases , cancers or Alzheimer's disease although some variants demonstrated pleiotropic effects . Although to some degree impeded by low statistical power for some of these conditions , these data suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions . Yet , the study demonstrates the value of combining whole genome and exome sequencing approaches to ascertain the genetic and molecular architectures underlying quantitative trait associations .
One-carbon metabolism ( OCM ) is a process whereby folate transfers one-carbon groups in a range of biological processes including DNA synthesis , methylation and homocysteine metabolism [1] , [2] . The water-soluble B vitamins , vitamin B12 ( B12 ) and folate play key roles as enzyme cofactors or substrates in OCM . Individuals with deficiencies in these vitamins can develop anemia and , in the case of B12 deficiency , serious neurological problems . In adults , epidemiological studies have also suggested that subclinical B12 or folate deficiencies are associated with increased risk of cardiovascular disease [3] , [4] , different cancers [5] , [6] and neurodegenerative disease such as Alzheimer's disease [7] . Serum levels of B12 and folate are in addition to nutrition influenced by several biological processes including absorption , transportation and cellular uptake , as well as processing of precursors into active molecules . Heritability , utilizing di- and monozygotic twins , is estimated to be 59% and 56% for B12 and folate levels , respectively , indicating that there is a substantial genetic component to the population diversity in these physiological variables [8] . Identification of sequence variants that affect circulating levels of B12 and folate can thus give insights into the interplay of diet , genetics and human health . Genome-wide association studies ( GWAS ) have yielded some sequence variants influencing B12 levels [9]–[12] , but have been less successful in identifying variants affecting folate levels [10] , [11] . Thus , genome-wide significant associations with serum B12 levels have been convincingly reported for four loci , FUT2 , MUT , CUBN and TCN1 in European populations [9]–[11] and additional four loci , MS4A3 , CLYBL , FUT6 and 5q32 in a Chinese population [12] . No genome-wide significant GWAS associations have been reported for serum folate levels , however , significant association with the MTHFR A222V variant was demonstrated prior to the GWAS era [13] , [14] and suggestive associations have been reported in European populations for two loci ( FIGN and PRICKLE2 ) [10] , [11] . The classic GWAS applied commercial chip-based genotyping and imputation of HapMap variants of which a majority were common single nucleotide variants ( SNVs ) with very few rare variants with minor allele frequency ( MAF ) <1% [15] , [16] . However , the search for the truly associated functional variants and the targeted gene at each locus has been hindered by the lack of coverage of the full spectrum of the sequence variation of the human genome . Recently , focus has turned to the use of next generation sequencing of whole genomes ( WGS ) [17] , exomes ( WES ) [18] or specific targets [19] , all contributing to a better understanding of the spectrum of allelic variations in the human genome . We expect that attempts to directly cover low frequency and rare sequence variants through next generation sequencing , in addition to the common variants , will improve the search for functional variants and thus the understanding of the underlying biology of human traits and diseases . Here we aimed to identify and characterize associations of SNVs across the allele frequency spectrum with serum levels of B12 and folate by compiling data in up to 45 , 576 individuals based on sequencing initiatives in Iceland and Denmark . For the first time we apply next generation sequence data to identify sequence variants affecting serum levels of B12 and folate and the present datasets are the largest utilized to date for the analysis of these traits .
We estimated the heritability of B12 and folate serum levels based on 38 , 229 and 21 , 708 Icelandic sibling pairs , respectively . Our analysis revealed estimates of 27% for B12 and 17% for folate which are lower than previously reported [8] . To search for sequence variants affecting serum B12 and folate levels we compiled data from two sequencing initiatives in Iceland and Denmark . In Iceland , a large population-based resource has been generated applying WGS and highly accurate imputation of the sequence information into a large fraction of the population [20] , [21] . Utilizing this resource many low frequency and rare causative sequence variants have recently been discovered that affect the risk of common diseases [22]–[26] . In the Danish samples , WES was used to search for low frequency variation associated with complex traits [27] , [28] . The outline of the present study is depicted in Figure 1 . In the Icelandic study sample , 1 , 176 individuals were whole genome sequenced to an average depth of >10× and 22 . 9 million SNVs were identified . These variants were then imputed into 25 , 960 and 20 , 717 chip-genotyped Icelanders with serum B12 and folate measurement , respectively , using highly accurate long-range phasing based imputation [20] . The Icelandic genealogical database allowed for further propagation of the sequence information , applying genealogy based imputation , into 11 , 323 and 8 , 196 relatives of the chip-genotyped individuals , for a total sample size of 37 , 283 and 28 , 913 , respectively , for the two phenotypes [25] ( Text S1 and Table S1 ) . In the Danish part of the study whole exomes of 2 , 000 Danes were sequenced to an average sequencing depth of 8× [28] . From that effort , 16 , 192 coding SNVs with allelic frequency above 1% were selected for Illumina iSelect genotyping in two Danish population-based cohorts of 8 , 293 individuals with measurements of serum B12 and 8 , 428 individuals with measurement of serum folate ( Table S2 ) . Of the 16 , 192 SNVs , 15 , 994 overlapped with the Icelandic variants . A generalized form of linear regression was used to test for association of serum levels of B12 or folate with SNVs , taking into account relatedness and population stratification within each sample set , applying the method of genomic control ( GC ) . Analyses were performed in three steps; sequence variants were analyzed in the Icelandic and Danish samples separately , then by combining in a meta-analysis the overlapping sequence variants identified in both study samples . Loci that associated significantly with B12 or folate levels from these studies were fine mapped using the Icelandic WGS data imputed into chip genotyped individuals and the same data set was used to identify additional signals at each of these loci trough conditional analysis . Finally , the full Icelandic data of 22 . 9 million SNVs were used in GWAS to identify additional loci represented by non-coding variants or rare coding signals not genotyped in the Danish design . Genome-wide significance ( GWS ) level in the study was set at P<2 . 2×10−9 , based on Bonferroni correction for the 22 . 9 million SNVs ( Figure 1 ) . In the separate and combined analyses of SNVs with serum B12 and serum folate levels in the Icelandic and Danish data , a total of 13 genetic loci were found to associate at GWS , P<2 . 2×10−9 ( Table 1 and 2 , Figure S1 and S2 ) . Of the 11 loci associated with serum B12 , five ( CD320 , TCN2 , ABCD4 , MMAA and MMACHC ) were novel and six were previously reported either in populations of European or East-Asian ancestry [9]–[12] ( Table 1 ) . Association analyses with serum folate yielded one novel locus ( FOLR3 ) and confirmed the reported MTHFR locus ( Table 2 ) . Since only coding variants were in the combined analysis we used the Icelandic WGS-based data to screen for stronger non-coding signals at the loci identified in meta-analysis of coding variants . Interestingly , the strongest signal at 10 of the 11 B12-associated loci in the Icelandic data corresponded to missense ( n = 9 ) or nonsense ( n = 1 ) mutations with only the FUT6 locus having a stronger non-coding signal ( rs708686 ) than the missense P124S mutation ( Table S3 ) . As only SNVs had been called from the WGS data and imputed into the Icelandic samples we reassessed each of the 13 B12 and folate loci with INDEL data called using the GATK algorithm ( http://www . broadinstitute . org/gatk/ ) . None of the INDELs detected at the 11 B12 loci associated more strongly than the lead SNVs . However , when reassessing each of the two folate-associated loci we detected a two nucleotide insertion ( rs139130389 , NM_000804:exon3:c . 318_319insTA ) encoding a common ( MAF 10 . 0% ) frameshift mutation in exon 3 of FOLR3 , that associated more strongly with folate levels than the intronic SNV rs652197 identified in the initial scan ( rs139130389: P = 2 . 45×10−12; effect = 0 . 087 SD , Table 2 ) . The insertion and rs652197 are in linkage disequilibrium ( LD ) in the Icelandic sequencing data ( r2 = 0 . 51 ) . Upon further inspection , we found that the ancestral sequence contained the insertion indicating the occurrence of a two base deletion in humans . The deletion with an allelic frequency of 90% in Iceland creates a premature stop codon at amino acid position 107 compared to the full-length protein consisting of 245 amino acids . Coding variants are thus lead signal of both folate loci ( FOLR3 and MTHFR ) . The lead SNVs included both rare , low frequency and common variants with MAFs ranging from 0 . 2% to 48% ( Table 1 and 2 ) . Of the six novel loci , four contained a lead variant with MAF below 6% with the rare missense rs12272669 variant ( MAF 0 . 22% ) in MMACHC that associates with B12 found in the Icelandic data being at the extreme ( Table 1 ) . This variant has been observed in other populations than the Icelandic , albeit at much lower frequency ( MAF 0 . 02% ) ( Exome Variant Server , http://evs . gs . washington . edu/EVS/ ) . For TCN1 and FUT6 previously reported to associate with serum B12 levels we confirmed the association , yet with different SNVs than reported . At the TCN1 locus the strongest associated SNV in the Icelandic data was rs34324219 ( Table 1 ) encoding a D301Y missense mutation , whereas the reported [10] , [11] and correlated ( r2 = 0 . 28 ) non-coding rs526934 was more weakly associated ( Table S4 ) . At the FUT6 locus , the P124S missense mutation ( rs778805 ) identified in the combined analysis of Icelandic and Danish data associated more strongly ( Table 1 ) than the previously reported promoter rs3760776 variant ( Table S4 ) . For the remaining four reported B12-associated loci , MUT , FUT2 , CUBN and CLYBL , we confirmed the association signal [9]–[11] ( Table 1 ) . At the MTHFR locus the strongest folate association was for the major allele of the common A222V ( rs1801133 ) for which previous association with serum folate has been reported [10] , [13] , [14] ( Table 2 ) . For the two loci reported to associate with B12 levels in individuals of East-Asian ancestry ( MSRA and 5q32 ) the variant was either not present in the Icelandic data or at very low frequency ( Table S4 ) whereas the reported non-coding folate signals at FIGN and PRICKLE2 loci did not replicate in the Icelandic folate data ( Table S5 ) . At a less stringent significance level of P<1×10−6 we found three additional loci , CPS1 , SPACA1 and ZBTB10 with suggestive associations with serum B12 levels ( Table S6 ) while suggestive association with folate levels at P<1×10−6 was found for eight additional loci ( Table S7 ) . For the 13 loci associated with serum B12 or folate levels we performed stepwise conditional analyses to search for secondary signals applying Icelandic WGS data imputed into the 25 , 960 and 20 , 717 chip-genotyped Icelanders with serum B12 and folate information . We detected additional signals at five loci , CUBN , TCN1 , TCN2 , FUT6 and MTHFR ( Figure 2 ) . For the serum B12-associated loci , secondary independent association signals at P<5×10−8 were detected at three , CUBN , TCN1 and TCN2 ( Figure 2 , Table 3 , Table S8 ) , while the secondary independent signal at FUT6 ( observed for the reported B12-associated rs3760776 upstream of FUT6 [12] ) did not reach the threshold of significance ( P = 4 . 4×10−6 ) . The secondary signal at the CUBN locus was shown for a group of correlated markers represented by rs56077122 ( located in an intron of the neighboring TRDMT1 ) ( Figure 2 ) . In TCN1 two additional independent signals at P<5×10−8 for serum B12 were found including a missense variant ( R35H ) and an intergenic variant whereas one secondary signal in the TCN2 locus , represented by rs5753231 , was located immediately 5′ to TCN2 ( Figure 2 , Table 3 ) . In the folate-associated loci , a secondary independent signal was found at the MTHFR locus represented by rs17421511 located in intron 4 of the MTHFR gene ( Figure 2 , Table 3 ) . In contrast to the lead SNVs a large fraction of the secondary B12 or folate signals were non-coding . Of the identified variants ( lead and secondary ) the fraction of variance in serum B12 or folate levels explained is estimated to be 6 . 3% for B12 and 1 . 0% for folate ( Text S1 ) . To determine whether any of the lead or secondary association signals at the B12 or folate loci affect the expression of the target gene we analyzed genome-wide expression QTL ( eQTL ) data from white blood cells ( n = 1 , 001 ) and adipose tissue ( n = 673 ) from Icelanders with information on 22 . 9 million SNVs [29] . Of the lead and secondary B12 or folate signals that are coding ( Tables 1–3 ) two showed strong association with the expression of the target gene; the R532H missense variant in MUT ( P = 9 . 1×10−59 in white blood cells and P = 2 . 5×10−16 in adipose tissue ) and the frameshift INDEL in FOLR3 ( P = 7 . 1×10−110 in white blood cells and P = 2 . 5×10−62 in adipose tissue; Table S9 ) . Of all the cis variants at the MUT locus the R532H missense mutation had by far the strongest effect on MUT expression indicating that this effect is not mediated by a non-coding regulatory variant in LD with the R532H mutation . The large effect of the frameshift mutation on FOLR3 expression is likely caused by nonsense-mediated decay of transcripts containing the premature termination mutation [30] . A similar effect was not seen for the nonsense mutation in the CLYBL gene which can likely be explained by the closeness of the mutation to the N-terminal of the CLYBL protein ( amino acid 259 of 340 ) ( Table S9 ) . Of the non-coding lead or secondary B12 or folate signals a statistically significant effect on expression was only seen for the TCN2 promoter variant , however , other markers in the region , that had no effect on serum B12 levels associated more strongly with TCN2 expression . Although lack of appropriate tissue to evaluate the effect of the B12 and folate mutations on expression cannot be excluded , these data suggest that except for the MUT gene the effects of both the coding and non-coding mutations are unlikely to be through expression . Rare mutations in some of the B12 genes described here i . e . MMACHC , MMAA , MUT , CD320 , TCN2 and CUBN have been described in connection with rare conditions of methylmalonic aciduria and megaloblastic anemia that all relate to defects in B12 metabolism ( OMIM database , http://www . ncbi . nlm . nih . gov/omim/ ) . In addition , epidemiological studies have suggested a link between reduced B12 and folate levels and the risk of common conditions such as cardiovascular diseases [3] , [4] , cancers [5] , [6] and neurodegenerative disorders [7] . To evaluate the effect of the B12 or folate variants on these conditions we analyzed the association with coronary artery disease ( CAD ) , stroke , colon cancer , prostate cancer and Alzheimer's disease in data obtained from deCODE's phenotype database . As outlined in Table S10 , variants associated with serum B12 or folate levels did not consistently affect the risk of the diseases tested; the B12 or folate increasing allele for some variants was weakly protective and for others weakly at risk , and only two loci ( CUBN associated with CAD and MTHFR with stroke ) were statistically significant ( P<0 . 0018 ) but with opposite effects on these diseases . B12 or folate deficiencies can lead to increased serum homocysteine [2] , yet of all the B12 or folate loci tested only two associated significantly with homocysteine levels , with the B12 or folate increasing allele decreasing the homocyteine levels as expected ( Table S10 ) . These loci were the folate-associated MTHFR variant previously reported to associate with homocysteine [10] , [31] , [32] and the B12-associated variant at the MUT locus . Neither of these loci associated with cardiovascular disease or Alzheimer's disease , despite increased homocysteine has been suggested to increase the risk of these diseases . Deficiency of B12 or folate is associated with megaloblastic anemia characterized by the presence of abnormally large red blood cells , increased mean corpuscular volume ( MCV ) and increased mean corpuscular hemoglobin ( MCH ) . None of the identified variants associated significantly with MCV and MCH ( Table S10 ) . We also tested the recessive model for the B12 or folate variants in relation to these conditions , but did not detect any new associations . Inconsistency in the direction of the effect of each of the variants on these conditions ( increased or decreased risk ) ( Table S10 ) indicates that for a given condition the combined effect of all the variants would be consistent with lack of association . The absence of observed directional consistent effects of the B12 and folate variants on the phenotypes tested suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions , likely reflecting that B12 and folate levels have weak effects on these conditions . However , we recognize that for some of the conditions analyzed sample sizes are too small to detect weak effects , calling for cautious interpretation . One of the B12-associated loci , FUT2 , has previously been associated with reduction in liver enzymes including alkaline phosphatase ( ALP ) [33] and cholesterol levels [34] , increased risk of Crohn's disease [35] , [36] , psoriasis [37] , retinal vascular caliber [38] and type 1 diabetes [39] and protection against Norovirus infection [40] . These associations can be explained by the function of FUT2 in cell surface glycobiology as determinant of the Lewis antigen blood group . To evaluate pleiotropic effects of the identified B12 and folate variants , we screened the deCODE phenotype database , which contains information on the majority of common diseases and their associated risk factors ( n = 400 ) , applying both multiplicative and recessive genetic models ( P = 3 . 5×10−6 after Bonferroni correction ) . We found that the FUT2 variant associated strongly with serum levels of ALP ( P = 1 . 1×10−73 ) and also with psoriasis ( P = 4 . 3×10−3 ) as previously reported . We also detected a strong association with serum levels of cancer antigen 19-9 ( P = 1 . 1×10−146 ) , lipase ( P = 2 . 2×10−24 ) and suggestive association with bone mineral density ( BMD ) ( P = 1 . 3×10−5 ) with the B12-increasing allele decreasing ALP levels , increasing the serum levels of the cancer antigen 19-9 and lipase and increasing the risk of developing low BMD ( osteoporosis ) ( Table S11 ) . An increase in serum lipase is associated with Crohn's disease [41] , but the causal link is unclear . The increased risk for low BMD observed for the FUT2 variant may be secondary to reduced ALP activity that might be a reflection of reduced bone remodeling . When applying the recessive model to the B12 and folate variants we found suggestive associations of the FUT6 variant with abdominal aortic aneurysm ( AAA ) and of the folate-associated variant in MTHFR with thoracic aortic aneurysm ( TA ) . In both cases the effect of the B12- or folate-increasing allele was protective ( Table S11 ) . These associations could be mediated through the effect of these variants on B12 and folate levels as reduced levels of B12 and folate have been linked to the development of aortic aneurysm [42] .
Here we performed association analyses of up to 22 . 9 million SNVs , identified through WGS and WES , in up to 45 , 576 individuals to identify and characterize genetic variation influencing population diversity in serum levels of B12 and folate . We discovered five novel loci that associate with serum B12 levels and one novel locus for folate levels and replicated the six reported B12 loci and one folate locus . In addition , we identified five novel secondary independent signals at both the new and previously reported loci . The fraction of variance in serum B12 or folate levels explained by the identified variants is estimated to be 6 . 3% for B12 and 1 . 0% for folate ( Text S1 ) . Of the identified SNVs , both common and rare , we find that a large fraction ( 13 of 18 ) is represented by coding variants which is an unusually high fraction of coding variants compared to previous GWAS for other traits . Furthermore , of the 13 loci that associate with serum B12 and folate levels the genes at 11 of them can be directly linked to the current understanding of B12 and folate metabolism such as absorption , transport or enzymatic processes and one ( FUT6 ) has potential links with these processes ( Figure 3 ) . Only CLYBL has a function that cannot be directly related to these pathways . Specifically , eight loci are involved in transporting B12 and folate between different tissues , four of them TCN1 , FUT2 , FUT6 and TCN2 as co-factors or regulators of co-factors necessary for the transport and the other four , CUBN , CD320 , ABCD4 [43] and FOLR3 as membrane transporters actively facilitating membrane crossing . MUT and MTHFR catalyze enzymatic reactions in the OCM where MMACHC and MMAA are involved in co-enzymatic processes ( Figure 3 ) . Moreover , we note that of the 13 genes , two ( TCN2 and CD320 ) are known and two ( MUT and MMAA ) are suggested to interact in vivo [44] ( Figure 3 ) . Together with the high fraction of coding mutations these data indicate that the target genes at all of the loci have been identified . By screening the deCODE database for pleiotropic effects of the B12 and folate variants we replicated some of the previous associations of the FUT2 gene and detected novel suggestive association with increased risk of osteoporosis ( low BMD ) potentially mediated through diminished bone remodeling as a consequence of reduced ALP activity . We also detected suggestive associations of the FUT6 and the MTHFR variants with AAA and TA , respectively . However , we did not demonstrate association of any of the variants with the cardiovascular diseases , CAD and stroke , colorectal cancer , prostate cancer or Alzheimer's disease and only two of the variants associated with homocysteine levels . Although to some degree impeded by low statistical power for some of these conditions , these data suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions .
All participants gave written informed consent . The studies were conducted in accordance with the Declaration of Helsinki II and were approved by the local Ethical Committees ( approval numbers Denmark: H-3-2012-155 , KA 98155 and KA-20060011 , DeCode 08-105-V3-S1 ( issued 30 . 08 . 2011 ) ref . VSNb2008060006/03 . 1 ) . For the Icelandic samples , serum B12 and folate levels were assessed in blood samples from Icelanders at the Landspitali University Hospital Laboratory or at the Icelandic Medical Center ( Laeknasetrid ) Laboratory in Mjodd ( RAM ) , between the years 1990 and 2011 . B12 and folate levels were normalized to a standard normal distribution using quantile normalization and then adjusted for sex , year of birth and age at measurement . For individuals for which more than one measurement was available we used the average of the normalized value . The Danish data were generated in two population-based study samples recruited in Copenhagen . The Inter99 cohort is a randomized , non-pharmacological intervention study for the prevention of ischaemic heart disease , conducted on 6 , 784 randomly ascertained participants aged 30 to 60 years at the Research Centre for Prevention and Health in Glostrup , Denmark [45] ( ClinicalTrials . gov: NCT00289237 ) . Detailed characteristics of Inter99 have been published previously [45]–[47] . The Inter99 cohort included 5 , 481 and 5 , 624 individuals with genotypes and measurement of serum B12 and folate , respectively . Health2006 is a population-based epidemiological study of general health , diabetes and cardiovascular disease of 3 , 471 individuals aged 18–74 years [48] . Health2006 was also conducted at the Research Centre for Prevention and Health in Glostrup , Denmark . The Health2006 cohort included 2 , 812 and 2 , 804 individuals with valid genotypes and measurement of serum B12 and folate , respectively . In Inter99 serum B12 and folate were measured by a competitive chemiluminescent enzyme immunoassay ( Immulite 2000 System; Siemens Medical Solutions Diagnostics , Los Angeles , CA , USA ) as previously reported [14] . In Health2006 , serum B12 and folate were measured by chemiluminescent immunoassay ( Dimension Vista platform , Siemens Healthcare Diagnostics GmbH , Eschborn , Germany ) . In the Icelandic part , SNVs were identified through the Icelandic WGS project . A total of 1 , 176 Icelanders were selected for sequencing based on having various neoplasic , cardiovascular and psychiatric conditions . All of the individuals were sequenced to a depth of at least 10× . The generation of genotypic data in Iceland is detailed in earlier reports [23] and in Text S1 , and consisted of the following steps: SNV calling and genotyping in WGS , long range phasing , genotype imputation and in silico genotyping . In the Danish part of the study 16 , 192 SNVs for genotyping were selected from a WES study of 2 , 000 individuals [28] . In brief , exon capture and Illumina sequencing to a depth of 8× were performed in 2 , 000 Danes by methods previously described [27] . The exome was captured by a NimbleGen 2 . 1M HD array with a target region of 34 . 1 Mb including 18 , 954 genes defined by CCDS ( Consensus Coding Sequence database ) . The average number of reads sequenced for each individual was 22 . 3 million with most reads being 30 to 80 bases long . After alignment to the human reference genome ( assembly hg18 , NCBI build 36 . 3 ) and stringent quality assurance , including uniqueness of genomic mapping and Q-score >20 , the median coverage per individual was 91% of the target region and had an average depth of 8× ( 96% coverage and 11× depth before filtering ) . After applying quality criteria 70 , 182 SNVs with an estimated MAF above 1% based on the reads using maximum likelihood were identified [49] . The details of the WES have been described previously [28] . 20 , 005 SNVs were , as part of a published study , selected from the exome sequencing for genotyping in 16 , 888 samples by a custom-designed Illumina iSelect array . First , 18 , 358 SNVs annotated to the most likely deleterious categories ( 179 nonsense , 15 , 789 nonsynonymous , 219 located in splice sites and 2 , 171 in untranslated regions ) were prioritized . Second , 1 , 048 SNVs nominally associated with type 2 diabetes ( P<0 . 05 ) in a sequencing-based association study were selected . Finally , we selected 599 synonymous variants in 192 loci previously associated with common metabolic traits at GWS . Genotype data was obtained for 18 , 744 SNVs . Quality control of samples included removing closely related individuals , individuals with an extreme inbreeding coefficient , individuals with a low call rate , individuals with a mislabeled sex and individuals with a high discordance rate to previously genotyped SNVs . 15 , 989 individuals passed all quality control criteria . The SNVs were filtered based on their MAF ( >0 . 5% ) , genotype call rate ( >95% ) , Hardy-Weinberg equilibrium ( P>10−7 ) or cross-hybridization with the X-chromosome . 16 , 192 SNVs passed all filters [28] . Genotyping of FOLR3 rs652197 in Danish samples was done by KASPar SNP Genotyping System ( KBioscience , Hoddesdon , UK ) . | Genome-wide association studies have in recent years revealed a wealth of common variants associated with common diseases and phenotypes . We took advantage of the advances in sequencing technologies to study the association of low frequency and rare variants in conjunction with common variants with serum levels of vitamin B12 ( B12 ) and folate in Icelanders and Danes . We found 18 independent signals in 13 loci associated with serum B12 or folate levels . Interestingly , 13 of the 18 identified variants are coding and 11 of the 13 target genes have known functions related to B12 and folate pathways . These data indicate that the target genes at all of the loci have been identified . Epidemiological studies have shown a relationship between serum B12 and folate levels and the risk of cardiovascular diseases , cancers , and Alzheimer's disease . We investigated association between the identified variants and these diseases but did not find consistent association . | [
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] | 2013 | Genetic Architecture of Vitamin B12 and Folate Levels Uncovered Applying Deeply Sequenced Large Datasets |
Ubiquitin ( Ub ) can generate versatile molecular signals and lead to different celluar fates . The functional poly-valence of Ub is believed to be resulted from its ability to form distinct polymerized chains with eight linkage types . To provide a full picture of ubiquitin code , we explore the binding landscape of two free Ub monomers and also the functional landscapes of of all eight linkage types by theoretical modeling . Remarkably , we found that most of the compact structures of covalently connected dimeric Ub chains ( diUbs ) pre-exist on the binding landscape . These compact functional states were subsequently validated by corresponding linkage models . This leads to the proposal that the folding architecture of Ub monomer has encoded all functional states into its binding landscape , which is further selected by different topologies of polymeric Ub chains . Moreover , our results revealed that covalent linkage leads to symmetry breaking of interfacial interactions . We further propose that topological constraint not only limits the conformational space for effective switching between functional states , but also selects the local interactions for realizing the corresponding biological function . Therefore , the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity . The simulation results also provide several predictions that qualitatively and quantitatively consistent with experiments . Importantly , the K48 linkage model successfully predicted intermediate states . The resulting multi-state energy landscape was further employed to reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb . Our results further suggest that hydrophobic interactions are dominant in the functional landscapes of K6- , K11- , K33- and K48 diUbs , while electrostatic interactions play a more important role in the functional landscapes of K27 , K29 , K63 and linear linkages .
Ubiquitin ( Ub ) was discovered in the mid-1970s [1] and has been found to ubiquitously exist in eukaryotes . Ub plays a central role in regulating the balance between a protein's destruction and its synthesis . The dysfunction of Ub is closely linked to a wide range of disorder diseases ( including Alzheimer's , Parkinson and Prion diseases and others ) [2] . Besides the well-known function of protein degradation , Ubs also serve as numerous regulatory signals including endocytosis , DNA repair , autophagy and transcription [3] . Most signal functions of Ub can be understood by considering it as a “molecular tag” which marks a protein and determines the fate of this post-translationally modified protein . Ubiquitin tag is achieved via covalent attachment to a substrate protein with a monomeric Ub ( monoubiquitination ) , multiple Ubs ( multi-monoubiquitination ) or a Ub polymer ( polyubiquitination ) [4] . In a poly-Ub chain , Ub units are assembled with each other through forming covalent bonds between the carboxyl-terminal group of one Ub ( termed the distal moiety ) and the side-chain -amino group of a lysine among the all seven lysines ( K6 , K11 , K27 , K29 , K33 , K48 and K63 ) or the amino-terminal residue ( M1 , corresponding chains often referred to as linear ) of another Ub ( termed the proximal moiety ) . It is well established that all ubiquitin linkage types coexist in all cells with varying abundance [5]–[7] . Remarkably , almost half are populated by K48 and K63 linkage types whose cellular functions have been well characterized [8] . Extensive studies suggested that the former usually takes action in proteasomal degradation ( the most common fate of a ubiquitinated protein ) , while the latter plays non-degradative roles in cell signalling , such as endocytosis and DNA damage repair [8]–[10] . Beside the two typical linkages , K11 linkage is also abundantly present in cells . A few recent work reported that K11 linkage chain not only has non-degradative roles but also acts as potent proteasomal degradation signals in diverse cellular pathways [11]–[13] . This is a surprise finding because K48-linked chains have always been considered to be the unique destruction tag for unneeded proteins in cells . By contrast , very little is known about the remaining five atypical linkage types [4] , [14] , [15] . At present , it seems clear that different polyUb chains generate distinct molecular signals and lead to different cell fates [16] . But how do these linkage types determine the different functions of Ub chains and the diversity of ubiquitin recognition ? The answer to this question seems to lie in the structure of Ub chains and their fluctuations ( conformational dynamics ) by considering the fact that all types of polyUb chains are constructed by identical Ub units with the same physicochemical properties ( mass , charge and interactions ) but different topology ( linkage position and length ) . In fact , the topology of an Ub polymer has been suggested to be important in the control of the fate of a Ub modified protein by a few experimental and theoretical studies [17]–[19] . However , it is still unclear how the topology affects the underlying energy landscape of polyUb chains themselves . Great efforts have been made in the elucidation of conformational diversity between alternatively linked polyUB chains . In summary , there are five linkage types ( including M1 , K6 , K11 , K48 and K63 , see Fig . 1 , as well as Table 1 in Text S1 ) which have been structurally characterized on the basis of traditional biophysical tools , such as X-ray crystallography , nuclear magnetic resonance ( NMR ) [20] and small angle X-ray scattering ( SAXS ) [21] . In addition , a most recent work carried out by Tang and coworkers reported the conformational dynamics of free Ub monomers in solution by using paramagnetic relaxation enhancement ( PRE ) , an NMR techniques sensitive to lowly populated species [22] . This PRE study has suggested that Ubs can form non-covalent dimers with a modest binding affinity [22] . This reveals that Ubs not only interact with ubiqtitin-binding domains in the cell , but also are able to interact with themselves to form dimeric molecules , whose role is nonnegligible in the case of high concentration . All these data provided strong evidence that the conformational behaviours of Ub monomers and polymers are a lot more complicated than originally thought . Structural characterizations can provide important local information ( corresponding to energy minima or metastable states ) on the functional landscape at the bottom of “energy funnel” [23] , [24] . For a deeper understanding that how Ub system functions , however , it is essential to obtain a global picture through the functional landscape [24] . This presents a unique chance for theoretical modelling and simulations . It is well-known that a protein in nature is marginally stable through the balance of interactions ( folding and binding interactions ) . This is especially true for a protein complex or a multi-domain protein which functions via frequent binding and unbinding events between folded units . These events are largely driven by two types of interactions , that is , electrostatic and hydrophobic interactions [25] . For polyUb systems , this notion is also strongly supported by two facts . On the one hand , at near-physiological conditions , Ubs can form compact interfaces involving numerous hydrophobic residues [22] , [26] , highlighting the important role of hydrophobic interactions in the Ub assemble . On the other hand , the conformational dynamics of polyUb can be highly dependent on the environmental pH [17] , [20] , [27] , indicating the importance of electrostatic interactions . However , it is still unknown about their relative contributions in the association of Ub units and their relationships to the distinct functional landscapes of Ub chains . In the present work , we will develop a flexible binding model by the introduction of electrostatic and hydrophobic interactions and employ it to explore the functional landscape of polyUb chains . Note that only two Ub units ( diUb ) were used in our model because this is the simplest form of a polyUb chain and the minimal structural unit for longer polyUb chains . Different polyUb chains with all seven lysine linkages and linear linkage as well as free Ub monomers ( without a linkage ) were investigated based on the flexible binding model . This model allows us to determine the dominant driving forces in the assembly of diUbs with different linkages . The simulation results provide several predictions that qualitatively and quantitatively consistent with experiments . Importantly , the functional landscape of K48-diUb is predicted to have three intermediate states . Inspired by the multi-state functional landscape , we employed a simple three-state model to well reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb .
First , we investigated the conformational dynamics of two free Ub monomers by performing MD simulation based on the free Ub model . Hereby , we point out that in the free Ub model , the two Ub monomers are not connected by a covalent bond . This free model simulation is important not only because our work is the first to simulate the dimerization of free Ub monomers , but also because the results will provide us a benchmark to estimate and quantify the effects of linkages on the conformational behaviour of diUb chains . After calibrating the energetic parameters in the free model , we then constructed the corresponding covalent linkage models to investigate the conformational dynamics of diUbs with all linkage types . These linkage models were carried out by introducing an isopeptide or peptide bond between G76 of one Ub monomer and one of its seven lysine residues ( by bead ) or the N-terminal M1 ( by bead ) of another Ub monomers on the basis of the free model ( see Table 2 in Text S1 and Methods ) , To assess the conformational space sampled by our flexible binding model , we plotted the free energy surfaces as a function of the distance between the center of mass of Ub monomers ( ) and the RMSDs from available structures resolved by X-ray crystallography and NMR ( see Fig . S1 ) . The results show that most of the experimental structures can be sampled by the free model and further validated by the corresponding linkage models . We further calculated the minimal RMSD of Ub dimers from all experimental structures ( see Table 3 and 4 in Text S1 ) . It shows that all these structures have minimal RMSD less than 0 . 35 nm in the free model and less than 0 . 25 nm in the corresponding linkage models . It is unexpected to us given the fact that the huge conformational space of two free Ub monomers was explored with limited computational time , despite that most of them are not located at the free energy basins in the free model . Furthermore , the results show that the compact structures in contrast to open structures were better captured by the corresponding linkage models . Remarkably , the good characterization of compact structures was also able to be achieved by the free model with a given protein concentration ( 5 mM in the present work ) . To emphasize this point , free energy surfaces projected onto and RMSD from compact structures are shown in Fig . 2 . It indicates that the compact structures of M1- , K6- , K11- , K48- and K63-linked diUbs have remarkable populations on the conformational space . Especially for K6 , K11 , and K48 linkage types , we found that there are free energy basins located at or near the native conformational region ( typically with RMSD from the compact structures less than 0 . 4 nm ) on the binding landscape sampled by the free model and the functional landscape sampled by their linkage models . As a control , we projected the conformational space sampled by the free Ub model onto RMSD from a dimeric structure only stablized by crystal packing forces ( so represents a “wrong” structure ) , as shown in Fig . S2 . It shows that the free Ub monomers never sample such conformation . It therefore supports that the assembly of free Ub monomers to the conformations similar to the experimental structures of diUbs is far beyond an accidental event . This leads to a remarkable finding that Ub monomers without covalent linkages have the ability to assemble the native structures of polyUb chains , and these assembled conformations are further stabilized by the formation of linkages between Ub units . To further shed light on molecular or microscopic details of the assembly process of Ub units , we measured the interfacial interactions by counting the inter-molecular contacts . Fig . 3A shows that the two Ub molecules form noncovalent dimeric conformations through a wide interface composed of residues K6-K11 , E34-P37 , Q40 , R42 , I44 , G47 , H68-G76 . This result is in good agreement with the PRE experimental data [22] which indicates a symmetric interface encompassing residues 4–12 , 42–51 and 62–71 with the exception of E34-P37 region and the C-terminal tails . This could be a result of insufficient number of paramagnetic tags used for PRE measurement [29] . In fact , the later two regions contain several important hydrophobic residues including I36 and L73 which are members of another hydrophobic patch , referred to as the I36 patch [4] . In other words , our simulation results highlight the important role of two hydrophobic patches in the formation of Ub interfaces . One is the well-known I44 patch , and another is the I36 patch involving L8 , I36 , L71 and L73 . In fact , the I36 patch has been found to be sequestered in the interface of K6- and K11-linked polyUb chains as shown in three X-ray structures ( PDB 2XK5 , 3NOB and 2XEW , see Fig . 1 ) . Fig . 3A also indicates that the interfacial contact distributions of two Ub monomers are perfectly overlapped . This is expected because our model does not introduce any biasing to a particular assembled structure . The identical distribution of interfacial interactions not only reflects the sufficient sampling of the simulations , but also indicates the symmetry of interfacial interactions between Ub units . The symmetry is also supported by the interfacial contact matrix , as shown in Fig . S3 . To monitor the effect of covalent linkages on the binding of two Ub units , we further inspect the interfacial contact distributions of diUbs with all linkage types , as shown in Fig . 3B–I . By comparison with the data obtained by the free model , it clearly indicates that the symmetry of interfacial interactions present in the binding of free Ub monomers is broken by the introduction of a covalent bond between two Ub units . We may ask what is the physical reason ( entropic or enthalpic ) of the symmetry breaking ? Or whether the symmetry breaking has its biological benefits ? In contrast to the free model , the only difference lies in an extra isopeptide/peptide bond introduced between Ub units in each linkage model . The free energy contribution of the bonded constraint was further quantified on the basis of polymer theory [30] ( see Fig . S4 ) . The result suggested that the impact is mainly entropic rather than enthalpic . In fact , in contrast to the binding free energy landscape of two Ub monomers , the functional landscapes of covalently linked diUbs are significantly more compact ( with a smaller average ) . Furthermore , to quantify the relative entropic and enthalpic contributions of the bonded constraint to the functional landscapes of different linkage types , we performed a detailed analysis of entropy-enthalpy compensation and calculated the correlation coefficients between entropy and free energy ( ) , as well as the correlation coefficients between enthalpy and free energy ( ) . The results are shown in Fig . S5 , S6 , S7 and Table 5 in Text S1 . By comparison with the free model , increases in all linkage models , but is dependent on the linkage types . Specially , for M1 , K27 , K29 and K63 linkage types , the bonded constraint reduces and increases . Therefore , their interfacial symmetries are broken by entropy . For K6 and K11 linkage models , the degree of the increase of is significantly larger than the degree of the increase of . So their interfacial symmetries are mainly broken by enthalpy . While for K33 and K48 linkage models , neither nor shows strong correlations , but the degree of increase is much larger than the degree of increase . Thus this case was considered to be entropically driven . In summary , the quantification analysis of entropy-enthalpy compensation supports the proposal that the symmetry breaking arises mainly from entropy rather than enthalpy for most linkage types . Entropy reduction breaks the symmetry of interfacial interactions by decreasing the degrees of freedom of Ub system so as to facilitate the searching of functional states on the functional landscape of polyUb chains . By inspecting the compact or closed structures of diUbs , we found that charged residues are also involved in the formation of interfaces in addition to hydrophobic residues . For example , in the closed state of K48-diUb ( PDB 1AAR ) , the hydrophobic interface formed between I44 patches contains three basic residues R42 , K48 and H68 . More importantly , there are not any negatively charged residues located at the opposite face of these positively charged residues . This indicates that electrostatic interactions may play a negative role in the formation of hydrophobic interface between I44 patches . However , this does not rule out the possibility that electrostatic interactions contribute to form interfaces at other regions of the Ub surface . Now there are fundamental questions: which interactions are dominant for Ub-Ub binding ? And what are their roles in diverse functional landscapes of diUbs ? To investigate the relative contribution of hydrophobic and electrostatic interactions and their relationships with the interface formation in the binding of Ub units , we decomposed the interfacial energy of the system into two terms , that is , hydrophobic energy and electrostatic energy . Then we examined their respective distributions and correlations . Fig . S8A and B show the free energy profiles as a function of and and of and . By comparison of the free energy profiles , it indicates a physical picture as expected , that electrostatic force is long-ranged , while hydrophobic force is short-ranged . We further show the free energy profiles as a function of and in Fig . S8C . It indicates that is negatively related to , implying a competition between hydrophobic interactions and electrostatic interactions in the formation of Ub-Ub bound complex . This picture becomes clearer when investigating the distribution of and as a function of the distance between I44 hydrophobic patches of two Ub monomers ( ) , as shown in Fig . S8D . The energy distribution clearly shows that the I44-I44 interface is highly favored by hydrophobic interactions , but it is disfavored by electrostatic interactions . This is consistent with the structural analysis , indicating the interface is surrounded by three basic residues ( R42 , K48 and H68 ) , whereas no acid residues counterbalance to the net charges . Fig . 4 shows the relationship between conformational populations and interfacial interaction . We can see that hydrophobic interactions play a more dominant role in K11- , K6- , K48- and K33-linked diUbs than other linkage types . Especially for K11 , K6 and K48 linkages , electrostatic interactions made a negative contribution ( positive energy ) to formation of compact diUbs . It also indicates the competition between electrostatic interactions and hydrophobic interactions in the formation of compact structures . This finding is also consistent with the results from free Ub model which suggests that is negatively related to as shown in Fig . S8 . In fact , around the I36 and I44 hydrophobic patches there are several basic residues which may form electrostatically repulsive force as the hydrophobic patches are buried at the interface . Given the strong pH dependence of conformational equilibrium of K48 linkage evident from solution experiments [20] , [26] , [27] , we expect that the conformational distribution of K6 and K11 linkages highly dependent on pH as well . We hypothesize that , decreasing pH will open their conformations which may be validated by further experiments in the future . By contrast , electrostatic interactions are more important to the association of Ub units in K27 , K29 and K63 as well M1 linkages . To quantify the differences between the conformational space of diUbs with all types of linkages , we calculated the population of conformational states based on a three-state model derived from the free energy landscape of K48-diUb ( details shown in following subsection ) . The conformational space was coarse-grained into three states: open , closed and compact . The compact state can be further divided into I36-I36 , I36–I44 and other compact state according to the role of I36 and I44 hydrophobic patches in the formation of interface between Ub units . Note that the criteria for determining the open , closed and compact states are defined according to the corresponding free energy profiles ( Fig . S9 ) . The results are summarized in Table 1 ( and also Fig . 4 ) . It supports the open populations in this order: first M1 ( ) , K63 ( ) and K29 ( ) , then K27 ( ) , K48 ( ) and K33 ( ) , and finally K6 ( ) and K11 ( ) . If according to the closed populations , Ub chains can be categorized into two groups . For the first group the order is first K48 ( closed ) , subsequently K6 ( ) , then K11 ( ) and finally K27 ( ) . For the second group including M1 , K29 , K33 and K63 , they are not able to form closed conformation . This finding is fully consistent with the previous studies [31] . In addition , we predicted that K33-diUb is able to form I36 patches involved hydrophobic interface like K11-diUb . More precisely , the population of I36-I36 compact state for K33 is less than that of K11 of . While K27 and K48 linked diUbs in addition to K11 and K33 linked diUbs have the ability to form hydrophobic interfaces between I36 patch and I44 patch . Their populations of I36–I44 compact state are in this order: K6 ( ) , K48 ( ) , K33 ( ) , K27 ( ) and K11 ( ) . By integrating the entropy-enthalpy compensation analysis , we found that , the long-range electrostatic interactions play a remarkable role in entropy-driven cases ( such as K63 , M1 , K29 and K27 ) with significant population of open state , while the short-range hydrophobic interactions dominate in enthalpy-driven cases whose conformational spaces are tend to be compact . This implies an inherent relationship between entropy/enthalpy , electrostatic/hydrophobic interactions , and conformational distributions . Among the eight different polyUb chains , K48-linkage is the best characterized . K48-linked diUb has been suggested to have multiple distinguished structures by X-ray crystallography and NMR ( see Fig . 1 ) . Previous and recent NMR spectroscopy experiments have collected abundant data ( including chemical shift perturbation , residual dipolar coupling , and relaxation ) on conformational behaviour of K48-diUb in solution [20] , [26] , [27] , [32] . These data provide strong evidence that K48-diUb cannot be described by a single conformational state , instead its I44-involved hydrophobic interface rapidly opens and closes on 10–40 ns time scale [33]–[35] , implying multiple possible free energy basins at the functional landscape . Previous experimental data also suggested the high pH-dependence of the conformational equilibrium [20] , [26] , [27] . Despite the fact that lowering pH will increase the population of open state [26] , it is still a matter of debate whether the open state is predominant at physiological conditions . For example , Varadan et al . found the population of open state is [26] , but Hirano et al . concluded to be [20] . The present work will try to reconcile the seemingly conflicting observations by investigating the conformational dynamics of K48-diUb with CGK48 model ( two Ub monomers linked by a K48-G76 isopeptide bond , see Table 2 in Text S1 ) . To emphasize the prediction ability of our flexible binding model , we show the free energy profiles as a function of RMSD from the X-ray structure of closed form of diUb ( ) and the distance between the center of mass of Ub units ( ) or the distance between the center of mass of I36 hydrophobic patches ( ) ( Fig . 5A and B ) . The free energy surfaces show a free energy basin around ( , ) = ( 0 . 3 , 2 . 6 ) and ( , ) = ( 0 . 3 , 1 . 7 ) , corresponding to the closed state of K48-diUb ( labeled by “C” ) . The minimal value of is 0 . 17 nm ( see Table 3 in Text S1 ) . This indicates that the closed state of K48-diUb was validated by our model . In addition to the closed basin of K48-diUb , an open basin is present ( labeled by “O” ) . Remarkably , there are three free energy minima between open and closed basins . These minima represent intermediate states during the conformational change of opening and closing K48-diUb . It is worth noting that these intermediate states are not related to the X-ray structures of compact state of K48-diUb ( PDB 1TBE , 3NS8 , 3AUL ) as indicated by Fig . S10 . However , we found that one of these intermediate states ( labeled I3 ) similar to the compact structure ( PDB 2PE9 ) which was determined by NMR spectroscopy [34] . The compact X-ray structures might be resulted from the crystal packing forces , therefore are unstable in solution . Indeed , the theoretical prediction of intermediate states is supported by the NMR data from Fushman laboratory [33]–[35] . They found that K48-diUb in solution rapidly exchanges between at least three major states , including an open state , a closed state and an intermediate state which is hard to be structurally characterized . Let us go back to the debate on conformational equilibrium of K48-diUb . Considering that there are strong evidences of the presence of intermediate states from simulations and NMR experiments , it is reasonable to construct a simple three-state model in which K48-diUb in solution switches its conformations between open , closed and compact states . The compact state has well-defined interface like closed state . But the difference lies in that in compact state at least one of I44 patches is solvent-exposed and available to be recognized by Ub partner proteins . Subsequently , the three-state model was employed to explain the conformational dynamics of K48-diUb . To clarify or explain the contradiction between Hirano's and Varadan's measurements , we first have to make clear about the assumptions they adopt in obtaining the conformational population based on experimental data . In fact , Hirano's measurement was performed by comparing chemical shifts of wild-type K48-diUb with that of monomeric Ub as an open state and that of cyclic K48-linked diUb as a closed state . Because the cyclic K48-diUb has two iso-peptide bonds between Ub units which lock the bound conformation fully at closed form , using its chemical shifts as a reference will unavoidably overestimate the actual population of open state in solution . To check this speculation , we measured the population of all three states as shown in one-dimensional free energy profile in Fig . 5C . It shows that the populations of closed and open states are similar to each other at around . The remaining states occupy of the whole conformational space . This part of conformational space mostly consists of the compact conformations and contains multiple intermediate states . Considering that both Hirano's and Varadan's measurements were based on a two-state assumption , then , our simulation data can give an excellent explanation on the discrepancy between them . That is , the former was the result obtained from the closed population versus non-closed population , yielding especially high value for open population . Whereas the latter was obtained by monitoring the non-open population versus open population , resulting in a very high value for closed population . Although Hirano et al . argued that using K48C mutant of diUb in Varadan's measurement possibly affected the conformational equilibrium , Varadan et al . tested the K48R mutant and showed no change in the chemical shifts [26] . In other words , from our theoretical prediction , we believe that the differences between experimental observations are more likely caused by the inherent complex multi-state functional landscape rather than artificial errors . Above all , we predicted a multi-state functional landscape for K48-diUb and found that the multi-state model can be used to well reconcile the seemingly contradictory experimental measurements about the conformational equilibrium . K63-diUb has been considered to be a typical Ub chain like the classical K48-linked polyUb [4] . In contrast to the essential role of protein degradation of K48-linked polyUb , the function of K63-linked polyUb was found to be linked to numerous nondegradative signaling processes [21] . Several groups have attempted to illuminate the function discrepancy between K48 and K63 linkage from the structural view by using NMR , X-ray crystallography , and SAXS [7] , ( see also Table 1 in Text S1 ) . The conformational dynamics of K63-diUb and M1-diUb was explored by the CGK63 model ( two Ub monomers linked by a K63-G76 isopeptide bond , see Table 2 in Text S1 ) and the CGM1 model ( two Ub monomers linked by a M1-G76 peptide bond , also denoted as linear model ) , respectively . Fig . S11 shows the free energy profiles of K48-diUb , K63-diUb and linear diUbs as a function of ( where X represents the X-ray structures of PDB 3H7P , 3DVG , 3AXC , 2W9N ) and . It indicates that the free energy landscapes of K63-diUb and that of M1-diUb are almost identical with each other , however significantly distinct from that of K48-diUb . This implies that K63- and linear diUb share a highly similar functional landscape which is significantly different from K48-diUb . In fact , the spatial position of K63 and M1 is adjacent in the structure of Ub unit ( the distance between their separate atoms is 0 . 54 nm ) , making their constraining effects on conformational space of diUb similar . Beyond the topology similarity , the difference between the two linkage types might lie in the chemical properties of the linkages because M1-linked polyUb is linked by peptide bond rather than isopeptide bond . This is supported by the fact that both K63 and M1 linkage types can recognize the same receptor proteins having ubiquitin-binding domain , but only M1-linked Ub chains rather than K63-linked chains can be specifically cleaved by some deubiquitinase enzymes [7] . The results also support the notion that similar protein topology inclines to generate similar energy landscape [40] . Moreover , the conformational distribution summarized in Table 1 , shows that the populations of open conformation for K48- , K63- and linear diUb are about , and , respectively . It also supports the finding that the conformational space of K63- and M1-diUbs is more extended than that of K48-diUb . This is consistent with previous NMR and SAXS observations [7] , [38] as well as the recent single molecule fluorescence resonance energy transfer ( smFRET ) data [41] . Given the excellent qualitative consistence between previous experiments and our simulations , we have to note that the populations of conformational states from our simulations have differences with that from recent smFRET measurement [41] . The smFRET analysis suggested that the compact state of K63-diUb and M1-diUbs is 70% and 75% , respectively . It is worth pointing out that , quantitative analysis in the experiment was based on the dye distance between the N-terminal of distal Ub and the C-terminal of proximal Ub . In order to draw direct comparison from the smFRET data , we employed the distance between M1 of distal Ub and G76 of proximal Ub ( ) as the order parameter to obtain the conformational distribution ( Fig . S12 ) . The results from the simulations suggested that conformational distribution from this order parameter is not sufficient to reflect the complicated energy landscape of polyUb chains . Especially for K48-diUb , the predicted intermediate states are completely hidden on the one-dimensional free energy profile as a function of ( Fig . S13 ) . In fact , our recent work suggested that the multi-state functional landscape of adenylation kinase also cannot be well characterized by a single order parameter [28] . On the basis of this , we strongly recommend to use multiple order parameters rather than one to monitor the functional dynamics of a protein , especially for one with a possible complex landscape . Experimentally , simultaneous measurement of multiple pairs of fluorescent dyes or multi-color FRET is feasible [42] . Nevertheless , the finding that M1-diUb has slightly more open conformations than K63-diUb from simulations is consistent with that from smFRET experiments . Remarkably , the difference between the population of M1-diUb and that of K63-diUb ( 5% ) measured by smFRET is in reasonable agreement with the population difference measured by simulations ( 6% ) . This indicates our simulations not only exactly captured the small population difference but also their relative tendency to form compact conformations , that is , first K48- , then K63- and lastly M1-diUb . While polyUb chains with K48 and K63 linkages as canonical Ubs are the best studied , the K11 linkage is the most prominent among the left six atypical or non-canonical types and its relative abundance in yeast was found to be up to which is comparable to the level of K48 linkage [43] . Note that the high abundance is not validated in higher eukaryotes [13] . Several reports have identified its role in cell cycle regulation as an efficient proteasomal degradation signals like K48-linked chains [11] , [44] , [45] . This poses a question: How the two proteasomal degradation signals are distinguished by proteasome ? A partial answer to this question may be derived from the view of energy landscape . Fig . 6A and B show that the free energy surfaces as a function of and RMSD from the X-ray structure of the closed form of K48-diUb ( PDB 1AAR ) and the compact form of K11-diUb ( PDB 3NOB ) , respectively . Note that , the conformational dynamics of K11-diUb was investigated by the CGK11 model ( two Ub monomers linked by a K11-G76 isopeptide bond , see Table 2 in Text S1 ) . We can see that there is a free energy basin formed around the conformational region with RMSD less than 0 . 3 nm on the free energy surface F ( , ) . It indicates that the experimental compact structure of K11-diUb is perfectly validated by our model . By comparison with the free energy surface of K48-diUb , it further suggests that the functional landscapes of K48-diUb and K11-diUb have strikingly difference with each other . This is also supported by the free energy surface ( Fig . 6C ) projected on two order parameters independent of native structures . They are the distance between I36 hydrophobic patches ( ) and the distance between I44 hydrophobic patches ( ) . Fig . 6C shows that , K11-diUb mostly populates at the conformational region with while K48-diUb at the region with . Moreover , the distribution of interfacial interactions between distal unit and proximal unit in K11-diUb is also distinctly different from that in K48-diUb , as shown in Fig . 3H . In contrast to K11-diUb whose interfacial interaction distribution seems to be relatively symmetrical as the distribution of free Ub monomers , the distribution of K48-diUb is obviously asymmetrical . It is interesting and worthnoting that K11 is the only one whose interface symmetry is not significantly broken by the formation of linkage between Ub units ( see Fig . 2 ) . For K48-diUb , the “hot spot” residues ( corresponding to the residues contributing more than one average interfacial contact , see Fig . 3 ) in distal Ub comprises T7-G10 , Q40 , R42 , I44 , G47 , H68 and V70-G76 , while in proximal Ub contains K6-G10 , R42 , I44 , A46-Q49 , H68 , V70-L73 , G75 and G76 . By comparison , the hot spot residues at the interface between distal of K11 and proximal Ubs are comprised of T7-K11 , E34-P37 , Q40 and V70-G76 . The distributions of hot spot residues enable us to shed light on the binding patterns between Ub units . Our results suggest that the I36 patches ( consisting of L8 , I36 , L71 and L73 ) on both distal and proximal Ubs play a significant role on the formation of the interface of K11-diUb but not of K48-diUb , while the I44 patches ( consisting of L8 , I44 and V70 ) play a more important role on the interface of K48-diUb . In other words , we found that unlike K48-diUb in which the I44 patches can be totally buried in its closed form , for K11-diUb its I44 patches are scarcely shielded so as to be accessible to protein partners containing ubiqtitin-binding domains . Taken together , our results demonstrate that the functional landscapes and binding patterns of K11-diUb and K48-diUb have significant discrepancy . This may explain why the two share-function polyUb chains can be distinguished by proteosome , and why some proteins specifically interact with K11-linked Ubs but not with other polyUb chains [13] , [45] . Less is known about the remaining four atypical linkages involving K6 , K27 , K29 and K33 which are more rare in cells [7] . Especially for the three linkages K27 , K29 and K33 , their ubiquitin lysine residues are very close with each other not only in sequence space but also in configuration space ( the distances between atoms are in range of 0 . 5–0 . 8 nm ) , making them difficult to be measured by experimental tools , such as mass spectroscopy [46] . Currently their functional roles are not well-established , but there are evidences suggesting K29 and K33 as well as K6 linkages might play many non-proteolytic roles and K27 is important to mitochondrial biology [4] . Fig . 7 shows the free energy surfaces of K6 , K27 , K29 and K33 linkages , respectively . In order to predict the conformational space , we used two order parameters and because they are not dependent on specific structures like RMSD . The free energy surfaces of K11 , K63 and K48 are also shown as references of compact , open and multi-state landscape , respectively . By further analysis of their conformational spaces , we found that although K6-diUb and K11-diUb have highly compact conformational spaces , their compact states are different . The conformational space of K6-diUb is mainly contributed by compact structures with an I36-I44 interface , while that of K11-diUb by compact structures with an I36-I36 interface . We also found that , for both K27-diUb and K29-diUb , their conformations are more compact than that of K63-diUb and more open than that of K11-diUb , but are distinct from each other . These conclusions are also consistent with the aforementioned analysis of the conformational distributions ( Table 1 and Fig . 6 ) . Remarkably , there are multiple free energy basins on the functional landscape of K33-diUb ( see also Fig . S14 ) , corresponding to multiple functional states . By further inspecting the structures of these functional states , we identified two of them are similar to the compact structures of K6-diUb ( PDB 2XK5 ) and K11-diUb ( PDB 3NOB ) . This multi-state feature of K33-diUb is highly similar to that of K48-diUb . To our best of knowledge , our work provides the first theoretical evidence of the presence of multiple functional states on the functional landscape of K33-diUb . Interestingly , despite the close sequence and spacial positions between the three linkage residues K27 , K29 and K33 , the resulting functional landscapes have dramatic differences between each other . Our theoretical prediction can be validated by future experiments . This prediction further makes us to argue that , Ub as a small globule protein ( with 76 residues ) has been evolutionarily selected by nature , endowing it with the stable foldability and subtle binding specificity . So even a small shift of topological constraint can result in a large change of interfacial interactions . The topological sensitive of resultant functional landscape can be well explained by the highly designed local environment on the surface of Ub . From microcanonical perspectives , the topological constraint of covalent linkages is purely entropic , and dramatically reduces the conformational search space . However , the result could be the changing of the gap and roughness of the intrinsic landscape [47] . This is purely due to the limitation resulting the selection of local interactions ( local in sequence and space ) from the topological constraint . Thus , the topological constraint of all eight linkages provides a way for breaking the binding symmetry and reaching the functional specificity .
The post-translational modification of proteins in the cell is an important theme in molecular and cell biology . Different from other post-translational modifications , such as glycosylation , methylation and phosphorylation , ubiquitination provides a more versatile cellular signaling mechanism [7] . This is mostly contributed by the possibility of Ub units to form different polyUb chains through eight different linkages ( M1 and all seven lysines ) . Linking multiple Ub units in one chain not only strengthens the Ub signal , but also provides further differential new signals through the formation of numerous conformations [32] . The canonical ubiquitin chains , such as K48- and K63-linked chains , have been well characterized , both structurally and functionally . Despite of this , other linkages are less understood and all linkage types have to be studied in order to obtain a full picture of ubiquitin code [48] . In the present work , we explored the binding landscape of two free Ub monomers as well as the global functional landscapes of all eight linkage types by theoretical modeling . The results lead to a number of significant conclusions . In particular , the simulations from the free Ub model give a remarkable finding that most of the compact structures of covalently bonded diUbs resolved by X-ray or NMR pre-exist on the binding landscape of free Ub monomers . Additionally , all these experimental structures were validated by the corresponding linkage models . It is worth noting that our flexible binding model does not contain any prior knowledge of native protein-protein interactions in these structures . Therefore the pre-existence of native states of polyUb chains on the binding energy landscape of free Ubs is rather surprising . It also suggests the prediction ability of our flexible binding model despite at coarse grained level . Moreover , this finding leads us to speculate that the well-folded architecture of Ub monomer has embodied the information of forming polymerized functional structures on its binding energy landscape . The corresponding functional landscape of diUbs with a specific linkage type is further generated on the basis of the binding landscape on which functional states are selected by topological constraints as a form of covalent linkages between Ub monomers , as shown in Fig . 8 . It is important to note that only part of functional states on the binding landscape can be sampled on the corresponding functional landscape of a specific linkage type . Other part of functional states can only be sampled by the corresponding linkage types . In other words , most of conformational space of the binding landscape is forbidden on the functional landscape as a consequence of specific topological constraints . The conformational restriction arising from the topological constraint may account for the symmetry breaking of the interfacial interactions . In addition , it also can reduce the entropy of the functional landscape so as to facilitate the fast searching of functional states . The specific topological constraint also selects the local interactions for realizing the corresponding biological function . Therefore , the topological constraint provides a way for breaking the symmetry and reaching the biological specificity . Moreover , it is likely that forming a covalent linkage between two Ub units does not induce the appearance of a new functional state but instead just shifts the population of pre-existing states on the binding landscape . This notion is similar to the “conformational selection” scheme [49] , a mechanism which is extensively used in the description of the conformational change of a protein induced by ligand binding [28] , [50] . In summary , there are four points revealed by theoretical data consistent well with experimental data: ( 1 ) Intermediate states exist on the functional landscape of K48-diUb , besides the known open and closed states . ( 2 ) K63- and linear diUb share a highly similar functional landscape which is significantly different from K48-diUb . ( 3 ) M1-diUb has slightly more open conformations ( about 6% ) than K63-diUb . ( 4 ) The functional landscapes and binding patterns of K11-diUb are significantly different from that of K48-diUb . Furthermore , our simulations predict several points that may be validated by future experiments: ( 1 ) Hydrophobic interactions play a more dominant role in K6- , K11- , K48- and K33-linked diUbs . By contrast , electrostatic interactions are more important to the association of Ub units in K27 , K29 and K63 as well linear linkages . ( 2 ) The conformational distribution of K6- and K11-diUbs is highly dependent on pH like K48-diUb . Decreasing pH will increase their open conformations . ( 3 ) K33-diUb has a multi-state landscape where two functional states are similar to the compact structures of K6-diUb as well as K11-diUb . ( 4 ) The symmetry of interfacial interactions is not significantly broken in K11-diUb . ( 5 ) The functional landscapes among K27- , K29- and K33-diUbs have dramatic differences between each other despite the close sequence and spacial positions between the three linkage residues . While there are relatively abundant structures of K48-diUb , as a number of snapshots of its highly dynamic conformations , have been captured , people speculated that other linkage types also have other conformations which could be adopted besides the limited available structures [11] . This speculation was validated by our present simulation work . Not only three intermediate states are predicted to be present on the functional landscape of K48-diUb , other diUb chains , such as K33-diUb , are suggested to have several unprecedented functional states . Inspired by the multi-state landscape , we employed a simple three-state model to well reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb . The multi-state conformational equilibrium is believed to play an important role in the recognition of numerous proteins containing ubiqtitin-binding domains . Despite of ever increasing amount of three dimensional structures of protein-protein complexes and multi-domain proteins resolved by biophysical tools , especially X-ray crystallography and NMR spectroscopy , there are still big gaps between the number of experimental structures deposited in the Protein Data Bank and the number of predicted binary interactions between human proteins [51] . From this point , our coarse-grained protein-protein interaction model provides the computational community with a flexible binding tool necessary to predict the complexed structures of proteins or protein domains . More importantly , our method has the ability to give global information ( including number of functional states , their populations and free energy barriers between each other ) on the protein system . Given the exciting success on the implication of this method to polyUb chains , we will extend our flexible binding model to more protein systems and expect to exhibit the prediction ability in the future .
Each amino acid in polyUb chain is represented by one bead or two beads with or without charge which is dependent on its property . One bead named in our model is located at the position of the atom . The other bead named is located on the center of mass of the sidechain atoms ( with exception of Gly ) . The bead and bead are used to coarse grain the atoms of backbone and that of sidechain , respectively . We used the structure of Ub monomer ( PDB: 1UBQ ) resolved by X-ray crystallography [54] as the reference conformation for structure-based model ( SBM ) . It is important to point out that this structure of Ub monomer is the only structural information as an input of the present model . In addition , we found that the C-terminus of Ub monomer is quite flexible , as shown in Fig . S16 . Such flexibility reflected in the free model is naturally implemented into all of the linkage models , because all models share the same structure-based potential for Ub units . The electrostatic potential was represented by the Debye-Huckel ( DH ) model in which the details can be found in our previous works and elsewhere [24] , [28] , [55] . In this study , we set the dielectric constant and focus on physiological salt concentrations of ( lead to the Debye screening length 10 ) which are consistent with the experimental condition [22] . All charged residues were assigned charges according to their electrostatic properties at neutral pH 7 . Then , for Lys and Arg , for Asp and Glu , and for His , where e is the elementary charge . To account for the hydrophobic interface formation , we introduce nonnative hydrophobic interactions by the expression which is borrowed from Chan and coworkers' model [56]:Where is the hydrophobicity strength between beads i and j , is the distance between bead i and j and the optimal distance to form interfacial hydrophobic interactions between Ub units . After testing with an extensive set of s ranging from 5 . 0 to 10 . 0 , we finally set to be 8 . 0 instead of 5 . 0 which was used in protein folding study in ref . [56] . Constant C can be used to adjust the basin width of hydrophobic potential but is set to be 1 . 0 for simplification . is introduced to modulate the balance between the hydrophobic force and electrostatic force . After the calibration of hydrophobic potential by setting to be 0 . 92 , the binding affinity was estimated to be in an order of magnitude of ( at 0 . 1 M salt concentration and 5 mM protein concentration , see the calculation in SI Appendix ) , in good agreement with experimental measurement [22] . The parameter set of is obtained from our recent works [57] , [58] but with a bit modifications by using the expression as . By doing so , we can shift the parameter set of hydrophobic interactions into the region between 0 and 1 . Note that we set the hydrophobic strength to be zero as so as to avoid overestimating the electrostatic interactions which have been separately modelled by DH potential . In the original work [59] , . is the original Miyazawa-Jernigan ( MJ ) potential , is the mean value of the entire set of MJ weights in this protein system , is a variable to modulate the strength of energetic heterogeneity which reflects the sequence discrepancy , it is set to be 1 . 0 corresponding to the sequence-flavored model [59] . In other words , in the present work . was found to be −3 . 4 and −3 . 7 in a coupled folding and binding system ( histone chaperone and histone H2A . Z-H2B ) [58] a multi-domain protein ( Y-Family DNA Polymerase ) [57] , respectively . Here , we used the latter value because it can normalize ( the lowest MJ weight −7 . 37 is about twice of −3 . 7 ) . The final parameter matrix of hydrophobic interactions is shown in Fig . S17 . To model the diUb chains with different linkage types , we introduced the bond-stretching potential in the form of a harmonic potential , where kb is the spring constant , r is the bond length and is the reference bond length . Here we set , is set to be 0 . 4nm for an isopeptide bond ( for all seven lysine linkage types ) and 0 . 38 nm for a peptide bond ( for M1 linear linkage type ) . | Ubiquitination , as an important post-translational modification of proteins , provides a versatile cellular signaling mechanism . This is mostly contributed by the possibility of ubiquitin units to form different polyUb chains through eight different linkages . However , it is still unclear how these linkage types determine the different functions of polyUb chains . In this study , we address this question via the theoretical modeling and molecular dynamics simulation . This allows us to obtain a full picture of topology-function relationship of polyUb chains . The theoretical results led us to propose that topology of polyUb chains selects the functional landscapes from its binding landscape and the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity . | [
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] | 2014 | PolyUbiquitin Chain Linkage Topology Selects the Functions from the Underlying Binding Landscape |
Human T-cell leukemia virus type-1 ( HTLV-1 ) causes two distinct diseases , adult T-cell leukemia/lymphoma ( ATL ) and HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) . Since there are no disease-specific differences among HTLV-1 strains , the etiological mechanisms separating these respective lymphoproliferative and inflammatory diseases are not well understood . In this study , by using IL-2-dependent HTLV-1-infected T-cell lines ( ILTs ) established from patients with ATL and HAM/TSP , we demonstrate that the anti-inflammatory cytokine IL-10 and its downstream signals potentially act as a switch for proliferation in HTLV-1-infected cells . Among six ILTs used , ILTs derived from all three ATL patients grew much faster than those from three HAM/TSP patients . Although most of the ILTs tested produced IFN-γ and IL-6 , the production of IL-10 was preferentially observed in the rapid-growing ILTs . Interestingly , treatment with exogenous IL-10 markedly enhanced proliferation of the slow-growing HAM/TSP-derived ILTs . The IL-10-mediated proliferation of these ILTs was associated with phosphorylation of STAT3 and induction of survivin and IRF4 , all of which are characteristics of ATL cells . Knockdown of STAT3 reduced expression of IL-10 , implying a positive-feedback regulation between STAT3 and IL-10 . STAT3 knockdown also reduced survivin and IRF4 in the IL-10- producing or IL-10- treated ILTs . IRF4 knockdown further suppressed survivin expression and the cell growth in these ILTs . These findings indicate that the IL-10-mediated signals promote cell proliferation in HTLV-1-infected cells through the STAT3 and IRF4 pathways . Our results imply that , although HTLV-1 infection alone may not be sufficient for cell proliferation , IL-10 and its signaling pathways within the infected cell itself and/or its surrounding microenvironment may play a critical role in pushing HTLV-1-infected cells towards proliferation at the early stages of HTLV-1 leukemogenesis . This study provides useful information for understanding of disease mechanisms and disease-prophylactic strategies in HTLV-1 infection .
Human T-cell leukemia virus type-1 ( HTLV-1 ) is a retrovirus that infects approximately 5–10 million people worldwide [1] . Although most infected individuals remain asymptomatic , approximately 4% develop adult T-cell leukemia/lymphoma ( ATL ) and less than 2% develop HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) [2–4] . ATL is an aggressive lymphoproliferative disease with severe immunosuppression [5] , while HAM/TSP is a chronic inflammatory disease of the spinal cord featuring progressive demyelination [6 , 7] . The reasons why the same virus is the causative agent of two vastly different diseases are unknown . Previous reports have demonstrated that there are no disease-specific HTLV-1 strains causing either ATL or HAM/TSP [8 , 9] , indicating that the different clinical outcomes of infection could be attributed to host factors . However , the essential host factors and the mechanisms regulating the initiation of lymphoproliferative or inflammatory disease in HTLV-1 infection remain unclear . Thus far , several differences in host factors have been reported between the two diseases . For example , the incidence of disease is greater in males with ATL [10] , but greater in females with HAM/TSP [4] . Furthermore , the association of mother-to-child transmission of HTLV-1 infection with ATL development has also been suggested [11] . Analysis of genetic background indicated that the frequencies of several HLA alleles might differ in HTLV-1 infection-associated diseases [12 , 13] . The strength of the HTLV-1 Tax-specific cytotoxic T lymphocyte ( CTL ) response also differs between the two diseases , being elevated in HAM/TSP patients but impaired in ATL patients [14 , 15] . Even though HTLV-1-specific CTLs were detected in ATL patients , they usually do not sufficiently expand in response to their cognate antigens , suggesting the existence of an immunosuppressive mechanism [16 , 17] . Another difference between the different clinical outcomes observed in HTLV-1 infection is the virus expression level . HTLV-1 gene expression is usually low in vivo; undetectable at the protein level and barely detectable at the mRNA level [18 , 19] . HAM/TSP patients have slightly higher HTLV-1 mRNA levels , when compared with asymptomatic HTLV-1 carriers or ATL patients [20] . Viral protein expression is an important aspect of HTLV-1 infection . The pleiotropic HTLV-1 protein , Tax , can induce genes related to cell survival and proliferation , as well as inflammatory responses [21–23] , likely contributing to viral pathogenesis . Additionally , Tax protein is a dominant target antigen for CTL responses [15 , 24] . Enhanced expression of Tax may partly explain the elevated Tax-specific CTL responses observed in HAM/TSP patients . HTLV-1 basic leucine zipper factor ( HBZ ) encoded by the antisense HTLV-1 genome is also a multifunctional protein implicated for HTLV-1 pathogenesis [25 , 26] . However , the expression levels of HBZ do not differ among the diseases [27] . Recently , we reported that stromal cells can inhibit HTLV-1 expression via type I interferon ( IFN ) responses [28] , suggesting that the innate immune response might be involved in the difference in HTLV-1 expression levels between diseases . The innate host immune response itself may also contribute to HTLV-1 pathogenesis . HTLV-1 infected T-cells exhibit constitutive activation of NF-κB , a critical transcription factor for both leukemogenesis and inflammation [23 , 29 , 30] . Although Tax possibly activates NF-κB , it cannot fully explain the mechanism of constitutive NF-κB activation , because NF-κB is also activated in Tax-negative ATL cells [29] . The presence of genetic alterations in the mediators of T-cell receptor signaling pathway in ATL cells may partly be involved in the mechanisms of NF-κB activation [31] . In addition , our recent study indicated that ATL cells constitutively express antisense RNA including the HTLV-1 LTR region , resulting in constitutive activation of NF-κB through activation of the double-stranded RNA ( dsRNA ) -dependent protein kinase ( PKR ) [32] . This provided a new angle to understanding how HTLV-1 produces constitutive NF-κB activation without detectable Tax protein , once more indicating a link between HTLV-1 pathogenesis and innate host anti-viral immune responses . The potential involvement of host anti-viral innate responses in HTLV-1 pathogenesis leads us to hypothesize that individual differences in the host response might play a role in the clinical outcomes of HTLV-1-infection . Few studies have focused on the difference in innate immune responses between HAM/TSP and ATL patients . Several reports have demonstrated differences in the serum cytokine profiles of HTLV-1-infected individuals with various diseases . Among them , it is noteworthy that ATL patients present with higher levels of IL-10 compared with asymptomatic HTLV-1 carriers and healthy donors [22 , 33] , while this difference is not significant between HAM/TSP patients and asymptomatic HTLV-1 carriers [34 , 35] . IL-10 is a pleiotropic cytokine with immunoregulatory and anti-inflammatory functions , the expression of which is driven mainly by NF-κB and AP-1 following stimulation of various immune receptors including TLRs and PKR [36–38] . In the present study , to investigate host factors regulating HTLV-1-mediated lymphoproliferative and inflammatory diseases , we established IL-2-dependent HTLV-1-infected T-cell lines ( ILTs ) from patients with ATL and HAM/TSP , and characterized their cytokine production , viral protein expression , and proliferative abilities . We demonstrate that ATL-derived ILTs grew faster than HAM/TSP-derived ILTs , which was associated with their ability to produce IL-10 . We further demonstrated that exogenously added IL-10 converted the HAM/TSP-derived ILT cells into cells with better growth capabilities , which was associated with induction of survivin and IFN regulatory factor 4 ( IRF4 ) , resembling ATL cells . This study provides evidence that the IL-10-mediated signal is critical for determining the process that HTLV-1-infected cells gain proliferative abilities in the early stages toward HTLV-1 leukemogenesis .
ILTs were established by long-term culture of peripheral blood mononuclear cells ( PBMCs ) isolated from patients with ATL ( #22 , #227 , and H2 ) and HAM/TSP ( #294 , #441 , #439 ) in the presence of recombinant human IL-2 ( rhIL-2 ) . ATL-derived ILTs grew well in the presence of IL-2 , while the growth of HAM/TSP-derived ILTs was much slower . All ILTs were CD4+ CD8- ( Fig 1A top panel ) . In ILT cells , Tax was usually detectable only in a small subpopulation , although all the ILT cells were infected with HTLV-1 , as they expressed Tax after stimulation with PMA ( Fig 1A bottom panel ) . In a reporter assay , elevated levels of NF-κB activities were detected in all ILTs tested , when compared with Jurkat or MOLT4 cells ( Fig 1B ) . Tax expression levels and HTLV-1 proviral loads varied among ILTs regardless of their growth characteristics ( Fig 1A and 1C ) . The ILTs also expressed different combinations of cytokines , as evaluated by a bead-based cytokine assay ( Fig 1D ) . Notably , ATL-derived ILTs produced varying levels of IL-10 , while HAM/TSP-derived ILTs expressed negligible levels of IL-10 . Among the ILTs tested , ILT-H2 was the highest IL-10 producer . Most of the ILTs tested produced IL-6 , although ILT-227 produced considerably less . ILT-441 had the greatest TNF-α production , and ILT-227 , -H2 and -294 produced higher levels of IFN-γ than the others . Despite the preference of IL-10 production in some ILTs , there was no preference in GATA3 expression . ILTs expressed both TBX21 ( T-bet ) and GATA3 , but lower levels of RORC and FOXP3 mRNA , when compared with activated PBMCs from a healthy individual ( S1 Fig ) . Since ILTs producing higher levels of IL-10 appear to have increased proliferative capacity , we examined the significance of IL-10 on the proliferation of ILTs by adding rhIL-10 to the cell culture medium . Results demonstrated that , in all HAM/TSP-derived ILTs tested , the expansion of the cells was remarkably greater in the presence of rhIL-10 when compared with the cultures without rhIL-10 ( Fig 1E ) . ATL-derived ILTs grew well in the absence of rhIL-10 , and ILT-22 that had slightly slower growth ability than the other ATL-derived ILTs presented further growth enhancement in the presence of rhIL-10 . Similar IL-10-mediated growth enhancement was observed in ILT-227 to a lesser degree , while ILT-H2 showed no difference or a slight decrease in cell growth following IL-10-treatment . We next assessed the effects of IL-10-treatment on HTLV-1 Tax expression , as Tax has been shown to play important roles in the transformation of HTLV-1-infected cells [23] . In ILT-294 and ILT-439 , Tax expression was clearly reduced in the presence of rhIL-10 ( Fig 1F ) . In the other ILTs , the effects of IL-10 on Tax expression levels were limited and varied among experiments . Thus , IL-10 production was associated with proliferation of ILTs , and exogenously added rhIL-10 promoted cell growth , especially in ILTs derived from HAM/TSP patients . However , this was not correlated with HTLV-1 Tax expression . Since IL-10 promoted the expansion of ILT cells ( Fig 1E ) , we next investigated the effects of IL-10 on cell cycling and apoptosis . In the absence of exogenous rhIL-10 , the levels of Ki67 expression in HAM/TSP-derived ILT cells were lower than those of ATL-derived ones , consistent with their growth characteristics . In the presence of rhIL-10 , expression of Ki67 increased in most of the ILTs tested , indicating that IL-10 promoted progression of the cell cycle ( Fig 2A ) . In the absence of rhIL-10 , HAM/TSP-derived ILT cultures contained considerable proportions of apoptotic cells detected by Annexin V-staining , which were decreased in the presence of rhIL-10 ( Fig 2B , S2 Fig ) . In contrast , ATL-derived ILTs contained lower proportions of apoptotic cells , which were slightly increased or not affected by rhIL-10 . Interestingly , both HAM/TSP- and ATL-derived ILTs exhibited detectable levels of cleaved caspase-3 at 19 kDa in the absence of rhIL-10 in immunoblotting assays . The lower bands of cleaved caspase-3 ( 15–17 kDa ) were also detectable in some samples treated with the proteasome inhibitor MG132 , indicating the presence of active apoptotic signaling in these cells . Further increases in the 19 kDa cleaved caspase-3 were observed in ILT-294 , ILT-441 , ILT-439 , ILT-22 and ILT-227 cells cultured in the presence of rhIL-10 , especially in the samples treated with MG132 ( Fig 2C , S3 Fig ) . In ILT-H2 , which exhibited the highest levels of IL-10 production among ILTs tested , the effect of rhIL-10 on cleaved caspase-3 levels was limited and varied among experiments . Since there was an apparent paradox that IL-10 suppressed apoptosis despite enhanced caspase-3 cleavage , especially in the HAM/TSP-derived ILTs tested , we next assessed the levels of survivin . Survivin is known to inhibit apoptosis by directly interacting with active caspases [39] and is expressed in ATL cells [40–42] . Immunoblotting analysis indicated that IL-10 enhanced survivin expression , especially in ILT-294 , ILT-441 , and ILT-439 cells ( Fig 2D ) . Survivin expression in ILT-22 , ILT-227 and ILT-H2 was slightly enhanced or unchanged following IL-10-treatment . These observations suggested that , although ILTs have constitutively active apoptotic machinery , IL-10 promoted cell cycle progression and inhibited apoptosis partly through the induction of survivin . We next assessed signaling pathways involved in IL-10-mediated proliferation in ILTs . We focused on NF-κB and STAT3 , because both transcription factors are constitutively activated in ATL cells and implicated in leukemogenesis [29 , 43] . We used ILT-H2 and ILT-294 reporter cells that had been transduced with luciferase reporter genes driven by NF-κB and STAT3 . Following IL-10-treatment , NF-κB activity was decreased , and STAT3 activity was markedly enhanced in ILT-294 cells ( Fig 3A ) . In ILT-H2 , the effects of IL-10-treatment were limited . Since IL-10 activates the JAK-STAT signaling pathway through engagement of its cognate receptor [44] , we assessed cell surface expression of IL-10 receptor α , and confirmed that the ILTs tested expressed IL-10Rα at similar levels ( Fig 3B ) . We then evaluated the effects of rhIL-10 on STAT3 phosphorylation . Immunoblotting analysis revealed that phosphorylation of STAT3 was strongly enhanced in the presence of rhIL-10 in ILT-294 , ILT-441 , ILT-439 , and ILT-22 ( Fig 3C ) . In ILT-227 and ILT-H2 , which spontaneously produced high levels of IL-10 , STAT3 was strongly phosphorylated in the absence of exogenous rhIL-10 , for which further IL-10-treatment showed little effect . Nucleotide sequencing indicated that none of the ILTs tested had mutations in the hot spot regions ( exon 20 and 21 ) of the STAT3 gene [45 , 46] ( S4 Fig ) . We also assessed NF-κB pathways by immunoblotting , and found a reduction in the amount of NF-κB p52 , especially in ILT-294 cells , suggesting that IL-10 suppressed the non-canonical NF-κB pathway in this cell line ( S5 Fig ) . A similar trend was observed in ILT-441 and ILT-22 cells but not in the other ILTs tested . Thus , IL-10 strongly enhanced STAT3 phosphorylation with mild suppression of NF-κB signaling in the ILTs with low IL-10 production , especially those derived from HAM/TSP patients . We next investigated the role of STAT3 in ILTs using siRNA for STAT3 ( si-STAT3 ) . In ILT-294 cells cultured in the presence of rhIL-10 , si-STAT3 significantly suppressed mRNA levels of STAT3 , IL10 , BIRC5 ( survivin ) , MYC , and IRF4 , but not BCL2 or BCL2L1 ( Bcl-xL ) , when compared with control siRNA ( Fig 4A , top ) . Similar results were obtained in ILT-22 cells ( Fig 4A bottom ) . The marked reduction in IL10 expression by si-STAT3 suggested the presence of a positive feedback loop between STAT3 and IL-10 . This idea was further supported by the finding that IL10 expression was induced by treatment with rhIL-10 in the ILTs producing little or no IL-10 ( Fig 4B ) . The role of the IL-10-STAT3 pathway in the cell growth in ATL-derived ILTs was further examined by using several inhibitors . AS101 , a synthetic tellurium compound , has been reported to exhibit various immunomodulatory effects including IL-10 inhibition [47] . AS101 suppressed the growth of ILT-H2 and ILT-22 cells in 5 days of culture ( Fig 4C ) . Knockdown of IL-10 also partly suppressed the cell growth in ILT-H2 but hardly in ILT-22 cells ( S6 Fig ) . Moreover , a STAT3-inhibitor cucurbitacin I ( JSI-124 ) [48] markedly inhibited the cell growth in ILT-H2 and ILT-22 , as well as in ILT-294 cultured in the presence of rhIL-10 ( Fig 4D ) . These findings indicate the importance of the IL-10-STAT3 pathway in the growth of ILT cells , to which the positive feedback loop may potentially contribute . Following STAT3-knockdown , reduction of survivin , IRF4 , and the 19 kDa band of cleaved caspase-3 was commonly observed in ILT-294 ( with rhIL-10 ) , ILT-22 and ILT-H2 cells ( Fig 4E ) . Thus , STAT3 contributed to the IL-10-mediated or spontaneous cell growth in HAM/TSP-derived and ATL-derived ILTs , respectively , which was associated with induction of downstream molecules including survivin and IRF4 . IRF4 expression has been reported previously in ATL cells [49 , 50] . However , the relationship between IL-10 and IRF4 expression remains to be elucidated . We , therefore , examined intracellular IRF4 expression in ILTs by flow cytometry . The levels of IRF4 expression in HAM/TSP-derived ILTs tested were lower than in ATL-derived ILTs in the absence of rhIL-10 ( Fig 5A ) . There were two populations with low and high IRF4 expression respectively , and the IRF4-high population was more common in ATL-derived ILTs . Exogenous rhIL-10 clearly enhanced IRF4 expression , especially in HAM/TSP-derived ILTs . A similar trend was also observed in ATL-derived ILTs , except for ILT-H2 , which constitutively expressed the highest levels of IRF4 regardless of IL-10 treatment ( Fig 5A ) . Although several mutations in the exons 2 and 3 in the IRF4 gene have been found in ATL [31] , no mutations in those regions of the IRF4 gene were found in the ILTs tested ( S4 Fig ) . To further investigate the role of IRF4 in the expansion of ILTs , we knocked down IRF4 using si-IRF4 in ILT-294 ( cultured in the presence of rhIL-10 ) , ILT-22 and ILT-H2 cells . Immunoblotting analysis confirmed that knockdown of IRF4 resulted in decreased survivin expression and increased the 19 kDa form of cleaved caspase-3 in these cells ( Fig 5B ) . IRF4 knockdown mildly decreased IL10 mRNA expression ( S7 Fig ) . The cell number was more efficiently decreased by IRF4 knockdown than STAT3 knockdown in ILT cells . The effects of STAT3 and IRF4 knockdown in Jurkat cells were limited ( Fig 5C ) . We also assessed the relationship between IRF4 and Ki67 by flow cytometry ( Fig 5D ) . In ILT-H2 cells transfected with control siRNA , most of the cells strongly expressed both IRF4 and Ki67 . Knockdown of STAT3 suppressed IRF4 expression , which was associated with decreased Ki67 . Knockdown of IRF4 more clearly demonstrated that the decreased Ki67 expression was associated with IRF4 reduction . Similar results were obtained in ILT-22 cells and ILT-294 cells cultured in the presence of rhIL-10 ( Fig 5D ) . These results indicate that IL-10 enhances expression of IRF4 partly through STAT3 , and IRF4 plays critical roles in the survival and proliferation of ILTs .
In the present study , by using patient-derived HTLV-1-infected T-cell lines that were not fully transformed , we demonstrated that the anti-inflammatory cytokine IL-10 and its downstream signaling play critical roles in the proliferation of HTLV-1-infected T lymphocytes . The proliferation speed of the ILTs varied widely among lines , even in the presence of rhIL-2 , and appeared to be associated with their ability to produce IL-10 . This was confirmed by the addition of rhIL-10 in the culture resulting in marked enhancement of cell growth , especially in ILTs derived from HAM/TSP patients . These findings imply that , while HTLV-1 infection alone may not be sufficient for proliferation of the infected cells , IL-10-dominant polarization of the infected cell itself or its microenvironment potentially contributes to proliferation of HTLV-1-infected cells . Lymphoproliferation is a characteristic of ATL , which is not associated with HAM/TSP . Therefore , IL-10-mediated signals might act as a switch toward leukemogenesis at the early stages of ATL development . It has been reported previously that ATL cells frequently express IL-10 and the majority of acute ATL patients present with serum IL-10 levels higher than chronic ATL patients , asymptomatic HTLV-1 carriers and healthy donors , with serum IL-10 concentration increasing significantly with disease progression [22 , 33] . Since IL-10 is an immunoregulatory cytokine suppressing inflammation and Th1 responses [36] , the increased IL-10 levels in ATL patients have been implicated in the severe immunosuppression associated with ATL [33] . In contrast , no significant difference in serum IL-10 concentration was found between HAM/TSP patients and asymptomatic patients [34 , 35] . Our present findings are consistent with these previous clinical observations , and further indicate the biological importance of IL-10 in proliferation of HTLV-1-infected cells . Notably , positive roles for IL-10 in the growth and survival of tumor cells have also been described in non-Hodgkin's lymphoma [51] , Burkitt’s lymphoma [52] and non-small cell lung cancer [53] . In the present study , we demonstrated that both cell cycling and apoptosis occurred simultaneously in ILTs . IL-10 increased Ki67 , cleaved caspase-3 , and survivin expression ( Fig 2 ) . The presence of both Ki67 and cleaved caspase-3 has been reported previously in primary ATL cells [54] . Expression of survivin has also been reported in primary ATL cells , and high survivin expression is linked to low survival rates in ATL patients [40–42] . Given the fact that survivin is a member of the inhibitor of apoptosis family [55] , the levels of apoptosis in ILTs might reflect the balance between pro- and anti-apoptotic machinery . It is speculated that ILTs continuously produce an apoptotic signal that takes over in the absence of IL-10 . When these cells are exposed to IL-10 , the apoptotic signal may be further enhanced , but apoptosis is blocked by survivin . The binding of survivin to p19 fragments of cleaved caspase-3 may block further cleavage resulting in accumulation of p19 fragments . Decreased p19 fragments of cleaved caspase-3 by STAT3 knockdown in ILTs confirmed that IL-10 induced this phenomenon through STAT3 ( Fig 4E ) . The signaling pathways downstream of IL-10 in ILTs involved STAT3 activation , which appeared to form a positive feedback loop for IL-10 production , as knockdown of STAT3 significantly decreased IL10 gene expression ( Figs 3C , 4A and 4B ) . This positive feedback potentially contributes to the overproduction of IL-10 in some ATL-derived ILTs . In IL-10-producing ATL-derived ILTs , however , the contribution of their own IL-10 to the cell growth seemed to be partial and varied among ILTs ( S6 Fig ) , suggesting the additional presence of IL-10-independent mechanisms of cell growth in these cells . Nevertheless , the suppressive effects of AS101 and cucurbitacin I on the cell growth in ILTs imply the importance of the IL-10-STAT3 pathway in these cells , which might potentially be a therapeutic target ( Fig 4C and 4D ) . STAT3 was also involved in the expression of BIRC5 ( survivin ) , MYC and IRF4 in ILTs , but not BCL2 or BCL2L1 , known target genes of STAT3 ( Fig 4A ) . This might be partly because these cells are IL-2-dependent . The presence of rhIL-2 in the assay medium might have compensated for the effects of si-STAT3 on the genes that could be upregulated by IL-2 . A constitutively activated STAT3 pathway is frequently found in ATL , as well as other malignant diseases [43 , 56] . Recent reports indicated that STAT3 mutations are frequently observed in ATL patients [45] . Once the cells acquire gain-of-function mutations of STAT3 , IL-10 may be dispensable . In the ILT cells tested , however , no mutations were found in the hotspots of the STAT3 gene ( S4 Fig ) , and IL-10 may still serve as a mediator to activate this pathway . Although IL-6 also activates STAT3 [57] , the effects of IL-10 dominated in ILTs , as IL-10 strongly enhanced the growth and STAT3 phosphorylation of HAM/TSP-derived ILTs that produced high levels of IL-6 ( Figs 1D , 1E and 3C ) . ATL-derived ILT-H2 cells produced the highest levels of IL-10 , and most strongly expressed Ki67 and IRF4 among the ILTs tested . Further treatment with rhIL-10 produced no change or sometimes slightly suppressed the cell growth ( Fig 1E ) . Since this cell line constitutively exhibits high levels of phosphorylated STAT3 , the additional activation might become toxic for the cells . It has been reported that excessive activation of the STAT3 pathway does not protect chronic lymphocytic leukemia cells from apoptosis [58] . Overexpression of IRF4 has been implicated in oncogenicity in multiple myeloma [59] , the activated B cell-like subtype of diffuse large B cell lymphomas [60] , primary effusion lymphoma [61] and peripheral T-cell lymphomas [62] . IRF4 directly enhances MYC , and MYC enhances IRF4 , generating an autoregulatory circuit in multiple myeloma [63] . Besides chromosomal translocation , constitutive NF-κB activation causes overexpression of IRF4 [62] . HTLV-1-infected and ATL cells also express IRF4 , and its contribution to enhanced cell growth by repressing DNA repair and pro-apoptotic genes has been suggested [50] . Although Tax can induce IRF4 expression , the mechanism of IRF4 expression in ATL cells remains controversial [49 , 64 , 65] . In the present study , IL-10 enhanced IRF4 expression , but mildly suppressed both NF-κB activity and Tax expression in HAM/TSP-derived ILT cells ( Figs 5A , 1F and 3A and S5B Fig ) . This suggests that , although the constitutively active NF-κB activity in ILTs could partly contribute to IRF4 expression , IL-10 further augmented IRF4 expression through STAT3 , independently of NF-κB or Tax . Interestingly , a previous study reported that constitutive IRF4 expression is exclusively observed in HTLV-1-transformed and ATL cells , but not in HTLV-1-infected cells from HAM/TSP patients [64] . Knockdown of IRF4 markedly reduced the cell number and survivin expression in ILTs , more efficiently than STAT3 knockdown ( Fig 5B–5D ) , indicating a primary role of IRF4 in cell survival in ILTs . Knockdown of STAT3 did not fully suppress IRF4 expression , presumably because IRF4 is additionally regulated by NF-κB . It is also possible that STAT3 might indirectly enhance IRF4 by affecting other molecules . The different effect on the cleaved caspase-3 levels between si-STAT3 and si-IRF4 ( Figs 4E and 5B ) suggests the presence of simultaneous pro- and anti-apoptotic signals by STAT3 , in which IRF4 predominantly contributes to anti-apoptotic signals . Intriguingly , recent reports indicated that IRF4 mutations are more common in aggressive ATL , while STAT3 mutations are characteristic of the indolent type of ATL [31 , 66] , suggesting unique roles of STAT3 in ATL leukemogenesis . The reason for the variation in IL-10 production among ILTs is unknown . There were no clear differences in helper T-cell subsets among ILTs tested ( S1 Fig ) . It might reflect differences in the host responses . Since our previous study suggested that HTLV-1 provirus-encoded antisense RNAs at the LTR region potentially activate PKR , leading to NF-κB activation [32] , it is conceivable that such anti-viral intrinsic mechanisms could influence IL-10 induction . Notably , polymorphisms in the promoter region of IL-10 are thought to be associated with various diseases , the outcome of both HCV [67] and HIV-1 [68] infection . For HTLV-1 infection , the IL-10-592A/C SNP affects Tax-induced transcription and susceptibility to HAM/TSP , ratifying the importance of this cytokine in the disease outcome of HTLV-1 infected patients [69] . However , this SNP may not be applicable to the Brazilian HAM/TSP population [70] . Further mechanisms responsible for the difference in IL-10 responses in HTLV-1 infection remain to be clarified . In conclusion , the anti-inflammatory cytokine IL-10 and its downstream signaling act as a switch for the proliferation of HTLV-1-infected cells through activation of the STAT3 and IRF4 pathways . The IL-10-dominant microenvironment may be a critical host factor allowing the HTLV-1-infected cells to proliferate in the early stages of HTLV-1 leukemogenesis , partly explaining the mechanism , which until now has been unexplained , how HTLV-1 causes lymphoproliferative and inflammatory diseases without disease-specific virus variation . The present findings will contribute not only to the understanding of the disease mechanisms , but also to prediction of the disease risks and prophylactic strategies against disease development in HTLV-1 infection .
This study was approved by the Medical Research Ethics Committee of Tokyo Medical and Dental University . IL-2-dependent HTLV-1-infected T-cell lines ( ILTs ) were established from the PBMCs of patients with HAM/TSP ( #294 , #439 , #441 ) and ATL ( #227 ) who donated their blood samples after providing written informed consent . We also used old established cell lines ILT-22 and ILT-H2 that had been similarly established from the PBMCs of ATL patients [71] . The original blood samples or PBMCs used for establishing ILTs were provided from the St . Marianna University School of Medicine ( Kanagawa , Japan ) , Imamura Bun-in Hospital ( Kagoshima , Japan ) and Kumamoto University School of Medicine ( Kumamoto , Japan ) following anonymization . ILTs were established by long-term culture of PBMCs of HTLV-1-infected patients in the presence of rhIL-2 ( Shionogi , Osaka , Japan ) for at least 6 months , following stimulation with phytohemagglutinin or Dynabeads Human T-Expander CD3/CD28 ( Invitrogen , Carlsbad , CA ) . ILT-22 , ILT-227 , and ILT-H2 are derived from patients with ATL , while ILT-294 , ILT-439 , and ILT-441 are derived from patients with HAM/TSP . ILTs were maintained in RPMI 1640 medium ( Sigma-Aldrich , St . Louis , MO ) containing 10% FBS ( Sigma-Aldrich ) , 100 U/mL penicillin , 100 μg/mL streptomycin ( Wako , Osaka , Japan ) , supplemented with 30–50 U/mL ( ILT-22 , -227 , -H2 ) or 50–100 U/mL ( ILT-294 , -439 and -441 ) of rhIL-2 . As the growth speed of HAM/TSP-derived ILTs was extremely slow , rhIL-10 ( 20 ng/mL , PeproTech , London , United Kingdom ) was added to cultures to expand these cells , and then cultured in rhIL-2-containing medium without rhIL-10 at least for 1 week before use in most of the experiments . PBMCs from a healthy individual that had been activated by Dynabeads Human T-Expander CD3/CD28 ( Invitrogen ) were also used . HTLV-1-infected cell lines MT-2 [72] and TL-OmI [73] , and HTLV-1-negative Jurkat [74] and MOLT4 [75] cells were cultured in RPMI 1640 medium containing 10% FBS . For flow cytometry , PE or FITC-labeled anti-human CD4 , CD8a , PE-labeled anti-human CDw210 ( IL-10 receptor α ) , FITC-labeled Annexin V ( BD Biosciences , San Jose , CA ) , Alexa Fluor 488-labeled anti-human Ki-67 ( Biolegend , San Diego , CA ) , PE-labeled anti-human IRF4 ( eBioscience , San Diego , CA ) , and their isotype controls were used . To detect HTLV-1 Tax , Alexa Fluor 488-conjugated Lt-4 [76] and its isotype control ( mouse IgG3 ) antibodies were used . For immunoblotting assays , antibodies specific for phospho-NF-κB p65 ( Ser536 ) , NF-κB p65 , phospho-NF-κB p100 ( Ser866/870 ) , NF-κB p100/p52 , cleaved caspase-3 , caspase-3 , survivin , phospho-STAT3 ( Tyr705 ) , STAT3 and IRF4 were purchased from Cell Signaling Technology ( Beverly , MA , USA ) . Antibodies specific for α-tubulin and β-actin were obtained from Sigma-Aldrich ( Buchs , Switzerland ) . AS101 , the non-toxic tellurium IL-10-inhibitor ( Tocris , Ellisville , MO ) [47] and cucurbitacin I ( JSI-124 ) , a STAT3-inhibitor ( Tocris ) [48] were also used . For cell surface staining , cells were incubated with antibodies for 20 min on ice . For intracellular staining , cells were fixed and permeabilized using fixation/permeabilization buffers ( eBioscience ) according to the manufacturer’s instructions prior to incubation with antibodies . To detect intracellular Tax protein , cells were fixed with 20 μg/ml lysolecithin/1% paraformaldehyde for 5 min , and permeabilized with methanol for 15 min followed by treatment with 0 . 1% Triton-X for 5 min on ice , and then incubated with Alexa Fluor 488-labeled Lt-4 or isotype control antibodies . For multiplex bead-based immunoassay , culture supernatants were incubated with beads coated with antibodies to various cytokines using the LEGENDplex kit ( Biolegend ) following the manufacturer’s protocol . The stained cells and beads were analyzed on a MACSQuant Analyzer ( Miltenyi Biotec , Bergish Gladbach , Germany ) , and the data were analyzed using FlowJo ( Tree Star Inc . , Ashland , OR ) or LEGENDplex ( Miltenyi Biotec ) software , respectively . ILTs were lysed in RIPA buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 1% Nonidet P-40 , 0 . 1% SDS ) containing protease and phosphatase inhibitor cocktail ( Roche Diagnostics , Basel , Switzerland ) and 1 mM PMSF . In some experiments , 10 μM of MG132 ( Peptide Institute , Osaka , Japan ) was added to the culture 3 h before harvest and also to the lysis buffer . Cleared cell lysates were denatured with sample buffer ( Thermo Scientific , Waltham , MA ) containing 2-mercaptoethanol , separated by SDS-PAGE and transferred to PVDF membrane ( ATTO , Tokyo , Japan ) . The membranes were blocked with Block Ace ( DS Pharma Biomedical , Tokyo , Japan ) or BSA ( Sigma-Aldrich ) and incubated with the indicated primary antibodies followed by a secondary incubation with horseradish peroxidase-conjugated anti-rabbit IgG ( Cell Signaling ) or anti-mouse IgG ( GE Healthcare , Pittsburg , PA ) antibodies . Bands were visualized by chemiluminescent substrate Novex ECL ( Invitrogen , Carlsbad , CA ) and ImageQuant LAS 4000 mini ( GE Healthcare ) . The images were analyzed using ImageJ software . Protein quantification was performed by using ImageQuant TL software ( GE Healthcare ) . Lentivirus particles ( Cignal Lenti Reporter Assay ) ( Qiagen , Duesseldorf , Germany ) containing firefly luciferase reporter genes for NF-κB ( NF-κB-luc ) , STAT3 ( STAT3-luc ) and renilla-luciferase reporter gene for thymidine kinase ( TK-RL ) were used for a transient reporter assay or establishment of stable reporter cell lines . For transient assays , ILTs were infected with a mixture of lentiviruses containing NF-κB-luc and TK-RL at a 2:1 ratio , and the luciferase activities were measured 4 days after infection . The stable ILT-H2 and ILT-294 reporter cells that have been established by infection with lentiviruses containing NF-κB-luc or STAT3-luc and TK-RL genes followed by puromycin selection were used for evaluating the effects of IL-10-treatment . Cells were lysed in Passive Lysis Buffer ( Promega , Madison , WI ) and the luciferase activities of the lysates were measured by a luminometer ( Berthold , Bad Wildbad , Germany ) using the Dual-Luciferase Reporter Assay System ( Promega ) . Values were normalized using Renilla luciferase activity . 106–107 cells were subjected to electroporation using a Neon Transfection System ( Invitrogen , Eugene , OR ) with 1 μM siRNA . The siRNA for STAT3 ( si-STAT3: sense , 5’-CACAUGCCACUUUGGUGUUUCAUAA-3’ ) , and control siRNA ( si-CTRL 47: sense , 5’-AGGUAGUGUAAUCGCCUUG-3’ ) were obtained through custom services of Invitrogen . To knockdown IRF4 or IL10 , three siRNAs targeting IRF4 ( si-IRF4-HSS 105508 , -HSS 105509 , -HSS 105510 ) or IL-10 ( si-IL10-HSS105365 , -HSS105366 , -HSS 179890 ) purchased from Invitrogen were used as a mixture , respectively . Cells were incubated for 48 h after electroporation , and then lysed in RIPA buffer for immunoblotting or in ISOGEN ( Nippon Gene , Tokyo , Japan ) for RNA extraction . For flow cytometry , cells were harvested 72 h after electroporation . The nucleotide sequences of the primers used for RT-PCR , proviral load measurement [17] , and DNA sequencing of the mutation hot spots of STAT3 [46] and IRF4 [77] genes are shown in S1 Table . The NCBI Primer-Blast Tool and qPrimerDepot database were used for designing primers . RNA extracted from cells was DNase treated ( Ambion , Austin , TX ) and reverse transcribed using First Strand cDNA Synthesis Kit with oligo ( dT ) 20 ( TOYOBO , Osaka , Japan ) . The resulting cDNA was then used as a template for quantitative RT-PCR using THUNDERBIRD SYBR qPCR Mix ( TOYOBO ) and a LightCycler 2 . 0 ( Roche ) . Quantified mRNAs were normalized to the ACTB or GAPDH mRNA level . DNA was extracted from ILT cells by using DNeasy Blood & Tissue kits ( QIAGEN , Courtaboeuf , France ) and subjected to quantitative PCR with HTLV-1 Tax-specific primers . The copy number per cell was calculated based on the Beta-globin copy number and further normalized against HTLV-1 proviral number of TL-OmI , which has been reported as 1 . 8 copies per cell [78] . The primers used are listed in S1 Table [17] . Statistical significance was tested by a two-tailed unpaired t test , and the difference between groups considered significant at p < 0 . 05 . | It has been a long-unsolved question why HTLV-1 can cause totally different diseases such as ATL and HAM/TSP , manifesting as malignant lymphoproliferation and chronic inflammation , respectively , without disease-specific viral differences . Although the constitutive NF-κB activation in HTLV-1-infected cells has been implicated for HTLV-1 pathogenesis , NF-κB potentially contributes to both leukemogenesis and inflammation . Here , we report that IL-10 and its downstream STAT3 pathway play a critical role in proliferation of HTLV-1-infected cells . Our finding that IL-10 converted the nature of the poorly proliferative HTLV-1-infected cells from HAM/TSP patients into rapidly-growing cells resembling characteristics of ATL cells implies the importance of the host IL-10-dominant microenvironment in building the proliferative machinery in HTLV-1-infected cells . The positive-feedback regulation between IL-10 and STAT3 would likely accelerate this process . These findings emphasize the importance of the STAT3 pathway in addition to the NF-κB pathway in the process leading to leukemogenesis in HTLV-1 infection . This study provides evidence that the host microenvironment is potentially involved in the disease mechanism , partly explaining the reason for the different disease outcomes in HTLV-1 infection . | [
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"... | 2017 | IL-10-mediated signals act as a switch for lymphoproliferation in Human T-cell leukemia virus type-1 infection by activating the STAT3 and IRF4 pathways |
Vascular branching morphogenesis is activated and maintained by several signaling pathways . Among them , vascular endothelial growth factor receptor 2 ( VEGFR2 ) signaling is largely presented in arteries , and VEGFR3 signaling is in veins and capillaries . Recent reports have documented that Snail , a well-known epithelial-to-mesenchymal transition protein , is expressed in endothelial cells , where it regulates sprouting angiogenesis and embryonic vascular development . Here , we identified Snail as a regulator of VEGFR3 expression during capillary branching morphogenesis . Snail was dramatically upregulated in sprouting vessels in the developing retinal vasculature , including the leading-edged vessels and vertical sprouting vessels for capillary extension toward the deep retina . Results from in vitro functional studies demonstrate that Snail expression colocalized with VEGFR3 and upregulated VEGFR3 mRNA by directly binding to the VEGFR3 promoter via cooperating with early growth response protein-1 . Snail knockdown in postnatal mice attenuated the formation of the deep capillary plexus , not only by impairing vertical sprouting vessels but also by downregulating VEGFR3 expression . Collectively , these data suggest that the Snail-VEGFR3 axis controls capillary extension , especially in vessels expressing VEGFR2 at low levels .
During vascular morphogenesis , new vessels sprout from existing ones to generate a functional and hierarchical branched network [1] . The retina has widely been used as a model system to investigate the mechanism of vascular morphogenesis [2] . The retinal vasculature is composed of the superficial and deep plexus ( S2A Fig ) [3] . The superficial vascular plexus is a well-differentiated structure of arteries , veins , and capillaries , whereas the deep vascular plexus is composed of capillaries . At birth , mice have avascular retinas . By the first postnatal day ( P1 ) , the vessels emerge at the optic stalk and initially form the superficial vascular plexus , which begins centrally and proceeds peripherally . By P8 , the vessels are rapidly remodeled into a hierarchical structure that consists of arteries , veins , and capillaries . Beginning around P7 , vertical angiogenic sprouting generates from the mature part of the superficial plexus and penetrates into deep retinal layers . When the vertical vessels reach the inner and outer boundaries of the inner nuclear layer ( INL ) , the vessels turn sideways , sprout , and fuse to establish the deep vascular plexus . Vertically sprouting vessels can sense and respond to attractive and repulsive signals within their immediate microenvironment from the ganglion cell layer ( GCL ) through the INL to the outer plexus layer ( OPL ) . In the vasculature , several signaling pathways control endothelial cell ( EC ) sprouting , migration , and network expansion [1] . Examples of these signaling components are Notch , vascular endothelial growth factor receptor ( VEGFR ) 2/3 , Delta-like ligand 4 ( Dll4 ) , and bone morphogenetic proteins ( BMPs ) . Dll4 expression is dynamically regulated and associated with actively growing vessels , but its expression is gradually reduced with the cessation of angiogenic sprouting in the superficial plexus . In mature vessels of mice at P9 , Dll4 is expressed in arteries rather than in veins [4] . VEGFR2 and 3 are expressed in the specialized tip cells of actively growing vessels . After the maturation of the superficial plexus , VEGFR2 is largely detected in arteries , and VEGFR3 is expressed in veins and capillaries [5 , 6] . VEGFR2 triggers multiple downstream signals and consequently stimulates ECs to guide proper angiogenic sprouting vessels , filopodia extension , and network expansion . On the other hand , VEGFR3 has dual activities , where it can promote but also inhibit angiogenesis [7] . VEGFR3 blocks angiogenesis by interfering with VEGFR2 signaling in the superficial plexus . VEGFR3 signaling can also be pro-angiogenic via VEGFC- and extracellular matrix ( ECM ) component-mediated signals , and it plays an important role in venous angiogenesis and lymphangiogenesis [7–9] . However , the induction mechanism and functional roles of proangiogenic VEGFR3 remain largely unknown . The Snail family of zinc-finger transcription factors is comprised of Snail1 ( Snail ) , Snail2 ( Slug ) , and Snail3 [10] . Snail is localized to the cytoplasm and nucleus , whereas Slug is localized to the nucleus . Most functions of the Snail family , such as epithelial-to-mesenchymal transition ( EMT ) , survival , cell motility , and cell movement , have been studied in epithelial cells [11 , 12] . During epithelial branching morphogenesis , epithelial cells induce the expression of the Snail family at the leading edge of growing branches and appear to undergo EMT [13] . Furthermore , in the Drosophila trachea , branchless ( a fibroblast growth factor ligand ) signaling establishes tip/stalk cells and controls the expression of escargot , which is a Drosophila homolog of Slug that is involved in branch fusion [14] . Recent studies have demonstrated remarkable similarities between epithelial morphogenesis and angiogenic sprouting with regard to the organization of sprouting cells into the tip and stalk , cell migration , and fusion between tip cells [1] . Accumulating evidence has indicated that the Snail family may participate in vascular branching morphogenesis . The vascular effects of Snail have been revealed in embryos of mice with the epiblast-specific deletion of Snail [15] . Snail deletion results in the failure to form appropriately interconnected vascular networks . In Xenopus vascular development , the ectopic expression of Slug/Twist is sufficient to rescue a Myc knockdown-induced vascular defect [16] . Notably , in extracted lysates from Dll4+/- retinal ECs , Slug is expressed in highly motile tip cells [17] . More recently , Slug has been shown to be associated with sprouting angiogenesis by inducing membrane type 1-matrix metalloproteinase ( MT1-MMP ) in vitro [18] . Parker et al . [19] have demonstrated that Snail is detected in the extracts of ECs isolated from invasive breast ductal tumors; however , Snail is undetectable in the normal breast . Although the evidence seems to support a role for the Snail family in the developing vessels and tumor vasculature , the precise expression pattern and cellular function of Snail in vascular morphogenesis remain unclear . To gain insight into the spatiotemporal induction of global genes during vascular morphogenesis , we used Affymetrix oligonucleotide arrays ( GRE accession number GSE12891 ) to compare their mRNA levels at time points that corresponded to dramatic morphological changes during EC network formation . In this study we showed that Snail was dynamically and predominantly expressed in active vessels . We evaluated the role of Snail on VEGFR3 in capillary branching morphogenesis .
Affymetrix oligonucleotide arrays ( GRE accession number GSE12891 ) were used to compare the mRNA levels of global genes at time points that corresponded to dramatic morphological changes during vascular morphogenesis . Specifically , we looked for genes that were altered during EC network formation , because they may influence endothelial morphological changes in response to cell-cell and cell-ECM interactions ( S1 Fig ) . Snail and Slug expression levels were dramatically increased in those processes . Quantitative reverse transcription-polymerase chain reaction ( qRT-PCR ) and western blot analyses confirmed that Snail mRNA and protein levels were dramatically increased at 1 and 2 h when the behavior of ECs was robust ( Figs 1A and S1A ) . At 4 h when vascular network formation was complete , Snail expression disappeared . Although Slug mRNA expression dramatically increased , Slug protein levels could not be detected , thus suggesting that Slug protein is highly unstable during vascular network formation ( Fig 1A , middle and right ) . Furthermore , we found that ectopic expression of Slug in human umbilical vein ECs ( HUVECs ) dramatically increased Snail , which suggests that Slug could be upstream of Snail ( Fig 1B ) . Similar to our finding , Slug has been reported to be indirectly involved in epithelial branching via Snail upregulation [13] . The differential function between Snail and Slug has been suggested , such that Slug is predominantly effective in cell survival , whereas Snail is involved in invasive and migrating events . Hence , we focused on the role of Snail in the angiogenic process , although Snail and Slug appear to play roles in vascular morphogenesis . We next investigated whether Snail was involved in vascular development in vivo . The retinal vasculature is composed of the superficial plexus and deep plexus ( Figs 1C and S2A ) [20] . Whole flat-mount analysis showed that the immunoreactivity of Snail was found in active vessels at P5 ( S2B Fig ) . Z-stack analysis indicated that Snail was located in the nuclei of sprouting cells , as shown in the x-z axis and y-z axis ( S2C Fig ) . At P8 , Snail was detected in sprouting vessels from the vein ( Fig 1D ) . At P11 , the deep plexus was observed , and Snail was detected in the vertical vessel of the cross-section and in whole flat-mount retinas ( Figs 1E and S2D ) . In particular , Snail immunoreactivity was detected in the IPL and INL where vessels are sprouting and branching ( S2D and S2E Fig ) . However , Snail expression was not detected after completion of the deep plexus ( S2D Fig , OPL ) . These results demonstrate that Snail was expressed in the sprouting vessels prior to vascular plexus formation . The role of Snail in vascular sprouting was examined in HUVECs using the Matrigel plug assay . Matrigel plugs containing the small hairpin ( sh ) Lenti Snail virus ( shSnail ) attenuated vascular formation compared with the shLenti control virus ( shCon ) ( Fig 1F ) . Immunohistochemical analysis indicated that mouse ECs infiltrated into Matrigel plugs containing shSnail , but the vessel ingrowth abilities of ECs displayed deficits ( Fig 1G and 1H ) . In the fibrin gel setting , Snail was detected in sprouting ECs ( Fig 1I ) . In mixed culture , Snail small-interfering RNA ( siSnail ) -transfected HUVECs failed to sprout and migrate toward the fibrin-gel matrix , and the ECs remained on the beads , whereas control siRNA ( siCon ) -transfected HUVECs migrated and sprouted ( Fig 1J ) . Knockdown of Snail attenuated the ability of ECs to sprout from the bead ( Fig 1K ) . Furthermore , we embedded individual ECs inside a three-dimensional fibrin gel and assessed the sprouting ability of the cells ( S3 Fig ) . The sprouts from siSnail-transfected ECs exhibited a dead-end morphology , whereas those from siCon-transfected ECs sharply extended within the fibrin gel ( S3A Fig , arrows ) . SiSnail-transfected ECs were also less able to form tubes ( S3A Fig ) . In contrast , ectopic Snail increased EC sprouting and branching ( S3B Fig , arrow head ) . Furthermore , ectopic expression of Snail ( 6SA ) , an unleashed form from GSK3β-proteosomal degradation , enhanced the ability of EC sprouting ( S3B Fig ) . Therefore , the data suggest that Snail is essential for the initiation and induction of vascular sprouting and branching . The developing retinal vasculature into the deep retinal layer is influenced by ECM-mediated integrin signals and retinal neuron-induced hypoxic signals [3 , 21] . On the basis of the finding that Snail immunoreactivity was detected in the invading vessels into surrounding matrix microenvironment , we investigated the mechanism by which ECM could regulate Snail expression . Fibronectin and collagen type I are major ECM components that are involved in angiogenesis [22] . Exposure of HUVECs to these ECM components dramatically induced Snail protein and mRNA expression ( Fig 2A and 2B ) . In comparison , exposure of HUVECs to poly-L-lysine ( PLL ) , a non-specific adhesion facilitator , only slightly induced Snail protein and mRNA expression . In normal , cultured ECs , Snail protein is unstable and can only be detected in the presence of proteosome inhibitors [10] . Several studies examining the stability of Snail protein have shown that Snail is rapidly degraded via the glycogen synthase kinase ( GSK ) 3β-dependent proteosomal system in epithelial cells . Activated Akt can phosphorylate GSK3β , and this process stabilizes Snail by releasing it from the GSK3β system [23] . We found that exposure of HUVECs to the ECM induced Akt phosphorylation ( Fig 2C ) . Thus we examined whether the maintenance of Snail protein on ECM component was due to Akt activity . Pretreatment with MK2206 ( an allosteric Akt inhibitor ) attenuated fibronectin- and collagen type I-mediated Snail induction in protein level ( Fig 2D , left and middle ) . In contrast , mRNA level of Snail showed slight decrease ( Fig 2D , right ) . Although further experiments are required to better understand the transcriptional regulation of Snail by ECM signaling , these results suggest that ECM-induced Snail protein in ECs was stabilized by Akt signals , which prevented Snail from GSK3β-proteosomal degradation . On the basis of our finding that Snail was dominantly expressed in the sprouting vessels and regulated by ECM signals , we investigated whether Snail influenced the expression of EC sprouting-related genes , including VEGFRs and Neuropilin ( NRP ) . In particular , we have focused on VEGFR3 , because VEGFR3 is known to interact with ECM and ECM ligands , including fibronectin and integrin α5β1 [9 , 24] . Furthermore , VEGFR3 is highly expressed in leading-edged ECs that undergo sprouting and migration but is weakly and rarely expressed in phalanx ECs and quiescent ECs [1] . Thus , VEGFR3 needs to be induced for resting ECs to initiate angiogenesis . Most studies on VEGFR3 expression have focused on lymphatic ECs , and VEGFR3 is induced by the formation of prospero homeobox protein 1 ( Prox1 ) -COUP transcription factor 2 ( CoupTFII ) , Prox1-nuclear factor-κB , or Prox1-Ets complexes [25 , 26] . In blood ECs , the binding of Notch to the VEGFR3 promoter can induce VEGFR3 mRNA [27] . However , the induction mechanism of VEGFR3 in angiogenically active blood ECs is largely unknown . To determine whether ECM could induce the expression of VEGFRs in ECs , human retinal endothelial cells ( HRECs ) and HUVECs were exposed to fibronectin . Fibronectin , but not PLL , dramatically increased VEGFR3 mRNA and protein expression ( Figs 3A and S4A ) . Interestingly , Snail knockdown with siRNA reversed ECM-mediated VEGFR3 upregulation at the protein and mRNA levels but showed no effect on VEGFR2 and NRP ( Fig 3B and 3C ) . The increase in VEGFR3 was confirmed by the ectopic expression of Snail ( Fig 3D ) . To explore whether Snail mediated VEGFR3 via the enhancement of VEGFR3 promoter activity , we employed the luciferase reporter system . Exposure of ECs to ECM components enhanced VEGFR3 promoter activity ( Fig 4A ) . VEGFR3 promoter activity was downregulated and upregulated by Snail knockdown and ectopic Snail , respectively ( Figs 4D , S4B and S4C ) . Because Notch activates VEGFR3 promoter activity [27] , we examined whether the ECM-mediated increase in VEGFR3 was Notch dependent . Notch siRNA ( siNotch ) transfection slightly downregulated VEGFR3 promoter activity . A similar effect was observed with DAPT , which is an inhibitor of the γ-secretase and Notch response ( S4D Fig ) . Therefore , the intracellular domain of Notch is unlikely to be a transcriptional regulator of VEGFR3 under the influence of ECM in our system . The Snail family is known to act as a transcriptional repressor for tight junction genes , polarity-related genes , and cell cycle regulators by directly binding to their conserved E-box element [10] . Nonetheless , many genes are also upregulated by the Snail family , which suggests that it functions as a transcriptional activator . Several reports indicated that Snail interacts and cooperates with the Egr-1/Sp1 complex to enhance the promoter activity of its target genes , and Egr-1 is implicated in several vascular disease states and fibroblast growth factor 2-mediated angiogenesis [28–30] . By screening the TRANSFAC MATRIX TABLE , we found that the promoter region of human VEGFR3 contained multiple conserved Sp1-binding sites , a nearby conserved Egr-binding element , and a putative E-box element located within approximately 200 bp upstream from the initiation of VEGFR3 mRNA ( Fig 4B ) . Exposure of ECs to fibronectin induced Egr-1 , Snail , and VEGFR3 ( Figs 3A and 4C ) . Knockdown of Egr-1 decreased VEGFR3 protein expression and VEGFR3 promoter activity , suggesting the involvement of Egr-1 in VEGFR3 transcription ( Figs 4C , 4D , and S4B ) . To examine direct involvement of Snail in VEGFR3 promoter activity , we performed the mutagenesis of putative E-box elements ( Fig 4B ) . Site-directed mutagenesis of the VEGFR3 promoter region significantly reduced Snail-induced VEGFR3 promoter activity , thus demonstrating the requirement of Snail for VEGFR3 promoter activity ( Fig 4E ) . To determine whether the intimate binding region of the E-box and Egr-1 in the VEGFR3 promoter could lead to the interaction between Snail and Egr-1 , we exposed HRECs to fibronectin for 2 h to induce Snail and Egr-1 and performed the immunoprecipitation assay . Because Snail is a zinc-finger transcription factor , we added the zinc ion to HREC lysates . Incubation of EC lysates with anti-Egr-1 revealed the interaction between Egr-1 and Snail ( Fig 4F ) . Co-treatment with ethylenediaminetetraacetic acid ( EDTA ) , a zinc chelator , inhibited the binding , which suggests that the interaction of Egr-1 with Snail is specific and is apparently related to its transcriptional activity ( Fig 4F ) . To examine the direct binding of Snail to the promoter region , we performed chromatin immunoprecipitation ( ChIP ) analysis in Snail-overexpressing HUVECs . The VEGFR3 promoter region containing Snail and Egr-1-binding sites was co-immunoprecipitated with anti-Snail antibodies ( Fig 4G ) . These results demonstrate that Snail and Egr-1 were induced and stabilized under ECM signals . Subsequently , Snail bound to the VEGFR3 promoter through Egr-1 cooperation , thus leading to the transcriptional activation of VEGFR3 in ECs . Growing venous and capillary vessels have dominant VEGFR3 expression under physiological and pathological conditions , whereas VEGFR2 and Dll4 are strongly expressed in arterial vessels [6] . In the developing retina , the deep vascular plexus is a unique vessel network with capillary vascular plexus . Combined these reports , we assumed VEGFR3 expression in the deep capillary plexus and probed for the expression of VEGFR3 in postnatal retinal angiogenesis . Prior to the experiments , we validated the anti-VEGFR3 antibody that was used in this study by whole-mount cornea staining ( S5A–S5C Fig ) . The immunoreactivity of VEGFR3 colocalized with those of isolectin B4 ( iB4; a blood vessel marker ) and lymphatic vessel endothelial receptor ( LYVE; a lymphatic vessel marker ) in the developing cornea , which indicates that the antibody is suitable for detecting VEGFR3 . At P11 , the superficial plexus had been fully formed , and it vertically extended toward the deep retina . We determined that the immunoreactivity of VEGFR2 was strong in the GCL of retinas ( Fig 5A , upper panel , arrows ) . In contrast , the immunoreactivity of VEGFR3 was weak in the same area ( Fig 5A , middle , arrows ) . Interestingly , VEGFR3 was strongly detected in vertically invading capillaries toward the deep retina ( Fig 5A , middle panel , triangles ) . Serial z-axis analysis showed that VEGFR3 was highly expressed in deep capillary vessels and migrating and sprouting ECs ( S6 Fig ) . VEGFR2 was barely detected in the vertical vessels , but it appeared to be expressed in neuronal cells ( Fig 5A , upper panel , triangles and arrow heads ) . Furthermore , we probed for the expression of VEGFR3 in sprouting vessels from venous vessels of the superficial plexus in the P8 retina and found that some venous ECs and sprouting ECs that invaded toward the deep retina showed prominent immunoreactivity of VEGFR3 ( Fig 5B and 5C , arrows ) . The results suggest that VEGFR3 and VEGFR2 are differentially expressed in angiogenic vessels . VEGFR3 was strongly induced by sprouting angiogenic cells toward the deep retina to undergo capillary extension and formation , whereas VEGFR2 was strongly expressed in vessels in the superficial plexus . To examine the role of Snail in the formation of vertical branching and deep capillary plexus , stable Snail siRNA ( siSnail ) was daily injected into mice from P7 to P10 or from P6 to P8 intraperitoneally ( Fig 6A ) . The efficacy of the siSnail was validated by quantitative RT-PCR at P11 and whole flat-mount analyses at P9 ( Figs 6B and S7A ) . Moreover knockdown of Snail significantly downregulated VEGFR3 expression in whole retinal lysates ( Fig 6B ) . Whole flat-mount analysis showed that the deep plexus was formed from the optic stalk to the retinal margin at P11 ( Fig 6C ) . Snail knockdown impaired the formation of the deep plexus ( Figs 6C and S7A , OPL ) . The distance of the vasculature from the optic stalk to the margin was decreased in siSnail mice . Furthermore , the vertical vessels from the superficial plexus were decreased ( Fig 6D , IPL; S7A Fig , GCL ) . The numbers of vertical vessels that sprouted from the vein were reduced in siSnail retinas and vessel branch points in the deep plexus were also reduced ( Figs 6D , 6E , and S7A , IPL and OPL ) . Confocal z-stack analysis showed the attenuation of vertical vessels in siSnail retinas , compared to siCon retinas ( Fig 6F ) . To avoid the off-target effects of stable siSnail , we utilized the shSnail system , as described in Fig 1F–1H . After intraperitoneal treatments with shShail , whole flat-mount studies showed the reduction in vertical sprouting from the superficial plexus ( Figs 6A and S7B ) . These data demonstrate that Snail played a crucial role in venous vertical sprouting and in the formation of the deep capillary plexus . Whole flat-mount staining was performed to assess whether Snail colocalized with VEGFR3 in sprouting vessels ( Fig 7A ) . Expression of VEGFR3 and Snail was high in veins of the GCL at P8 , demonstrating that their colocalization could be related to venous sprouting and extension . We thus investigated whether Snail knockdown-induced sprouting defects could be related to VEGFR3 expression in vivo . Knockdown of Snail by shSnail injection at consistent intervals reduced both VEGFR3 immunoreactivity and sprouting in the vein at P8 ( Figs 6A and 7B ) . Results from confocal z-axis analysis demonstrate that Snail knockdown reduced the intensity of iB4 immunoreactivity and VEGFR3 expression in retinas at P11 ( Figs 7C , 7D and S8 ) . To investigate whether VEGFR3 triggered the formation of the retinal deep vasculature , we used MAZ51 to block VEGFR3 receptor kinase activity . Treatments with MAZ51 at P4 and P5 significantly reduced the vasculature , radial length , and sprouts at P6 ( S9A–S9C Fig ) . However , MAZ51 treatment from P7 to P10 did not impair the retinal deep vasculature ( S9D Fig ) . The total vessel area and vascular density of MAZ51-treated retinas were not different from that of vehicle-treated retinas ( S9E and S9F Fig ) . These data indicate that the retinal deep vasculature was not dependent on VEGFR3 receptor kinase activity . Similar results have been reported by others [9 , 31] . They showed that collagen type I-induced VEGFR3 phosphorylation is not blocked by MAZ51 in vitro [9] . However , the superficial plexus is inhibited by MAZ51 treatment in vivo [31] . Therefore , we speculated that VEGFR3 activation might be different between the superficial and deep vascular plexus in the retina . VEGFR3 has been shown to bind to integrin upon fibronectin exposure and activate downstream signals [7 , 9] . We examined whether there was a relationship between VEGFR3 and integrins in the formation of the deep vasculature . Whole flat-mount staining and z-stack analysis revealed that venous sprouting vessels showed strong CD29 ( integrin β1 ) immunoreactivity in retinal vessels at P8 ( S9G and S9H Fig ) . The immunoreactivities of VEGFR3 colocalized with those of CD29 in sprouts that extended from the vein ( S9I Fig ) . Next , we asked whether integrin-mediated VEGFR3 activation could induce deep plexus development . In vitro experiments have demonstrated that CD29 and c-Src can form a complex with VEGFR3 by integrin engagement to the ECM [9] . They demonstrate that CD29 recruits c-Src , which then phosphorylates VEGFR3 . This phosphorylation of VEGFR3 by c-Src is suggested to be distinct from that by VEGFR3 receptor kinase activity [9] . Furthermore , they showed that the exposure of ECs to ECM induces the phosphorylation of VEGFR3 by c-Src , and this process was blocked by PP2 , which is a Src family inhibitor . To further investigate the role of the Src family in the development of the deep vasculature , we administered intraperitoneal injections of PP2 . PP2 attenuated the formation of the deep vasculature ( S9J Fig ) and inhibited vascular network formation ( branching points ) and sprouting ( red-broken circles ) in the OPL ( S9K Fig ) . These data suggest that VEGFR3 is activated in the receptor kinase activity-independent manner during deep plexus development . Because ECM-mediated signals upregulated the Snail-VEGFR3 axis , we expected that Snail-mediated VEGFR3 upregulation may facilitate angiogenic sprouting and migration toward the fibrin gel . Results from the fibrin sprouting assay suggest that Snail overexpression promotes angiogenic sprouting with regard to sprout number , length , and branch points . Knockdown of VEGFR3 reduced Snail-mediated sprouting ( Fig 7E ) . Overall , our data suggest that Snail-mediated VEGFR3 expression plays a crucial role in sprouting angiogenesis , particularly in the process of deep capillary plexus formation
Findings from this study show that ( a ) Snail was induced in angiogenically activated ECs in the postnatal retinal vasculature via ECM signaling; ( b ) the Snail-Egr-1 complex upregulated VEGFR3 mRNA; and ( c ) Snail knockdown attenuated the formation of the deep vascular plexus by impairing vertical sprouting and affecting VEGFR3 expression . Collectively , the data demonstrate that a Snail-VEGFR3 axis contributed to the extension of capillary vessels and venous vessels ( Fig 8 ) . The regulated expression of genes to sense and interpret intrinsic and extrinsic changes is needed for functional and efficient sprouting of ECs in space and time [32 , 33] . Genetic experiments have established VEGFR3 as a negative regulator that interferes with VEGFR2 and NRP complex formation in the superficial plexus , which highly expresses VEGFR2 [7] . Interestingly , VEGFR3 has also been suggested to positively regulate angiogenesis under certain conditions , including ECM signaling and VEGFR2-independent conditions [5 , 7 , 31] . In addition , VEGFR3 is strongly expressed in capillaries of interductal breast tumors and in small vessels that are incompletely covered by perivascular cells , thus demonstrating a role of proangiogenic VEGFR3 in capillary extension [34] . Studies in zebrafish have demonstrated VEGFR3-dependent hyperbranching in the absence of Dll4 and suggested that VEGFR3 is indispensable for venous angiogenesis [8] . Thus , the differential expression patterns of VEGFR2 , VEGFR3 , and Dll4 may be related to variations in vessel patterning . As a result , we sought to investigate whether the differential expression pattern and functionality of VEGFRs may be due to changes in the microenvironments in which vascular branching morphogenesis occurs . In developing retina vasculature , the vertical vessels extend and migrate from the GCL ( rich in nuclei ) to IPL ( rich in neurites ) , and they seem likely to experience microenvironments that may differ with regard to ECM components and growth factors [3 , 21] . Our findings show that ECM components played an important role in VEGFR3 expression . Moreover , VEGFR3 expression was high in vertical vessels and the deep capillary plexus . We and others have shown that VEGFR2 is strongly expressed in neurons , but not ECs , in the deep retina [35] . Moreover , Okada et al . have shown that the neuronal expression of VEGFR2 can inhibit vertical angiogenesis by titrating soluble VEGFA [35] . Based on these reports , we assumed that VEGFR3 expression in deep retinal ECs could contribute to angiogenesis , whereas VEGFR2 expression in neurons could limit retinal angiogenesis . Our data show that stable siSnail mice exhibited reduced VEGFR3 expression and defective vertical vessels , demonstrating that the Snail-VEGFR3 promoted angiogenesis under conditions involving ECM exposure and low levels of VEGFR2 . The formation of the deep plexus occurs in mice at P8 when venous vessels in the superficial plexus sprout vertically and extend ( Fig 8B ) [2 , 3] . The vertical sprouting begins in the center of the retina ( optic stalk ) and expands toward the margin . The extension of vertical vessels from the superficial plexus penetrates the retina to reach the boundary of the OPL . In the OPL , the vessels sprout and interact sideways to form the capillary network . Several reports have suggested a potential involvement of perivascular or extravascular cells in the formation of the deep retina plexus . Neuroglial expression of VEGFA in the INL seems to regulate the timing for vertical sprouting [21 , 35] . The border of the INL seems to express VEGFA for induction of the deep plexus . Macrophages have also been shown to regulate deep vessel branches [7 , 36 , 37] . M2-type macrophages can promote angiogenesis by secreting growth factors , such as VEGFC , whereas M1-type macrophages can inhibit angiogenesis by initiating programmed EC death and engulfing dying cells [38] . In particular , the macrophage can act as a bridge between two tip cells in the sprouts to establish a vascular network via VEGFR3 in the process of sprout anastomosis [7] . However , it is suggested that macrophages can also have anti-angiogenic effects in the deep vasculature . When vertical sprouts are close to initiating the deep plexus in the INL and OPL , macrophages are in close proximity to the vertical sprouts and can inhibit their branching . Wnt and VEGFR1 pathways are involved in the inhibition of vertical branching in the developing retina [36] . In addition , the production of the Notch ligand , Delta-like 1 , by extravascular cells is essential for endothelial sprouting toward the deep retina [39] . Further insights into the development of the deep plexus can be gained by using adhesion molecules and ECM components . R-cadherin levels are increased in the border of the INL region , where the deep plexus is formed [40] . The ECM component , fibronectin , may affect the vascular phenotype , as it accumulates around sprouting vessels [41] . We also show through in vitro and in vivo studies that ECM-induced Snail plays an important role in the initiation process of venous sprouting from the superficial plexus in the deep retina vasculature . Regarding the downstream of ECM , it is reported that ECM-activated Src and Rho initiate capillary morphogenesis via Snail in vitro [42] . In addition , in vitro study shows that integrin-mediated VEGFR3 is activated in a c-Src-dependent manner , and this activity of VEGFR3 is distinct from that of ligand-induced receptor kinase activity that is inhibited by MAZ51 [9] . We also found in this study that MAZ51 did not attenuate , but the Src family inhibitor , PP2 , delayed and reduced the deep plexus . Although further studies are required to elucidate the role of endothelial c-Src in the deep plexus , our results suggest that the Snail-mediated increase in VEGFR3 can facilitate and augment sprouting vessels by c-Src . Hence , the orchestrated combination of the ECM-mediated induction of the endothelial Snail-VEGFR3 axis , Wnt-VEGFR1 pathway , and neuronal VEGFR2 may serve as triggering cues for the formation of the deep capillary plexus . The vascular capillary is flexible and dynamic . It repeatedly appears or disappears in response to different physiological and pathological states . For example , both neuronal synaptic activity and ischemic diseases in the retina and brain are deeply correlated with the dynamics of vascular capillaries . Given that Snail and Egr-1 are increased in ischemic conditions [28 , 43] , the rapid induction of Snail and Egr-1 in veins and venules in response to local changes of the retina and brain can trigger the initiation of vessel sprouting . This may be followed by VEGFR3 upregulation , which induces EC sprouting and capillary morphogenesis . Furthermore , the induction and extension of tumor capillary vessels may be mediated via Snail and VEGFR3 in ductal breast tumors , because normal resting vessels do not express Snail and VEGFR3 [19 , 34] . Therefore , the spatiotemporal expression pattern of the Snail-VEGFR3 axis in response to local needs is likely to play a crucial role in transient capillary formation . Many studies have focused on the contribution of the Snail family to epithelial morphogenesis under physiological conditions and to the EMT phenomenon under pathological conditions [12 , 44] . Despite similarities between epithelial cells and ECs in their morphogenetic processes , the role of the Snail family in ECs has barely been studied . In our study , Snail-deficient ECs were unable to sprout and migrate . Similar results have been recently reported [18] . Epithelial branching morphogenesis requires Snail for branching initiation during mammary epithelial branching [13] . Furthermore , epithelial branching initiation is thought to be triggered by mesenchymal markers , such as Snail and vimentin , at branch sites . This is a potential role of partial EMT in branching morphogenesis . However , vascular branching may not be related to this process . The EC itself has been shown to exhibit mesenchymal-like characteristics and express high levels of vimentin [45] . Moreover , ECs can easily shuffle between the tip and stalk cells in angiogenic leading vessels . However , the possible presence of endothelial-mesenchymal transition ( EndMT ) in angiogenic processes has also been suggested [46] . Mice with endothelial-specific disruption of cerebral cavernous malformation-1 undergo EndMT by upregulating transforming growth factor-β and BMP signaling . Hence , whether our findings regarding Snail expression at endothelial branch points are akin to EndMT will require further analysis . Overall , we propose that Snail contributes to capillary formation through the initiation of venous sprouting ( Fig 8 ) . The induction and stabilization of Snail in response to local changes could promote morphological changes via the loss of EC junctions ( e . g . ZO-1 and Occludin ) , loss of EC polarity , and degradation of the basement membrane ( e . g . , MT-MMPs ) [18] . Snail also increased VEGFR3 expression to initiate EC sprouting , which subsequently led to the formation of the deep vascular network . Our findings provide mechanistic insights into the induction of EC branching events by factors that are specifically and transiently regulated within the microenvironment to enforce vascular branching morphogenesis , such as capillary morphogenesis .
HUVECs were isolated from human umbilical cord veins by collagenase treatment . Cells at passages 2–7 were used and cultured in EC growth medium ( EGM ) -2 supplemented with 10% fetal bovine serum ( FBS ) . HRECs were purchased from Applied Cell Biology Research Institute ( Kirkland ) , and passages 2–7 were used for experiments . HRECs were grown in EC basal medium ( EBM-2 ) containing the EGM-2 kit ( Clonetics , Lonza Walkersville ) and 10% FBS . Cells were transiently transfected using lipofectamine or the lipofectamine LTX_Plus system ( Invitrogen ) . For ECM experiments , ECs were reseeded at a density of 2–2 . 5×104 cells/cm2 in poly-L-lysine ( 20 μg/mL ) , ( fibronectin ( 20 μg/mL ) - or collagen type I ( 20 μg/mL ) -coated dishes in EBM-2 supplemented with 1–2% FBS for the indicated time points . Small-interfering Egr-1 ( siEgr-1; 5’-GUGCAAUUGUGAGGGACAU-3’ ) was from Bioneer Corporation ( Korea ) . Other siRNAs were ON-TARGET plus SMART pool siRNAs that were designed by Dharmacon , Inc . Flag-Snail and Flag-Snail ( 6SA ) were gifts from Mien-Chie Hung ( Addgene plasmids #16218 and #16221 , respectively ) . Flag-Snail ( 6SA ) has six mutation sites ( S974A , S101A , S108 , S112A , S116A , and S120A ) . Recombinant human VEGFA 165 was from Koma Biotech . , LTD . Fibronectin , collagen type I , and poly-L-lysine were from Sigma , BD Biosciences , and Sigma , respectively . Growth factor-reduced Matrigel ( BD Biosciences ) was placed on 60-mm culture dishes and polymerized for 0 . 5 h . HUVECs ( 1×106 cells ) were plated on the layer of Matrigel and cultured . Total RNA was isolated and hybridized to the HG-U133A 2 . 0 microarray ( Affymetrix ) , according to the manufacturer’s protocol . Full data sets are available online ( GRE accession number: GSE 12891 ) . Total RNA was purified using a TRIzol reagent kit ( Invitrogen ) . Semi-quantitative RT-PCR was performed with 2x Maxima SYBR , as described in the manufacturer’s manual ( Thermo Scientific ) . The primers used for amplification were as follows: Snail , 5’-CCTCAAGATGCACATCCGAAGCCA-3’ and 5’-AGGAGAAGGGCTTCTCGCCAGTGT-3’; Slug , 5’-CCCCCATGCCATTGAAGCTGA-3’ and 5’-GCGCCCAGGCTCACATATTCC-3’; Egr-1 , 5’-TGACCGCAGAGTCTTTTCCT-3’ and 5’-TGGGTTGGTCATGCTCACTA-3’; VEGFR1 , 5’-TCCTTTGGATGAGCAGTGTG-3’ and 5’-AGCCCCTCTTCCAAGTGATT-3’; VEGFR2 , 5’-CCAGTCAGAGACCCACGTTT-3’ and 5’-TCCAGAATCCTCTTCCATGC-3’; VEGFR3 , 5’-TTCCTGGCTTCCCGAAAGT-3’ and 5’-AGGCCAAAGTCACAGATCTTCAC-3’; glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) , 5’-ATGGGGAAGGTGAAGGTCG-3’ and 5’-GGGGTCATTGATGGCAACAATA-3’ . ECs were washed with cold phosphate-buffered saline ( PBS ) and harvested in radioimmunoprecipitation assay ( RIPA ) buffer supplemented with 50 mM β-glycerolphosphate , 0 . 1 mM sodium orthovanadate , 1 mM dithiothreitol , and a protease inhibitor cocktail . The amount of protein in each sample was measured using the bicinchoninic acid assay kit ( Thermo Scientific ) , and the proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) . Immunoblotting was performed with antibodies to Snail ( Cell Signaling Technology , Inc . ) , Slug ( Cell Signaling Technology , Inc . ) , VEGFR3 ( Santa Cruz Biotechnology ) , VEGFR2 ( Cell Signaling Technology , Inc . ) , phosphorylated Erk1/2 ( p-Erk1/2; Cell Signaling Technology , Inc . ) , phosphorylated Akt ( p-Akt; Cell Signaling Technology , Inc . ) , Egr-1 ( Santa Cruz Biotechnology ) , and β-Actin ( Santa Cruz Biotechnology ) . ECs were seeded at a density of 2–2 . 5×104 cells/cm2 on fibronectin-coated dishes . After 2 h , cells were harvested with a buffer containing 0 . 5% NP-40 , 50 mM Tris-Cl ( pH 8 . 0 ) , 150 mM NaCl , 10% glycerol , 1 . 5 mM MgCl2 , 50 mM β-glyceraldehyde , 50 mM NaF , 0 . 1 mM Na3VO4 , 1 mM DTT , and a protein inhibitor cocktail . If necessary , 0 . 5 mM ZnCl2 and/or 1 mM EDTA was added . The lysates were immunoprecipitated with IgG or anti-Egr-1 antibody ( Santa Cruz Technology ) at 4°C overnight . On the next day , protein A/G slurry beads ( 30 μl ) were added for 2 h . The beads were spun and re-suspended with sample buffers and run on SDS-PAGE along with the 5% input sample and transferred to polyvinylidene fluoride membranes . Proteins were detected with the anti-Snail ( Millipore clone 10H4 . 1 ) or anti-Egr-1 antibody . For VEGFA experiments , ECs were exposed to 30 ng/mL VEGFA for the indicated time points . EC lysates were immunoprecipitated with anti-Snail antibody ( Cell Signaling Technology , Inc . ) and then probed with the anti-Egr-1 antibody ( Santa Cruz Biotechnology ) . ECs were plated on a gelatin-coated dish and transfected with 500 ng VEGFR3 promoter-reporter constructs and 50 ng pRL-TK using Lipofectamine LTX-plus reagents ( Invitrogen ) . In some experiments , ECs were transfected with siRNAs and then re-transfected with the VEGFR3 promoter-reporter and pRL-TK plasmid after 4 h . On the next day , ECs were cultured in fresh EBM-2 containing 1% FBS for 4 h , reseeded at a density of 2–2 . 5×104 cells/cm2 , and cultured on ECM-coated dishes for another 8–16 h . The cells were lysed using passive lysis buffer ( Promega ) , and luciferase activity was determined using a dual luciferase assay system ( Promega ) . A human VEGFR3 reporter ( HPRM21111-PG02 ) containing the 1 . 3-Kb promoter region of VEGFR3 was purchased from GeneCorpoeia , Inc . The VEGFR3 promoter was subcloned into the pGL3-basic luciferase reporter plasmid ( Promega ) . VEGFR3 reporters containing mutations in the region of the putative E-box region was generated using the QuickChange II Site-Directed Mutagenesis kit ( Agilent Technology ) , according to the manufacturer’s instructions . The ChIP assay was performed using the ChIP kit ( Millipore ) , according to the manufacturer’s instructions . SiSnail-transfected HUVECs were exposed to formaldehyde ( 1% final concentration ) to cross-link their genomic DNA and protein . The cells were harvested , lysed , and sonicated to generate 0 . 3–1 . 0-kb DNA fragments . After centrifugation , the cleared supernatant was incubated with the anti-Snail antibody ( Abcam , ab85931 ) or Immunoglobulin G for immunoprecipitation . The primers used for amplification were as follows: 5’-GGAAAGAAAGGACGGAAAAGAGC-3’ and 5’-GCTGCGCGTGGGTCCGA-3’; 5’-GCTCCCCTTTGCCCACCAG-3’ and 5’-CCACAGTCGCAGGCACAGC-3’ . PCR amplification was carried out under the condition of 95°C ( 60 sec ) , 60°C ( 30 sec ) , and 72°C ( 30 sec ) for 40–45 cycles . Amplified DNA was separated on a 1 . 5% agarose gel and visualized with ethidium bromide . HUVECs were loaded onto Matrigel on a 24-well culture dish at a density of 1 . 5×105 cells/well in EGM-2 supplemented with 10% FBS . Cells were photographed at the indicated time points . Cells were also transfected with siCon ( 40–80 μM ) or siSnail ( 40–80 μM ) . For quantitative RT-PCR or western blot analysis , cells were recovered from Matrigel using the Cell Recovery solution ( BD Bioscience ) , and RNA or protein was isolated . Following transfections , HUVECs were mixed with Cytodex microcarrier beads at a ratio of 1×106 cells:2500 beads . Coating was performed for 4 h in fluorescence-activated cell sorting tubes , which were shaken by pipetting every 20 min . After 24 h , the coated beads were dissolved in a solution of 2 mg/mL fibrinogen and 0 . 15 units/mL aprotinin in EGM-2 . The solution was added to 0 . 625 units/mL thrombin in each well of a 24-well plate . After forming clots , 2×104 WI-38 fibroblasts or fibroblast-conditioned media were loaded into each well . The medium was replaced every 2 days , and sprouting was analyzed after 7–10 days . The mean number of sprouts per bead was determined by counting the number of sprouts that originated from the cells that lined the surface of the bead , and the mean number of branch points per bead was determined by counting the number of sprout bifurcations per bead . For single EC sprouting assays , ECs were transfected with siSnail , flag-Snail or flag-Snail ( 6SA ) . The cells were resuspended in a solution of 2 mg/mL fibrinogen , 0 . 15 units/mL aprotinin , and 0 . 625 units/mL thrombin , and then rapidly loaded on top of a precoated fibrin layer . When the fibrin gel formed clots , a solution containing a 1:1 mixture of fresh EGM-2 and WI-38 fibroblasts-conditioned medium was loaded into each well and replaced every 2 days . After 7–10 days , cells were stained with 4 μg/mL calcein AM and imaged by fluorescence microscopy . The cumulative sprout length was quantified in a minimum of 6 fields ( 720 μm × 530 μm ) , and the total length was normalized to 1000 μm . The eyeballs of C57/BL6 mice were enucleated and fixed in 4% paraformaldehyde ( PFA ) for 1 h or 1% PFA for 30 min to stain retinal vessels or corneas , respectively . In whole flat-mount assays for Snail staining , retinas were dissected , post-fixed in methanol , and permeabilized with 0 . 5% saponin and 0 . 25% BSA in PBS overnight . The retinas were incubated with anti-Snail antibody ( Millipore , Clone 10H4 . 1 ) overnight . After washing with 0 . 25% saponin in PBS , retinas were incubated with Alexa Fluor 488-goat anti-mouse IgM and 594-conjugated Isolectin GS-iB4 solution at 4°C overnight . For VEGFR3 or CD29 stainings , a detergent was minimally used in the process of blocking , washing , and incubation with anti-VEGFR3 ( Abcam , ab51874; R&D , AF743 ) antibodies . The retinas were flat-mounted on slides using fluorescent mounting medium . Images were captured with Carl Zeiss confocal microscopes ( LSM 510 META or LSM 700 ) . For immunochemical assays , mouse eyeballs were fixed in 4% PFA overnight , incubated in 15% sucrose , and transferred to 30% sucrose at 4°C until they sank . The eyeballs were transferred to Optimal Cutting Temperature compound-embedding medium , sectioned ( 8–12 μm ) , and then stored at -70°C . All mice were maintained in a laminar air flow cabinet under specific pathogen-free conditions . All facilities were approved by the Association of Assessment and Accreditation of Laboratory Animal Care , and all animal experiments were conducted under institutional guidelines that were established for the Animal Core Facility at Yonsei University College of Medicine ( Korea , Seoul ) . The pGFP-C-shLenti mouse Snail clone ( A–D ) sets were purchased from Origene Technologies , Inc . Of these , pGFP-C-shLenti mouse Snail clones A and B ( referred as to shSnail #1 and #2 in this study ) were selected . The lentiviruses were collected and concentrated after checking for GFP-positive staining in 293T cells . C57/BL6 mice ( 7 weeks old ) were injected subcutaneously with 0 . 6 mL Matrigel containing GFP-shLenti Control or GFP-shLenti Snail . After 6 days , the skin of each mouse was pulled back to expose the Matrigel plug , which remained intact . To identify infiltrating mouse ECs , immunohistochemistry was performed with the anti-CD31 antibody ( BD Biosciences ) . For in vivo siRNA injections , mouse siSnail sequences were selected among four sets of mouse siSnail ( Dharmacon; ON-TARGETplus Mouse SnailLU-062765 ) and manufactured as in vivo siSTABLE mSnail ( siSnail ) by Dharmacon ( 5’-CAAACCCACUCGGAUGUGAUU-3’ ) . C57/BL6 mice were injected intraperitoneally with 4 mg/kg siSnail or scrambled siRNA ( siCon ) from P6–P7 or from P7–P10 . Mice were sacrificed at P8 or P11 , and enucleated eyes were processed for whole flat-mount staining . Each experiment was performed with three pups per group and repeated four times . For shLenti Snail virus injections , shCon or shSnail#2 was intraperitoneally injected at P6 and P7 , and the mice were sacrificed at P8 . Enucleated eyes were processed for whole flat-mount staining . Each experiment was performed with three or four littermates per group and repeated three times . For the pharmacological inhibition of VEGFR3 kinase activity and c-Src activity in vivo , 10 mg/kg MAZ51 ( Sigma ) or 10 mg/kg PP2 ( Sigma ) were intraperitoneally injected . To analyze the superficial plexus , MAZ51 was injected at P4 and P5 , and mice were sacrificed at P6 . To analyze the deep plexus , MAZ51 or PP2 was injected from P7 to P9 and P10 , and then mice were sacrificed at P10 or P11 . The retinal vessel area , radial length , branching points , and vertical vessels were measured . All pups were weighed before experiments . Littermates with identical weights for each experiment were used . Each experiment was repeated three times . The vessel length and area were determined by using Multi Gauge Fuji film ( Tokyo , Japan ) . Data were presented as mean ± standard deviation or mean ± standard error . Statistical comparisons between groups were performed using one-way analysis of variance , followed by the Student’s T test . All experiments were performed at least three times , and representative results were shown . | Endothelial cells have the intrinsic capacity to shuffle between tip , stalk , and phalanx cells in angiogenic processes . These transitions require the induction or repression of transcripts that are specific for their phenotypes , along with morphological changes . To gain insight into spatiotemporal induction during vascular branching morphogenesis , we used Affymetrix oligonucleotide arrays to screen for Snail . Then , we used stable , small-interfering RNA or the lentivirus-short hairpin RNA system to examine the angiogenic roles of endothelial Snail during retinal capillary morphogenesis . Knockdown of Snail in the developing retinal vasculature impaired deep capillary formation and attenuated vascular endothelial growth factor receptor 3 expression , indicating a functional link between Snail and vascular endothelial growth factor receptor 3 . Moreover , we showed vascular endothelial growth factor receptor 3 as a transcriptional target of Snail in vitro . In the retinal vasculature , the deep capillary plexus is a unique vessel with only capillary . The deep capillary plays a critical role in retinal development , neuronal survival , and pathological conditions , including ischemic diseases . Our findings provide molecular insights into the role of the Snail-vascular endothelial growth factor receptor 3 axis in capillary formation under pathophysiological conditions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Endothelial Snail Regulates Capillary Branching Morphogenesis via Vascular Endothelial Growth Factor Receptor 3 Expression |
Old English Sheepdogs and Gordon Setters suffer from a juvenile onset , autosomal recessive form of canine hereditary ataxia primarily affecting the Purkinje neuron of the cerebellar cortex . The clinical and histological characteristics are analogous to hereditary ataxias in humans . Linkage and genome-wide association studies on a cohort of related Old English Sheepdogs identified a region on CFA4 strongly associated with the disease phenotype . Targeted sequence capture and next generation sequencing of the region identified an A to C single nucleotide polymorphism ( SNP ) located at position 113 in exon 1 of an autophagy gene , RAB24 , that segregated with the phenotype . Genotyping of six additional breeds of dogs affected with hereditary ataxia identified the same polymorphism in affected Gordon Setters that segregated perfectly with phenotype . The other breeds tested did not have the polymorphism . Genome-wide SNP genotyping of Gordon Setters identified a 1 . 9 MB region with an identical haplotype to affected Old English Sheepdogs . Histopathology , immunohistochemistry and ultrastructural evaluation of the brains of affected dogs from both breeds identified dramatic Purkinje neuron loss with axonal spheroids , accumulation of autophagosomes , ubiquitin positive inclusions and a diffuse increase in cytoplasmic neuronal ubiquitin staining . These findings recapitulate the changes reported in mice with induced neuron-specific autophagy defects . Taken together , our results suggest that a defect in RAB24 , a gene associated with autophagy , is highly associated with and may contribute to canine hereditary ataxia in Old English Sheepdogs and Gordon Setters . This finding suggests that detailed investigation of autophagy pathways should be undertaken in human hereditary ataxia .
The hereditary ataxias are an important , heterogeneous group of movement disorders unified by the presence of degeneration of the cerebellar cortex , and in particular of the Purkinje neurons [1]–[5] . They may be inherited as autosomal dominant ( also known as the spinocerebellar ataxias or SCAs ) , recessive , X linked and mitochondrial traits with the autosomal dominant SCAs representing the most common group of ataxias in humans [3] , [4] . Associated mutations range from possible toxic gain of function mechanisms in polyglutamine diseases [6] , to ion chanelopathies such as the proposed calcium channel dysfunction in SCA6 [7] , to abnormalities in growth factors such as FGF14 in SCA27 [8] , and to structural proteins such as β-III spectrin in SCA5 [9] . While over 50 different genetic loci have been shown to be associated in humans , in 20–40% of patients , the genetic cause remains elusive [6] . Purebred dogs suffer from comparable neurodegenerative diseases affecting the cerebellar cortex referred to as cerebellar cortical degeneration , cerebellar abiotrophy or canine cerebellar or hereditary ataxia . Currently , hereditary cerebellar degenerative disorders have been described in over 20 breeds of dog [10] , [11] with many more sporadic cases reported . In most dog breeds , the disorder causes slowly progressive degeneration of the cerebellar cortex , with dramatic Purkinje neuron loss resulting in a progressive gait dysfunction [10] , [11] . Forms that primarily target the granular layer or produce ataxia without cell loss are also reported [12]–[18] . The canine form of the disease targets the cerebellum primarily and is most commonly inherited as a fully penetrant autosomal recessive trait [11] , [13] , [19] , [20] . Onset may be neonatal [13] , juvenile [21] or even older [22] . The genetic cause of three of these canine disorders has been described and include a mutation in SEL1L , a protein important in endoplasmic reticulum associated protein degradation in the Finnish hound [23] , SPTBN2 gene encoding β-III spectrin in the beagle [24] , and GRM1 , the metabotropic glutamate 1 receptor in the Coton de Tulear [25] . Autosomal recessive cerebellar degenerative disorders have been described in the Old English Sheepdog [20] and the Gordon Setter [26] , [27] . The clinical phenotype is identical in both breeds with an onset of cerebellar ataxia first noted in juvenile to young adult dogs aged from six months to four years . Dogs develop pronounced hypermetria , a truncal sway and intention tremor , and signs progress to cause severe gait disturbances . Cerebellar atrophy can be identified by magnetic resonance imaging ( MRI ) [28] and histopathological findings include loss of Purkinje cell , granule cell and molecular layer neurons causing atrophy of the cerebellar cortex . In more detailed work on Gordon Setters , profound changes in cerebellar neurotransmitter levels and synapses have been described [29] , along with the development of Purkinje neuron axonal spheroids [30] . The aim of this project was to investigate the genetic cause of hereditary ataxia in Old English Sheepdogs . To that effect , we genotyped an extended family of Old English Sheepdogs that suffer from canine hereditary ataxia , and identified a chromosomal locus associated with the trait using linkage and genome-wide association analyses . Sequence capture of the associated region was performed to facilitate fine mapping and this identified a mutation that segregated with the condition . Dogs from other breeds affected with cerebellar degeneration were also screened for the mutation and the identical mutation was found in Gordon Setters with canine hereditary ataxia .
Targeted sequence capture was performed to sequence the entire genomic region from 34 MB to 46 MB on CFA4 , thereby covering the whole homozygous region . Six dogs were sequenced , three cases , and three controls , including two obligate heterozygotes and one unrelated , phenotypically normal dog . A Roche NimbleGen array ( Madison , WI ) was designed to provide coverage of 95 . 7% of the region using unique probes . The design was reviewed to ensure that all predicted and known gene exons had adequate coverage . Following next-generation sequencing on Illumina's Hi-Seq 2000 machine ( Illumina; San Diego , CA ) , approximately 96% of the 12 , 000 , 000 targeted bases had at least 2× coverage with approximately 72% having at least 30× coverage . Quantitative PCR confirmed that the region of interest was enriched appropriately . Data processing using the Genome Analysis Toolkit ( GATK ) [35] revealed 40 , 711 total variants ( 32 , 475 SNPs and 8 , 236 indels ) . After filtering the total variants for known SNPs and those present in all six sequenced dogs , 28 , 061 variants remained of which only 288 were present in coding regions . Screening of these 288 variants revealed nine SNPs that segregated in an autosomal recessive inheritance pattern , six of which resulted in an amino acid change and were considered potential causative mutations for the disease trait . The six SNPs were located in the genes RGR , RAB24 , NSD1 , GPRIN1 , and CDHR2 ( Table S1 ) . The six SNPs identified as mutations of interest were verified by Sanger sequencing in the six dogs initially sequenced . Additional cases and controls were genotyped on the six SNPs ( Table S2 ) . SNPs in RGR , GPRIN1 , and CDHR2 did not segregate with phenotype in this larger population and were eliminated from further analysis . The two SNPs present in RAB24 and NSD1 showed segregation in the cases and controls consistent with an autosomal recessive mode of inheritance ( Table S2 ) . When these two SNPs were tested for association with the trait in the same 53 dogs as the GWAS , the p-values were both highly significant at pbonferroni = 5 . 7×10−9 . When additional dog breeds were tested ( Table S3 ) , the NSD1 SNP was present in the heterozygous state in three neurologically normal Labrador Retrievers ( from a group of 7 dogs ) and two neurologically normal Standard Poodles ( from a group of 7 dogs ) . Cerebellar degeneration has not been reported in Standard Poodles but has been reported in Labrador Retrievers [36] , [37] . However , an affected Labrador retriever was genotyped for this SNP and it was not present in the affected dog ( see results below ) . The presence of this SNP in five of 14 dogs from two breeds implies a relatively high prevalence of this polymorphism within these breeds . The lack of reports of hereditary ataxia in one of these breeds and the absence of this SNP in an affected dog from the other breed make it unlikely that it is a pathogenic mutation and focused our attention on RAB24 . The RAB24 SNP polymorphism was an A to C transversion located at position 113 in the first of its eight exons ( Figure 2e ) and it produced an amino acid change from glutamine ( Q ) to proline ( P ) at position 38 . In order to investigate the hypothesis that the Rab24 p . Q38P change was the causative mutation for hereditary ataxia in Old English Sheepdogs , a total of 376 Old English Sheepdogs were genotyped , all of which came from case blood lines , including the 14 cases used in the GWAS and an additional six cases . Of these dogs , all 20 confirmed cases were homozygous for the alternate allele . 109 controls were heterozygotes , four controls were homozygous for the alternate allele while the remainder were homozygous for the wild type allele . This cohort of dogs had been sampled approximately 20 years previously and at the time , their owners provided the dogs' phenotype . All dogs were old enough to be expected to exhibit neurological signs if affected . Of the four dogs that were reported as normal but exhibited the case genotype , two were littermates of affected dogs , with one confirmed affected parent and one parent confirmed as a carrier . The remaining two dogs were descended from parents who were carriers . Physical examinations were not performed on these dogs by a veterinarian and so their phenotype could not be confirmed . The effect of the Rab24 p . Q38P change on protein function was investigated using two different homology-based on-line tools , Polyphen-2 [38] and SIFT [39] . Both predicted that the change would probably damage protein function , with a score of 0 . 989 for Polyphen-2 ( sensitivity: 0 . 72 and specificity: 0 . 97 ) and 0 . 01 for SIFT . The Rab24 protein is a GTPase from the large Rab protein family important in vesicle trafficking , endocytosis and exocytosis [40] . Several Rab proteins , including Rab24 , have now been shown to play a vital role in autophagy [41] , the process by which proteins and organelles are moved to lysosomes for degradation . Domains known to be important to Rab protein function include the nucleotide binding and Mg2+sites necessary for GTPase activity; two switch regions play a vital role in facilitating GTPase activity . The p . Q38P change lies in a highly conserved amino acid ( Figure 3a ) located within the putative switch I region , suggestive of an effect on GTP binding ( Figure 3b ) . An additional 254 DNA samples from Old English Sheepdogs were obtained from the Orthopedic Foundation for Animals Inc . ( OFA ) CHIC DNA repository . Detailed phenotypic information was not available on these dogs although none were reported to have canine hereditary ataxia on health questionnaires completed by owners at the time of DNA submission . This cohort of dogs came from approximately 70 different breeding kennels . These samples were genotyped in order to determine the prevalence of the mutation in a wider population of dogs . Twenty-eight of these dogs were heterozygous while the remainder was homozygous for the reference allele , giving an overall alternate allele prevalence in the original pedigrees studied and the random selection of dogs ( a total of 630 dogs ) of 14 . 3% . To test for the potential of the mutation to discriminate cases and controls , receiver operating characteristic ( ROC ) curve analysis was performed in the total population of Old English Sheepdogs genotyped . The area under the curve was 99 . 5% ( S . E . = 0 . 0023 , 95% confidence interval = 0 . 9909–0 . 9999 ) , which was highly statistically significant ( p<0 . 001 ) . Hereditary ataxia is not unusual in dogs and has been reported in numerous breeds . In order to determine whether the p . Q38P mutation was found in affected dogs from other breeds , DNA samples from a Dalmatian , a beagle ( both confirmed by neurological evaluation and the presence of cerebellar atrophy on MRI of the brain ) , 2 Rhodesian Ridgebacks ( necropsy confirmed ) , 2 Gordon Setters ( one of which was necropsy confirmed and the other confirmed by neurological evaluation and the presence of cerebellar atrophy on MRI of the brain ) , 2 Scottish Terriers ( one MRI and one necropsy confirmed ) and a Labrador retriever ( diagnosed by neurological evaluation and clinical history only ) were genotyped for the RAB24 and the NSD1 SNPs . In addition , all exons of the RAB24 gene were sequenced in these affected dogs . The alternate allele for RAB24 or NSD1 was not present in any of the breeds except the affected Gordon Setters . Two heterozygous synonymous SNPs were identified in exon 3 of RAB24 in the beagle which were also present in the Old English Sheepdog sequencing data . Heterozygous non-exonic SNPs were also identified in the Dalmatian . None of these were consistent with the mode of inheritance or pathologic in nature . A total of 18 affected Gordon Setters were then genotyped , all of which were homozygous for the RAB24 mutation . DNA from ten of these cases was obtained from archived material from a research dog colony [19] . An additional 82 normal Gordon Setters were genotyped from Scandinavia and the US and 24 dogs were heterozygotes and 58 were homozygous for the wild type allele . None were homozygous for the alternate allele . Excluding the 10 cases that were archival material and therefore not part of the general breeding population , there was an alternate allele frequency of 22 . 2% . The phenotype of cerebellar degeneration in Gordon Setters has been described in detail [19] , [26] , [27] and is identical to the description of Old English Sheepdogs in terms of age of onset and progression of signs . To compare the genetic background of Gordon Setters and Old English Sheepdogs further , a cohort of seven affected and 26 control Gordon Setters were genotyped on the Illumina Infinium Canine HDBeadchip and the haplotypes on CFA4 were compared to those of the Old English Sheepdogs . This revealed an identical region of homozygosity extending from 39 , 245 , 536 bp to 41 , 172 , 873 bp in affected dogs from both breeds , including the NSD1 mutation also identified in the region ( Figure S1 ) , suggesting the mutation dates back to a common European ancestral dog population , from which these two separate breeds were founded . This region contains 29 genes inferred from human sequence data ( Table S4 ) . When targeted sequencing was performed in the Old English Sheepdogs , mean sequencing coverage of this region of shared homozygosity was 47× , making it unlikely that additional variants were missed . These findings are supportive of the hypothesis that the RAB24 mutation is the causative mutation of hereditary ataxia in these two breeds of dog . In order to determine whether this mutation was present in other breeds of dog representing diverse ancestral lineage , DNA samples were collected from at least eight individuals from breeds in each of 10 breed clusters reported as related ancestrally [42] , [43] . A total of 194 individuals from 43 different breeds were genotyped ( Table S3 ) . All additional breeds were homozygous for the wild type allele on the RAB24 SNP . Expression levels of RAB24 in the cerebellum were compared between case ( n = 4 ) and control ( n = 2 ) dogs by qRT-PCR . There was no significant difference in level of RAB24 expression between cases and controls ( p-value = 0 . 71 ) . The A>C change in the RAB24 transcript was present in all affected dogs and absent in the control dogs . The predicted exon/intron boundaries were also confirmed . Samples of brain from eight affected Old English Sheepdogs ranging in age from 2 . 5 to 13 years , and one affected 2 . 5-year-old Gordon Setter , and from 10 neurologically normal , age matched dogs were fixed in 10% neutral buffered formalin and embedded in paraffin for histological evaluation . Six of the eight Old English Sheepdogs were examined at the University of Pennsylvania in 1999 and the paraffin embedded blocks were retrieved from the archives . In the remaining two Old English Sheepdogs , the brains were harvested by a local veterinarian following euthanasia , placed in formalin and shipped to NCSU for paraffin embedding . The Gordon Setter was euthanized at NCSU and the brain was removed immediately and placed in formalin within 30 minutes of euthanasia . Sections were stained with hematoxylin and eosin , Periodic Acid Schiff ( PAS , to evaluate glycogen storage products ) , Bielschowsky silver stain ( to evaluate axonal processes ) , and luxol fast blue ( to evaluate myelination ) . Immunohistochemical staining was performed for GFAP , ubiquitin , and Rab24 . Samples of cerebellum from the affected Gordon Setter were fixed in McDowell's and Trump's 4F:1G fixative and processed for electron microscopy . Pathological changes were largely restricted to the cerebellum , with the majority of changes affecting the cerebellar cortex . There was dramatic loss of Purkinje neurons , with atrophy of the molecular and granular layers ( Figure 4a ) . Vacuoles were visible in the white matter throughout the cerebellum and axonal spheroids were identified in the granular layer ( Figure 4a ) . In addition , there were vacuoles in the cerebellar peduncles , the vestibular and cochlear nuclei and the nucleus of the dorsal trapezoid body . There were minimal changes in the cerebellar nuclei . The Bielschowsky stain highlighted the processes of basket cells and the lack of Purkinje neurons ( Figure 4b ) . There was no evidence of glycogen accumulation on PAS stained sections and the luxol fast blue staining confirmed the presence of myelin around the axonal spheroids identified in the granular layer . Immunostaining for GFAP showed increased astrocytic expression and highlighted mild to moderate astrocytosis . Ubiquitin immunostaining of controls revealed granular accumulations of ubiquitin positive material in the white matter , the density of which increased with age ( Figure 5a ) . In older control dogs , some positive staining was also seen in the granular layer and very fine positive granules were found at the junction of the molecular and granular layers around Purkinje neurons . In cases , the ubiquitin positive staining within the white matter was comparable to the age matched controls . However , there were also multiple , large ovoid bodies containing ubiquitin positive material staining in a punctate pattern , lying within the granular layer , at the junction of the granular and molecular layers , and in the cerebellar white matter ( Figure 5b and c ) . In some instances , these ubiquitin positive bodies appeared to be emanating from a Purkinje neuron and to co-localize with axonal spheroids seen on the hematoxylin and eosin stained sections ( Figure 5b and d ) . Ubiquitin positive bodies were limited to the cerebellum of cases , with no inclusions seen elsewhere in the brain . In addition , some Purkinje neurons , molecular layer neurons and Golgi neurons within the granular layer had strong diffuse cytoplasmic ubiquitin staining ( Figure 5b ) . The axonal spheroids were examined on electron microscopy and were packed with organelles such as mitochondria and the Golgi apparatus , and vesicular structures , many of which had the classic double membrane of autophagosomes ( Figure 6 ) . There was no significant difference in the intensity or pattern of Rab24 immunostaining in the cerebellum of cases and controls . There was positive staining within the cytoplasm of Purkinje neurons; the staining was granular and located in an eccentric perinuclear position as described in cell culture studies [44]–[46] ( Figure 7a ) . The terminal dendrites of the basket cells on Purkinje neurons stained intensely ( Figure 7a ) . In addition , neurons in the molecular layer , and the granular layer stained positively , as did the neurons of the deep cerebellar nuclei ( Figure 7b ) and oligodendrocytes ( Figure 7c ) throughout the white matter of the cerebellum . Axonal spheroids were negative for Rab24 .
The results of our work implicate a founder mutation in the GTPase Rab24 as the cause of autosomal recessive hereditary ataxia in both the Old English Sheepdog and the Gordon Setter although dysfunction of this protein has not been established . Genome wide linkage and association studies in a cohort of related Old English Sheepdogs identified a strong association between the disease phenotype and an approximately 12 MB region of homozygosity on CFA 4 in affected dogs . High throughput sequencing identified an A>C variant that predicted a Q>P change in the Rab24 protein . Evaluation of Gordon Setters with the same clinical phenotype revealed the same mutation in affected dogs and a shared block of homozygosity extending over 1 . 9 MB . While LD extends over long distances within breeds , across breeds , LD rapidly drops off . This is a characteristic resulting from two different bottlenecks in the history of domesticated dogs , the first dating back to their separation from wolves over 10 , 000 years ago , and the second more recent bottleneck occurring as modern breeds developed from a limited number of founder dogs only a few hundred years ago [47] , [48] . These breeds of dog are placed in different ancestral clusters with Old English Sheepdogs classified as herding dogs and Setters clustering with working dogs . This long shared haplotype between the two breeds is unusual , and the implications of our findings are that this mutation dates back to a time before these two breeds of dog were developed , that the two breeds have a shared ancestry , and that this shared founder mutation is the cause of hereditary ataxia in these breeds . This has been found with other mutated canine genes , for example , the mutation in prcd-PRA [49] . Rab24 is an atypical member of the large Rab family of small GTPases [45] . These enzymes play a vital role in membranous transport within the cell allowing movement of cell organelles , and endocytosis and exocytosis ( reviewed in [40] , [50] ) . They work in concert with the SNARE proteins to bridge membranes and drive fusion . The mechanism by which they achieve this has been well characterized for certain members of the family , and depends on their GTP state , and their ability to prenylate and thus cycle on and off membranes . Rab24 is unlike the other members of the family , having poor GTPase activity , and reduced prenylation and its mechanism of action remains elusive [45] . It has been shown to localize to the cis Golgi and ER and to co-localize with autophagosomal markers such as LC3 , and it is proposed to play a role in the late stages of autophagy related to the fusion of the autophagosome and lysosome [46] . Proteomic evaluation of autophagy networks also show interactions between Rab24 and other vesicle trafficking autophagic proteins [51] . While expressed at low levels in many tissues , it is expressed most highly in the brain [44] and there is evidence that it is upregulated during neuronal differentiation [45] and as a response to nerve injury [52] . There has been speculation that Rab24 dysfunction might play a role in neurodegenerative disease because mutations placed in a region known as the G2 domain that reduce the affinity of Rab proteins for GTP produce nuclear inclusions that disrupt the nuclear membrane , and stain positive for ubiquitin and Hsp70 , typical of protein aggregates in polyglutamine diseases [53] . However , more recently , Rab24 has been shown to play a role in cell division [54] . The mutation described here lies in the putative switch 1 region , important for GTP binding [55] . In support of this region being functionally important , in another Rab associated disease , Griscelli Syndrome , a mutation in the switch 1 region of Rab27A results in a profound phenotype due to failure of the Rab protein to interact with its target melanophilin [56] . Mutations in RAB7 have been associated with Charcot-Marie-Tooth type 2B neuropathy , a progressive neurodegenerative condition of the peripheral nervous system . One of the mutations described lies immediately adjacent to a highly conserved GTP binding domain [57] . Rab7 is involved in transport between endosomes and lysosomes , demonstrating the importance of subcellular trafficking in neuronal health . There are two main systems that allow for turn-over of cellular organelles and proteins , the ubiquitin proteasome system ( UPS ) and autophagy . The ubiquitin proteasome system ( UPS ) is well described and is a system by which short-lived regulatory and misfolded proteins undergo non-lysosomal degradation [58] , [59] . The UPS system has been shown to be vital to normal development of the central nervous system , to synaptic plasticity and to cellular homeostasis . Reflective of these important roles , many neurodegenerative diseases have been linked to abnormalities in the UPS system [58] , [59] . More recently , attention has shifted to the role that autophagy might play in neurodegenerative disease . Autophagy is the process by which more long-lived proteins and organelles are incorporated into autophagosomes for delivery to vacuoles or lysosomes for degradation . There is abundant cross communication between the UPS and autophagy systems , with specific ubiquitinated proteins being moved to autophagosomes for disposal . As a result of this convergence of disposal pathways , dysfunction of autophagy can result in ubiquitin accumulation [60] , [61] and accumulation of autophagosomes and ubiquitin positive inclusions , both evident in our affected dogs , are considered to be hallmarks of neurodegenerative diseases ( reviewed in [62] ) . However , these findings are quite non-specific , being present in most neurodegenerative diseases . Autophagy is believed to be vital for cell survival in several different ways . Constitutive autophagy plays an important role as a basal source of energy in cells with high metabolic needs , such as the Purkinje neuron , in the global turnover of cellular organelles and in the clearance of potentially toxic protein aggregates [62]–[65] . While there are a variety of hypotheses for the role of autophagy in neurodegenerative diseases [66]–[68] , the most compelling evidence that a primary defect in autophagy can induce neurodegenerative disease was generated by two studies in which neuron specific dysfunction of two proteins involved in the formation of autophagasomes , Atg5 and Atg7 , was induced in mice [65] , [69] . In both studies , mice developed progressive motor incoordination and balance deficits accompanied by progressive neurodegeneration . There was dramatic loss of Purkinje neurons accompanied by axonal swellings and development of ubiquitin positive inclusions and diffuse intraneuronal ubiquitin accumulation . The clinical and neurodegenerative phenotypes of these mice are similar to the dogs in our study in which we saw dramatic Purkinje neuron loss , axonal spheroids containing autophagosomes and large ubiquitin positive inclusions in addition to more diffuse intracellular ubiquitin accumulation . However , we were unable to demonstrate an alteration in the level of expression of the RAB24 gene or Rab24 protein by qPCR or immunohistochemically . It can be argued that Purkinje neurons are a natural target for many different pathological processes due to their high metabolic activity , requiring higher levels of protein and organelle turn over . Indeed , in hereditary ataxia affecting the Finnish Hound , a defect in SEL1L , a gene important in the endoplasmic reticulum associated degradation ( ERAD ) process that targets misfolded proteins to the UPS , causes early onset rapidly progressive Purkinje neuron loss [23] . Taken together , these two naturally occurring canine models of hereditary ataxia suggest that protein and organelle turnover is of vital importance to the maintenance of neuronal , and in particular , Purkinje neuron health . Additional in vitro work needs to be completed to demonstrate Rab24 dysfunction as a result of this mutation . We have described a mutation that results in an amino acid change in Rab24 , a protein as yet poorly understood , but believed to play a role in the late stages of autophagy . Our findings of Purkinje neuron degeneration in this naturally occurring canine neurodegenerative condition support the evidence that Rab24 is necessary for autophagy , and our clinical and histopathological findings are strongly reminiscent of the mouse studies demonstrating that defects in autophagy produce neurodegeneration targeting the Purkinje neuron . This finding may provide a novel tool for the investigation of the mechanisms of autophagy and defects in Rab proteins should be considered when investigating neurodegenerative diseases .
Old English Sheepdogs with canine hereditary ataxia were identified by referral from veterinarians , breeders and owners . DNA samples and pedigrees from these U . S . and Canadian dogs and their unaffected family members were collected . Affected status was determined by compatible clinical signs of cerebellar disease including ataxia , typical age of onset , physical exam , and neurological exam . Cerebrospinal fluid analysis was undertaken in many dogs , as well as a necropsy , when possible . All protocols were performed with approval from North Carolina State University's Institutional Animal Care and Use Committee . All normal dogs reached the age of four years of age with no evidence of clinical signs of ataxia . Dogs that could not definitively be classified as “affected” or “normal” based on collected information were classified as of “undetermined” status . DNA was extracted from whole blood using QIAamp DNA Blood Midi Kit ( Qiagen; Valencia , CA ) , frozen tissues; DNeasy Blood and Tissue Kit ( Qiagen; Valencia , CA ) , buccal swabs; QIAamp DNA Mini Kit ( Qiagen; Valencia , CA ) , and from saliva using Oragene Animal kit ( DNA Genotek; Kanata , Ontario ) . DNA concentrations were measured using a ND-1000 UV-Vis NanoDrop spectrophotometer ( Thermo Scientific , Wilmington , DE ) . Related dogs were genotyped using a genome-wide panel of 311 canine microsatellite markers ( representing an average of 9 MB resolution ) , organized into multiplex PCR groups [31] . Four fluorescent labels ( FAM , VIC , NED and PET ) were incorporated into the PCR primers to allow multiplexing . PCR fragments were analyzed on an ABI-3700 automated Genetic Analyzer ( Applied Biosystems ) , and genotypes were assigned using GeneMapper v3 . 7 software ( Applied Biosystems ) . Homozygous ( uninformative ) markers were excluded from further analysis . Genotyping of SNPs was performed using Illumina's Canine SNP20 ( 22 , 362 SNPs ) and CanineHD ( 170 , 362 SNPs ) genotyping beadchips ( Illumina; San Diego , CA ) . Nine cases and 13 control dogs were genotyped on the SNP20 chip and five cases and 28 controls on the CanineHD chip . One control dog was genotyped on both chips , enabling the comparison of genotype calls between them . The assays were performed at the National Institutes of Health's Laboratory of Neurogenetics ( Bethesda , MD ) according to the manufacturer's instructions . The amplified DNA products were imaged using a BeadArray Reader ( Illumina; San Diego , CA ) and analyzed using Illumina's Bead Studio and Genome Studio software ( Illumina; San Diego , CA ) . Data was pruned such that individuals with less than a 95% call rate and those having a significant number of Mendelian errors were removed from further analysis . SNPs having a minor allele frequency of less than 1% , missing genotype calls greater than 10% and showing inconsistent calls between the two chips were also removed from further analysis . The pruned dataset was then used to perform a case-control and family-based association analysis . The PLINK toolset v1 . 07 ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) was used to perform data pruning and the case-control allelic and genotypic association tests using a Bonferroni correction for multiple comparisons [33] . As most of the Old English Sheepdogs genotyped belonged to a large pedigree , a family-based association test was also performed using the Family Based Association Test ( FBAT ) toolkit ( v2 . 0 . 3 ) ( http://www . hsph . harvard . edu/~fbat/fbat . htm ) [34] . The FBAT toolkit improves upon the traditional transmission disequilibrium test ( TDT ) [70] by handling factors such as missing parents , additional family members , different genetic models and phenotypes , as well as controlling for false positive associations due to population structure [34] . A Bonferroni correction for multiple comparisons was implemented using JMP Genomics ( SAS; Cary , NC ) with adjusted P-values less than 0 . 05 considered significant . Receiver operator characteristic curve analysis was performed using Stata v10 ( www . stata . com ) . A 12 MB genomic region of interest was targeted using a custom array designed and manufactured by Roche NimbleGen ( Madison , WI ) . 19 , 720 tiled probes approximately 60–90 bp in length covered approximately 96% of the targeted bases between 34 , 000 , 000–46 , 000 , 000 bp on CFA4 . Unique probes were determined using the Sequence Search and Alignment by Hashing Algorithm ( SSAHA ) [71] . Targeted bases not captured were due to SSAHA's inability to determine valid probes possibly resulting from non-unique sequence , repetitive sequence , homopolymer runs or ambiguous bases . Three micrograms of genomic DNA from three cases and three controls was used to prepare libraries for sequencing . DNA samples were fragmented by sonication ( Covaris; Woburn , MA ) and the fragments end-repaired , A-tailed , and ligated to indexing oligonucleotide adapters using NEBNext reagents ( New England Biolabs; Ipswich , MA ) . Indexing adapters were provided by the Broad Institute ( Cambridge , MA ) . The indexed DNA fragments were enriched for by PCR using AccuPrime ( Life Technologies; Grand Island , NY ) and Phusion ( New England Biolabs , Ipswich , MA ) DNA polymerases . DNA purification was done using QIAquick and MinElute PCR purification kits ( Qiagen; Valencia , CA ) . Size selection and purification was done using Agencourt AMPure XP beads ( Beckman Coulter; Beverly , MA ) . Analysis of DNA libraries was done using the Agilent Bioanalyzer DNA 1000 ( Agilent Technologies; Santa Clara , CA ) and Quant-iT dsDNA HS assay ( Life Technologies , Grand Island , NY ) . Libraries were hybridized onto the array using a NimbleGen hybridization system ( Roche NimbleGen; Madison , WI ) at 42°C for 70 hours . Arrays were washed and samples eluted using NimbleGen's elution system ( Roche NimbleGen; Madison , WI ) . Post-capture amplification was done using the primers 5′-AAT GAT ACG GCG ACC ACC GAG-3′and 5′-GAA GCA GAA GAC GGC ATA CGA-3′ with Phusion ( New England Biolabs; Ipswich , MA ) and AccuPrime ( Life Technologies; Grand Island , NY ) enzymes . Quantitative PCR ( qPCR ) using QuantiFast SYBR Green PCR kit ( Qiagen; Valencia , CA ) was done to estimate relative fold-enrichment of the target region ( Table S5 ) . Paired-end sequencing was done on Illumina's Hi-Seq 2000 machine ( Illumina; San Diego , CA ) at the Broad Institute ( Cambridge , MA ) . Sequence reads were aligned to the canine reference genome CanFam2 using the Burrows-Wheeler Aligner ( BWA ) [72] . SNPs and insertion/deletions ( indels ) were determined using the Genome Analysis Toolkit ( GATK; http://www . broadinstitute . org/gatk/ ) [35] . Sequence data was processed according to the current best practices for data analysis found on the online GATK Wiki ( http://www . broadinstitute . org/gsa/wiki/index . php/Main_Page ) and included; initial SNP and indel calling , correcting alignment errors due to indels and inaccurate base quality scores , SNP and indel calling after the realignment and recalibration , and filtering the data using standard filtering parameters . Sequence and variants were viewed with the Integrative Genomic Viewer ( IGV ) [73] . Variants were identified in which all three affected dogs were homozygous for the non-reference allele , the two control carriers were heterozygous and the final control dog was either heterozygous or homozygous for the reference allele . Variants resulting in amino acid changes of coding regions of the genome were considered potential causative mutations for hereditary ataxia . Variants considered as potential causative mutations were genotyped via Sanger sequencing in Old English Sheepdogs and additional dog breeds . The RAB24 gene was also sequenced in affected dogs of breeds in which the A>C RAB24 variant was not present . Primers were designed using Primer3 ( http://frodo . wi . mit . edu/ ) [74] or NCBI's Primer-BLAST ( http://www . ncbi . nlm . nih . gov/tools/primer-blast/ ) [75] ( Table S6 ) . PCR reactions included: 10× Buffer B ( Thermo Fisher Scientific; Waltham , MA ) , 25 mM MgCl2 ( Thermo Fisher Scientific; Waltham , MA ) , 10× MasterAmp ( Epicentre Biotechnologies Madison , WI ) , 25 mM dNTPs ( Apex BioResearch Products , San Diego , CA ) , 10 µM forward and reverse primer ( IDT; Coralville , IA ) , Taq DNA polymerase ( Apex BioResearch Products; San Diego , CA ) and molecular grade water to a volume of 27 µL . DNA amounts varied from approximately 25 ng to 100 ng per reaction . Thermocycler conditions varied by primer set ( Table S7 ) . PCR products were purified using either Agencourt AMPure XP kit ( Beckman Coulter; Beverly , MA ) , QIAquick PCR Purification Kit ( Qiagen; Valencia , CA ) , or MinElute PCR Purification Kit ( Qiagen; Valencia , CA ) . The sequencing of each purified PCR product ( 20–40 ng/µl for 8 µl ) was carried out using the same forward and reverse primers used for PCR . Sequencing was performed by Eurofins MWG Operon ( Huntsville , AL ) following the standard BigDye Terminator v3 . 1 manufacture's protocol ( Applied Biosystems; Foster , CA ) with capillary electrophoresis carried out on the ABI 3730×l DNA Analyzer ( Applied Biosystems; Foster , CA ) . Sections of cerebellum were collected from euthanized affected Old English Sheepdogs ( n = 2 ) , affected Gordon Setters ( n = 2 ) and normal beagles ( n = 2 ) , and the tissue immediately frozen in liquid nitrogen . RNA was extracted from approximately 30 mg of the frozen tissue using Qiagen's RNeasy Mini Kit ( Qiagen; Valencia , CA ) and concentrations measured using a ND-1000 UV-Vis spectrophotometer ( Thermo Scientific; Wilmington , DE ) . Reverse transcription was performed using the Stratagene AffinityScript Multiple Temperature cDNA synthesis kit ( Agilent Technologies; Santa Clara , CA ) and 0 . 5 µg oligo ( dT ) and 0 . 2 µg random primers . Thermocycler conditions were set to 25°C for 10 minutes , 42°C for 5 minutes , 25°C for 60 minutes , and 72°C for 15 minutes . Primers to amplify the mRNA were designed using Primer3 ( http://frodo . wi . mit . edu/ ) [74] , with forward primer 5′-CGTGTCTCCAGGCGTAGC-3′ and reverse primer 5′-ACTGGGGGTAGCTCAGAC-3′ . The approximately 850 bp PCR product was excised from an ethidium bromide stained 2% agarose gel and cleaned using the QIAquick Gel Extraction Kit ( Qiagen; Valencia , CA ) . The cDNA of an affected Old English Sheepdog and Gordon Setter as well as a normal beagle were sequenced using the same forward and reverse PCR primers . Sequencing was performed as was done for genotyping the variants . Quantitative PCR was performed using Applied Biosystems' StepOne Plus instrument and Invitrogen's Power SYBR Green Master Mix ( Life Technologies; Grand Island , NY ) . RAB24 primers were designed using Primer3 ( http://frodo . wi . mit . edu/ ) [74] and PerlPrimer ( http://perlprimer . sourceforge . net/ ) [76] . The housekeeping gene RPS19 ( primers published [77] ) was used for normalization . 20 µl reaction volumes containing 200 nM primers were done in triplicate . Cycling conditions consisted of 95°C for 10 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute with a melt curve performed at completion . Reaction efficiencies were calculated using a 6 point 2∶1 dilution series of 100 ng cDNA . RAB24 and RPS19 efficiencies were estimated to be 90% and 88% respectively with both r2 values of 0 . 999 . Relative expression differences were calculated using the ΔΔCt-method and statistical significance determined using Microsoft Excel ( 2010 ) to perform an unpaired t-test on the relative differences between cases and controls . For histopathology , tissues were fixed in 10% neutral buffered formalin , embedded in paraffin , sectioned to five micrometers and stained with hematoxylin and eosin , PAS , Luxol Fast Blue/Cresyl Violet and Bielschowsky Silver Stain . Immunohistochemistry for GFAP , ubiquitin and Rab24 was performed on formalin fixed paraffin embedded brain tissue . Rabbit polyclonal antibodies were used for all three antigens purchased from Dako ( GFAP ) , Santa Cruz Biotechnology ( ubiquitin ) and Proteintech Group Inc . ( Rab24 ) . 5 µm sections were cut , deparaffinized and rehydrated . GFAP and ubiquitin immunostaining were performed using the Dako Autostainer . Rab24 immunostaining was performed manually . The autostaining method included a five-minute retrieval for GFAP using Dako Proteinase K ( Dako; Carpinteria , CA ) , followed by blocking of endogenous peroxidase activity using 3% hydrogen peroxide for 10 minutes . The primary antibody was applied ( concentrations and incubation time and conditions are given in Table S8 ) , and the product was visualized by incubation with Dako Envision and rabbit polymer ( Dako; Carpinteria , CA ) for 30 minutes followed by application of DAB for five minutes . Slides were counterstained with hematoxylin , dehydrated through sequential alcohol immersion to xylene and cover-slipped . Rab24 immunostaining involved a citrate retrieval using a Pascal Pressurized Heating Chamber at 120°C , followed by blocking of endogenous peroxidase activity using 3% hydrogen peroxide for 10 minutes . A protein block was then performed using normal goat serum ( protein block/normal goat serum , Biogenex; Fremont , CA ) applied for 20 minutes , followed by incubation with rabbit anti-Rab24 at a concentration of 1∶50 for 1 hour at room temperature . Slides were washed with phosphate buffered saline ( PBS ) then biotinylated goat anti-rabbit antibody ( Link; Biogenex , Fremont , CA ) was applied diluted to 1∶20 and incubated for 20 minutes at room temperature . Following washing , peroxidase conjugated streptavidin ( Label; Biogenex , Fremont , CA ) was applied and incubated for a further 20 minutes at room temperature . The product was developed under the microscope using DAB for about one minute to an appropriate level of staining . The slides were counterstained with hematoxylin , dehydrated through sequential alcohol immersion to xylene and cover-slipped . Sections were prepared for transmission electron microscopy from freshly harvested cerebellar tissue as described previously [11] . Samples of cerebellar cortex were cut into approximately 1 mm2 cubes and fixed in McDowell's and Trump's 4F:1G fixative prior to incubation in 1% osmium tetroxide for one hour . Following this period of fixation , samples were placed in a 1∶1 mixture of Spurr resin ( EMS Spurr resin kit 14300 , Hatfield , PA ) and acetone for 30 minutes and then placed in 100% resin changed three times at two hourly intervals . The final change of resin was polymerized at 70°C for eight hours . Semi-thin sections ( 0 . 25 µm ) were cut , stained with 1% toluidine blue-O in 1% sodium borate and used to identify areas of interest . Ultrathin sections ( 70–90 nm ) of these areas were stained with methanolic uranyl acetate ( EMS 22400 , Hatfield , PA ) , followed by lead citrate , and examined by TEM ( FEI/Philips EM208S TEM , Oregon , USA ) . Reagents were obtained from Fisher Scientific ( Pittsburgh , PA ) unless otherwise indicated . All animals evaluated in this study were privately owned pets . DNA samples were obtained with client consent and approval of the Institutional Animal Use and Care Committee or from DNA banks . Euthanasia and necropsies were performed at the owners' request . | Neurodegenerative diseases are one of the most important causes of decline in an aging population . An important subset of these diseases are known as the hereditary ataxias , familial neurodegenerative diseases that affect the cerebellum causing progressive gait disturbance in both humans and dogs . We identified a mutation in RAB24 , a gene associated with autophagy , in Old English Sheepdogs and Gordon Setters with hereditary ataxia . Autophagy is a process by which cell proteins and organelles are removed and recycled and its critical role in maintenance of the continued health of cells is becoming clear . We evaluated the brains of affected dogs and identified accumulations of autophagosomes within the cerebellum , suggesting a defect in the autophagy pathway . Our results suggest that a defect in the autophagy pathway results in neuronal death in a naturally occurring disease in dogs . The autophagy pathway should be investigated in human hereditary ataxia and may represent a therapeutic target in neurodegenerative diseases . | [
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] | 2014 | Canine Hereditary Ataxia in Old English Sheepdogs and Gordon Setters Is Associated with a Defect in the Autophagy Gene Encoding RAB24 |
Type I ( IFN-α/β ) and type III ( IFN-λ ) interferons ( IFNs ) exert shared antiviral activities through distinct receptors . However , their relative importance for antiviral protection of different organ systems against specific viruses remains to be fully explored . We used mouse strains deficient in type-specific IFN signaling , STAT1 and Rag2 to dissect distinct and overlapping contributions of type I and type III IFNs to protection against homologous murine ( EW-RV strain ) and heterologous ( non-murine ) simian ( RRV strain ) rotavirus infections in suckling mice . Experiments demonstrated that murine EW-RV is insensitive to the action of both types of IFNs , and that timely viral clearance depends upon adaptive immune responses . In contrast , both type I and type III IFNs can control replication of the heterologous simian RRV in the gastrointestinal ( GI ) tract , and they cooperate to limit extra-intestinal simian RRV replication . Surprisingly , intestinal epithelial cells were sensitive to both IFN types in neonatal mice , although their responsiveness to type I , but not type III IFNs , diminished in adult mice , revealing an unexpected age-dependent change in specific contribution of type I versus type III IFNs to antiviral defenses in the GI tract . Transcriptional analysis revealed that intestinal antiviral responses to RV are triggered through either type of IFN receptor , and are greatly diminished when receptors for both IFN types are lacking . These results also demonstrate a murine host-specific resistance to IFN-mediated antiviral effects by murine EW-RV , but the retention of host efficacy through the cooperative action by type I and type III IFNs in restricting heterologous simian RRV growth and systemic replication in suckling mice . Collectively , our findings revealed a well-orchestrated spatial and temporal tuning of innate antiviral responses in the intestinal tract where two types of IFNs through distinct patterns of their expression and distinct but overlapping sets of target cells coordinately regulate antiviral defenses against heterologous or homologous rotaviruses with substantially different effectiveness .
Mucosal surfaces of mammalian reproductive , respiratory and gastrointestinal ( GI ) tracts are functionally unique . Most pathogens enter the host through mucosal surfaces , and epithelial cells lining these tracts serve as a first line of defense against invading pathogens . Moreover , mucosal surfaces are constantly exposed to a variety of microbes and therefore have the unique task of distinguishing between harmful pathogens and commensal symbiotic microbes . This challenge is particularly important in the GI tract where tolerance to billions of commensal microbes must be established and maintained . At the same time , the GI tract provides protection against pathogenic bacteria and GI viruses such as rotaviruses ( RVs ) . RV infection causes severe diarrhea in infants and young children and is a major cause of morbidity and mortality in the developing world . The overall incidence of RV infection and morbidity appears to be similar in all unvaccinated areas , however the majority of RV-related deaths occur in developing countries [1 , 2] . Although RV replicates primarily in the mature intestinal epithelial cells ( IECs ) of the small bowel , it can breach intestinal barriers and spread to the circulation and extra-intestinal organs ( e . g . mesenteric lymph node ( MLN ) , central nervous system ( CNS ) , liver and biliary tree ) [1 , 3–5] . Initial antiviral protection in mammalian hosts is mainly dependent on the coordinated action of type I and type III IFNs , which are quickly produced by virus-infected and bystander IECs , as well as by intestinal hematopoietic cells [6–8] . These IFNs invoke innate antiviral mechanisms within virus-infected and uninfected bystander tissues , and coordinately regulate the development of adaptive immune responses against viral pathogens including RV [9–11] . Both IFN types activate the same signal transduction pathway which culminates in the formation of a ternary transcription complex , composed of STAT1 , STAT2 and IRF9 , and designated as IFN-Stimulated Gene Factor 3 ( ISGF3 ) [12–14] . Subsequently , type I and type III IFNs induce expression of the same sets of IFN-stimulated genes ( ISGs ) and have very similar biological activities in sensitive cells [13 , 15–17] . However , type I and type III IFNs engage distinct receptor complexes for their signaling . Whereas all type I IFNs utilize a heterodimeric receptor complex composed of IFN-αR1 and IFN-αR2 subunits , type III IFNs , or IFN-λs , engage the IFN-λR1 and IL-10R2 receptor chains for signaling [8 , 12 , 14 , 18] . A major difference between the type I and type III IFN-based antiviral systems resides in the distinct cell-type specific pattern of receptor expression . In contrast to the type I IFN receptor that is ubiquitously expressed , the IFN-λ receptor is expressed primarily by epithelial cells [19] . Recent studies identified type III IFNs as critical non-redundant antiviral mediators in the GI tract . Type I IFNs alone were unable to restrict reovirus replication in the IECs of mice deficient in the type III IFN receptor [20] . Efficient control of murine norovirus , which replicates in the IECs , dendritic cells and B cells of the mouse , also required a functional IFN-λ receptor [21] . Furthermore , type III IFNs have recently been identified as unique antiviral mediators that were indispensable for the protection of suckling mice against infection with murine RV strain EDIM [22 , 23] . However , the latter result contradicted other studies demonstrating that the murine EW-RV strain ( derived from the original EDIM strain ) replicated to a similar extent in wild-type ( WT ) or STAT1-deficient suckling mice due to its ability to effectively antagonize IFN production and signaling [6 , 24–27] . In contrast , heterologous simian RV ( RRV strain ) was found to replicate poorly in WT mice , but RRV infection of STAT1-deficient suckling mice resulted in substantially enhanced intestinal replication and efficient systemic virus replication and disease [25 , 26] . It was also demonstrated that despite their contrasting IFN-sensitive replication phenotypes , infection of suckling mice with either EW-RV or RRV results in similar induction of several IFN-stimulated genes ( ISGs ) in the small intestine at 16 hours post infection ( hpi ) , confirming prior observations and indicating that RRV replication is uniquely sensitive to one or more of these antiviral effectors [6] . In fact , the substantial restriction of non-homologous RV strain replication in heterologous host species likely underlies the attenuating principle of several live RV vaccines that were based upon restricted replication of bovine , lamb or simian RV in humans [28 , 29] . To further investigate the relative importance of type I and type III IFNs in regulating antiviral defenses in the GI tract , we utilized two distinct strains of RV , the homologous murine EW-RV strain and the heterologous simian RRV strain , and mice deficient in STAT1 , type I or type III IFN receptors , or both types of IFN receptors . Experiments reveal that neither type I nor type III IFNs alone , or both IFN types together were able to efficiently suppress the intestinal replication or diarrheal disease of murine EW-RV , demonstrating that homologous RV have evolved highly effective measures to circumvent the innate responses of their murine host . In contrast , we now demonstrate that both type I and type III IFNs are important mediators of antiviral protection of the GI tract and work cooperatively to limit intestinal replication of the heterologous simian RRV in suckling mice . Transcriptional analysis in the suckling mouse of bulk intestinal tissues revealed that similar patterns of ISG induction occurred in RRV-infected WT mice and in mice lacking either type I or type III IFN receptors , and induction of most ISGs was completely abolished in mice deficient in receptors for both IFN types . Further specific analysis demonstrated that IECs of neonatal mice were responsive to both types of IFNs as determined by immunohistochemical ( IHC ) staining for IFN-induced tyrosine phosphorylation and nuclear translocation of STAT1 . In addition , pretreatment of neonatal mice with either type of IFN resulted in suppressed intestinal RRV replication . We also observed that responsiveness of IECs of adult mice to type I IFNs was diminished , whereas lamina propria cells ( LPCs ) of both neonatal and adult mice were responsive to type I but not type III IFNs . Both type I and type III IFNs helped to limit extra-intestinal RRV spread , but only type I IFNs were essential for controlling RRV replication in MLN . Our results reveal a previously underappreciated contribution of type I IFNs to the protection of IECs against GI viruses in neonatal mice , and demonstrate that both type I and type III IFNs act as important mediators of antiviral defenses within the GI tract , acting cooperatively to suppress heterologous RV replication in IECs and restrict extra-intestinal spread .
To develop mice deficient in type III IFN signaling , exon 3 of the IFNLR1 gene was targeted for elimination ( Fig 1 and S1 Fig ) . LoxP sites flanking exon 3 were introduced into corresponding introns away from the splice signals to ensure that normal splicing of the modified IFNLR1 gene is not disturbed ( Fig 1A ) . The entire gene encoding IFN-λR1 consists of 7 exons . The deletion of exon 3 by Cre recombinase resulted in the generation of an abnormal IFN-λR1 transcript with exon 2 spliced to exon 4 leading to a reading frame shift and the premature termination of translation of the modified IFNLR1 transcript ( Fig 1B and 1C ) . Mice with the deleted exon 3 in the IFNLR1 gene had intact type I IFN signaling , but were unresponsive to type III IFNs as demonstrated by the inability of IFN-λ to trigger STAT1 phosphorylation in various tissues ( Fig 1D ) . In addition , freshly isolated kidney cells from these mice up-regulated MHC class I antigen expression only in response to type I IFN , whereas cells from WT mice responded to both types of IFNs ( S1C Fig ) . Previous studies have shown that RV strains differ in their ability to antagonize IFN responses , both in vitro and in vivo , in part dependent on the species origin of the virus and the host [6 , 30–34] . RV strains are best able to circumvent innate immune responses in their natural , homologous species host . For example , although both heterologous simian RRV and homologous murine EW-RV induce similar levels of type I IFNs and several ISGs in the small intestine at 16 hpi , replication of RRV was highly sensitive to IFN-mediated antiviral defenses and occurred much more efficiently in the intestine of type I IFN receptor and STAT1 KO suckling mice than in WT mice [6 , 25] , whereas murine EW-RV strain was able to replicate comparably in the intestine of suckling mice in the presence or absence of IFNs; EW-RV shedding and clearance proceeded at similar rates in wild type and STAT1 KO mice , which are deficient in type I , II and III IFN signaling [6 , 24 , 26] . In contrast to these results with STAT1 and type I IFN receptor KO mice , two recent studies with IFN receptor-deficient animals indicated that type III IFNs , and not type I IFNs , could mediate very significant and biologically relevant innate antiviral protection of neonatal mice IECs during murine RV infection [22 , 23] . In this studies , it was observed that the action of type III IFNs alone was sufficient to substantially restrict murine RV ( EDIM-RV strain ) intestinal replication and virus shedding while promoting suckling mouse weight gain and diminishing diarrheal disease . On the other hand , as previously reported by others [6 , 24 , 26] , type I IFNs did not appear to play a substantial role in intestinal epithelial antiviral defenses , viral replication , or in protecting the suckling mice from murine RV associated disease [22 , 23] . In order to better clarify the conflicting data in the reports that found no substantial changes on murine RV replication or disease in suckling Stat1-/- mice and the reports that found that murine RV replication and disease was substantially augmented in Ifnlr1-/- mice , eight-day-old WT , Ifnlr1-/- , Ifnar1-/ and Ifnar1-/-Ifnlr1-/- mice ( on the C57BL/6J background ) and WT , Stat1-/- and Rag2-/- mice ( on 129S6/SvEv background ) were orally inoculated with 104 diarrhea dose 50 ( DD50 ) of the murine EW-RV strain derived from the original EDIM-RV isolate [33] , and fecal EW-RV shedding was initially quantified by ELISA . We observed virtually identical fecal shedding of EW-RV in WT , Ifnlr1-/- , Ifnar1-/- , Ifnar1-/-Ifnlr1-/- or Stat1-/- mice during the first 7 days post infection ( dpi ) ( Fig 2A and 2B ) . Slightly delayed viral clearance in suckling mice deficient in type I , type III , or both IFN receptors , together with small differences in virus shedding , was observed on 8 and 9 dpi ( Fig 2A and 2B ) . In agreement with our previous studies [24–26] , we saw little difference in the kinetics of EW-RV clearance between 129S6/SvEv WT and Stat1-/- suckling mice , with a slight increase in shedding only detected in Stat1-/- mice on 9 dpi ( Fig 2A and 2B ) . Consistent with this observation , similar levels of EW-RV protein were detected in the small intestine of infected WT and Stat1-/- mice on 1 dpi ( Fig 2C ) . Of note , unresolved shedding was observed only in Rag2-/- animals ( Fig 2A and 2B ) , demonstrating that adaptive , rather than innate , immune responses are primarily responsible for resolving EW-RV infection . We also detected similar patterns of viral replication in small intestines of EW-RV-infected C57BL/6J WT , Ifnlr1-/- and Ifnar1-/-Ifnlr1-/- mice , as measured by qRT-PCR ( Fig 2D ) . Although IFN-λ transcripts were strongly up-regulated to similar levels in intestines of all mouse strains examined , IFN-β transcripts were induced less efficiently with considerably weaker IFN-β induction in Ifnar1-/-Ifnlr1-/- mice than in WT or Ifnlr1-/- mice ( Fig 2E ) , possibly due to the absence of a positive feedback loop in these animals . ISG induction occurred with similar efficiency in WT and Ifnlr1-/- mice , but was completely abrogated when both IFN-λ and IFN-α receptors were lacking ( Fig 2F ) . Thus , intestinal ISG expression following homologous RV infection can occur in the absence of IFN-λ receptor-mediated signaling . Nevertheless , the presence of IFN and ISG expression has minimal effect on EW-RV replication in small intestine of IFN receptor-sufficient or deficient mice ( Fig 2A–2D and 2F ) , demonstrating that EW-RV efficiently antagonizes most IFN-mediated antiviral responses [27] . These data obtained in both Ifnlr1-/- and Ifnar1-/-Ifnlr1-/- mice and confirmed in Stat1-/- mice differ from the results of recently published studies , where suckling Ifnlr1-/- and Ifnar1-/-Ifnlr1-/- mice were found to be substantially more susceptible to the murine EDIM-RV strain , and type III IFNs were postulated to be the primary mediators of ISG expression in the intestinal epithelium [22 , 23] . In these conflicting studies , mice reconstituted with a functional Mx1 gene were used . In our studies , all the mouse strains examined were on either C57BL/6J or 129S6/SvEv backgrounds and lacked the functional Mx1 gene . However , conventional C57BL/6J mice that are deficient in Mx1 , and Mx1-reconstituted C57BL/6J mice showed no differences in either EW-RV replication or in their patterns of IFN and ISG induction ( S2A–S2D Fig ) , ruling out the possibility that Mx1 was responsible for the observed differences between these studies . We also obtained the murine EDIM-RV strain that was used in the conflicting studies [22 , 23] and compared it to our murine EW-RV strain that was also derived from the original EDIM strain . Both strains replicated similarly in WT or Stat1-/- 129S6/SvEv or C57BL/6J WT suckling mice ( S3A and S3B Fig ) , indicating that differences in the replication phenotype of murine RV in the two studies were not likely due to viral strain variations . Overall , EW-RV replication in suckling mice was not significantly affected by the presence of either type I or type III IFNs , confirming the substantial insensitivity of murine RV to IFN-mediated antiviral effects on virus replication in the suckling murine host [6 , 24 , 26] . A previous study also revealed substantial growth retardation of EDIM-RV-infected Ifnlr1-/- pups compared to their WT counterparts , and correlated these differences with increased EDIM-RV replication in Ifnlr1-/- mice [23] . To determine whether lack of IFN signaling might affect pathophysiologic parameters other than RV replication , weight gain and diarrheal disease were also monitored in EW-RV-infected suckling WT , Stat1-/- and Rag2-/- mice ( on 129S6/SvEv background ) . Diarrhea appeared on 2 dpi in all groups , affected virtually all inoculated pups , and resolved between 8 and 11 dpi ( Fig 3A ) , with no difference in the numbers of animals affected , despite continuous virus shedding by Rag2-/- mice ( Figs 2A and 3A ) . However , diarrhea was moderately prolonged in the EW-RV-infected Stat1-/- mice ( Fig 3A ) , suggesting that IFN signaling may affect the duration of murine RV-associated diarrheal disease . Furthermore , similarly delayed resolution of diarrhea was observed in Stat1-/- mice infected with simian RRV ( Fig 3A ) . Despite moderately prolonged diarrhea in Stat1-/- animals or the continued EW-RV shedding in Rag2-/- mice , body weight gain of WT , Stat1-/- and Rag2-/- mice in either EW-RV or RRV-infected groups remained similar ( Fig 3B ) . These experiments suggest that RV-induced diarrhea and weight gain are not necessarily correlated with virus load , since the chronically infected Rag2-/- mice ( Fig 2A ) resolved diarrhea earlier than EW-RV or RRV-infected Stat1-/- pups and exhibited body weight gain comparable to WT mice ( Fig 3A ) . Therefore , although IFNs may be involved in the timely resolution of murine RV-induced diarrhea , this is not directly correlated with either virus load or weight gain . Of interest , although the level of shedding and the severity of diarrheal disease have been directly correlated in children [35] , this correlation is not invariable since the Rag2-/- mice resolved diarrhea while continuing to shed RV ( Figs 2 and 3 ) . Because homologous murine RV is remarkably resistant to IFN-mediated innate responses in suckling mice ( Fig 2 ) [6 , 21–23] and because prior studies had indicated that heterologous RVs might be more responsive to IFN mediated suppression [5 , 6 , 25] , we next examined the heterologous simian RRV to assess the relative contributions of type I and type III IFNs to the control of non-murine RV replication and clearance . Eight-day-old suckling WT mice or mice deficient in IFN type-specific signaling ( Ifnlr1-/- , Ifnar1-/- and Ifnar1-/-Ifnlr1-/- mice , all on C57BL/6J background ) were orally inoculated with 4 x 106 FFU RRV . Intestinal samples were collected and RV titers determined on 1 , 3 , 5 and 8 dpi . On 1 and 3 dpi , there were no significant differences in intestinal virus replication between Ifnlr1-/- and Ifnar1-/- mice , whereas either type I or type III IFN receptor-deficient ( Ifnar1-/- or Ifnlr1-/- ) animals supported significantly greater intestinal RRV replication ( >100 fold ) than did WT mice ( Fig 4A and 4B ) . Importantly , RRV replicated to significantly higher titers in Ifnar1-/-Ifnlr1-/- and Stat1-/- mice than in mice lacking either IFN receptor alone ( Fig 4A and 4B ) . Ifnar1-/-Ifnlr1-/- and Stat1-/- mice also showed delayed virus clearance , with virus still present in the small intestine on 8 dpi , a time point when virus could no longer be detected in WT , Ifnar1-/- and Ifnlr1-/- strains ( Fig 4A and 4B ) . Low , but sustained , RRV levels were detected in the small intestine of Rag2-/- mice from 1 to 8 dpi ( Fig 4A and 4B ) , which persisted through 15 dpi . Consistent with the virus titer results , infected IECs were rarely detected in the small intestines of RRV-infected Ifnar1-/- and Ifnlr1-/- mice by immunohistochemistry , with much more extensive antigen-staining present in the IECs of infected Ifnar1-/-Ifnlr1-/- animals ( Fig 4C ) . Viral antigen was found primarily in IECs at the tips of the villi in type I or type III IFN receptor-deficient mice , and RRV-infected IECs were essentially absent in infected WT mice . These results indicate that both type I and type III IFNs independently restrict replication of the heterologous simian RRV in intestines of suckling mice , with resolution of infection mediated primarily by the adaptive immune response . RV gene transcriptional analysis revealed robust RRV replication in Ifnar1-/-Ifnlr1-/- mice , with incremental decreases in replication occurring in the single IFN receptor KO and WT pups , respectively ( Fig 4D ) . The induction of IFN-β transcripts by RRV in WT mice occurred primarily on 1 dpi . In comparison , in mice lacking receptors for either type I or type III IFNs , as well as in the double IFN receptor KO mice , IFN-β induction was more robust and occurred over a prolonged period of time following RRV infection , particularly in Ifnar1-/-Ifnlr1-/- mice and to a lesser extent in Ifnlr1-/- mice ( Fig 4E ) . Similar expression patterns were observed for IFN-λ transcripts but with sustained up-regulation of IFN-λ expression on 2 and 3 dpi in all mouse strains ( Fig 4E ) . Similar to IFN-β , expression levels of IFN-λ transcripts were elevated in all KO strains in comparison to WT mice , and sustained elevated expression of IFN-λ transcripts was mostly pronounced in either Ifnar1-/-Ifnlr1-/- or Ifnlr1-/- mice . Patterns of IFN expression ( Fig 4E ) mirrored the transcriptional RRV load ( Fig 4D ) , suggesting that increased viral replication in the absence of the cognate IFN receptors and their effector pathways triggers prolonged and elevated expression of both IFN types in IFN receptor deficient mice . Because expression of IFN transcripts was similar in response to RRV infection of WT and single or double IFN receptor-deficient mice ( Fig 4E ) , type I and type III IFNs appear to be induced independently during RV infection . To assess whether much more robust up-regulation of Ifnl transcription compared to Ifnb transcription correlates with higher levels of type III IFN protein expression , homogenates of small intestines from RRV-infected mice were collected on 1 dpi and used for IFN-λ ELISA and type I IFN bioassay ( S4 Fig ) . Whereas IFN-λ proteins were detected at about 300 to 500 pg per 100 mg of tissue , levels of type I IFNs were below the detection level of the bioassay ( <30 units/ml; ~300 pg per 100 mg ) . These results correlate with our transcriptional analyses and indicate that RRV infection predominantly triggers production of type III IFNs in the small intestine . Both the magnitude and kinetics of ISG expression were similar in WT and single IFN receptor-deficient mice ( Fig 4F ) , demonstrating that either type I or type III IFN can up-regulate ISG expression in small intestine of RRV-infected mice independently . Moreover , the increased expression of these ISGs was abolished in double IFN receptor KO mice after RRV infection , despite the induction of type I and type III IFN transcripts ( Fig 4E and 4F ) . The diminished ISG induction in Ifnar1-/-Ifnlr1-/- mice correlated with increased viral replication in these animals , reflecting the sensitivity of RRV to IFN-mediated innate antiviral defenses . The delayed RRV clearance in Ifnlr1-/- mice ( Fig 4D ) correlated with only a modest induction of Ifnb ( Fig 4E ) and the lack of transcriptional Ifna responses ( S5A–S5C Fig ) . In contrast , levels of Ifnl2/3 transcription were substantially elevated in response to RRV infection ( Fig 4E ) and correlated with fast reduction of RRV by 2 dpi in Ifnar1-/- mice ( Fig 4D ) , suggesting a predominant role of type III IFNs in the intestinal antiviral defense . Because RRV replication was significantly increased in Ifnar1-/-Ifnlr1-/- and Stat1-/- animals when compared to single IFN receptor KO mice , it seemed likely that IECs in suckling mice can respond to either IFN-α or IFN-λ . Such a possibility is also supported by the abrogation of RRV-mediated intestinal ISG expression only in the absence of receptors for both IFN types . Signaling downstream of either the type I or type III IFN receptor leads to tyrosine phosphorylation of STAT1 ( pSTAT1 ) . To directly investigate responsiveness of IECs and cells within the lamina propria to IFNs , eight-day-old suckling C57BL/6J mice were subcutaneously injected with PBS , IFN-α , or IFN-λ , and levels and nuclear translocation of pSTAT1 in small intestine were assessed by immunohistochemical staining with pSTAT1 specific antibody ( Fig 5A ) . Both IECs and LPCs of the small intestine of suckling mice were responsive to IFN-α , whereas only IECs were responsive to IFN-λ ( Fig 5A ) . To exclude the possibility that lack of type III IFN signaling might alter responsiveness of IECs to type I IFNs , Ifnlr1-/- suckling mice were also treated with IFN-α and STAT1 phosphorylation was again examined in the small intestine . Type I IFN-induced pSTAT1 was detected in both IEC and LPC compartments while no pSTAT1 staining was found in response to IFN-λ treatment ( Fig 5B ) . Therefore , in the suckling mouse , both type I and type III IFNs are capable of triggering STAT1 activation in IECs , whereas LPCs are only responsive to type I IFNs . These data from suckling mice are different from previous observations wherein mouse IECs were found to be unresponsive to type I IFNs when adult mice were treated with plasmid-delivered IFNs [20 , 22] . To investigate whether the IFN responsiveness of IECs might be age-dependent , IFN-mediated STAT1 activation was subsequently assessed in six to eight-week-old WT or Ifnlr1-/- mice . Only LPCs , but not IECs , were strongly responsive to type I IFNs in the older mice , whereas responsiveness of IECs to type III IFNs remained robust in adult animals ( Fig 5C and 5D ) . Low levels of STAT1 phosphorylation were detected in PBS-treated IECs in adult WT mice , but not in mice deficient in type III IFN receptor ( Fig 5C and 5D ) , suggesting that weak , constitutive IFN-λ signaling is likely to be maintained in IECs in adult mice . These results reveal an unexpected age-related change in the type I IFN responsiveness of IECs , which is robust in early post-natal life , and strongly diminished as the mouse matures . To directly assess whether STAT1 activation in IECs correlates with antiviral protection , eight-day-old suckling mice were subcutaneously injected with either IFN-α or IFN-λ 6 h before RRV infection , and virus replication was analyzed on 1 dpi ( Fig 5E ) . Pretreatment with either type of IFN resulted in reduced RRV levels on 1 dpi , demonstrating that both type I and type III IFNs inhibit intestinal RRV replication . In a previous study , unresponsiveness of IECs to systemic type I IFN treatment was explained by the polarized nature of IFN signaling in IECs [22] . In that study , polarized IECs were shown to respond to type I IFNs only when IFN-β was delivered apically , whereas type III IFNs were active on both basolateral and apical surfaces [22] . In contrast , our experiments with human SW-1116 colorectal carcinoma cells demonstrated that upon polarization , these cells strongly respond to either type I or type III IFNs only basolaterally ( Fig 5F ) . Of note , sensitivity of SW-1116 cells to IFN-λ was enhanced upon polarization to higher degree than that to IFN-α , and weak responsiveness to IFN-λ at the apical surface was also detected ( Fig 5F ) . Overall , these results demonstrate that type I and type III IFNs are capable of inducing antiviral protection in IECs of suckling mice in a redundant manner . Previous mouse studies demonstrated that RRV can spread to and replicate in extra-intestinal sites as efficiently as murine RV including MLN , and that type I IFNs are important for restricting RRV replication and pathogenesis at these sites [4 , 5 , 25] . To further investigate the role of specific IFNs in controlling early extra-intestinal spread and replication , RRV titers in MLN of infected mice were assayed . There were no significant differences between virus titers in MLN of RRV-infected WT and IFN receptor-deficient animals on 1 dpi ( Fig 6A ) . However , by 3 dpi , elevated virus titers on the order of 100-fold greater than WT were detected in MLN of RRV-infected Ifnar1-/- mice , and 1 , 000-fold above WT levels in Ifnar1-/-Ifnlr1-/- animals ( Fig 6B ) . RRV replication was still detectable in MLN of Ifnar1-/- and Ifnar1-/-Ifnlr1-/- mice on 5 dpi , and MLN from several Ifnar1-/-Ifnlr1-/- mice were still RV positive on 8 dpi ( Fig 6A and 6B ) . Levels of RRV were not elevated above WT controls in the Ifnlr1-/- mice . Therefore , type I IFNs mediate the primary control of extra-intestinal spread and replication of RRV in MLN , although a further deficiency in type III IFN signaling enhances virus replication in MLNs when combined with a type I IFN deficiency at early times post infection . Consistent with these data , elevated virus titers were also found in MLN of RRV-infected Stat1-/- mice on 3 dpi , and virus was still detectable in MLN of some Stat1-/- mice on 8 dpi ( Fig 6A and 6B ) . RRV titers in the MLN of infected Rag2-/- were mainly unchanged from 1 dpi to the conclusion of the experiment on 8 dpi ( Fig 6A and 6B ) , emphasizing the importance of adaptive immunity for virus clearance . Murine and simian RV strains have also been shown to spread to the liver and replicate in the epithelial lining of the biliary tree [4 , 5 , 25] . To investigate involvement of type I and type III IFNs in limiting systemic simian RV infection in the liver , we determined hepatic virus titers in RRV-infected single and double IFN receptor-deficient mice . WT and single IFN receptor-deficient mice showed similar levels of virus replication in the liver on 1 dpi ( Fig 6C and 6D ) . RRV titers had declined in WT controls by 3 dpi , but remained significantly elevated in infected Ifnar1-/- , Ifnlr1-/- and Ifnar1-/-Ifnlr1-/- animals ( Fig 6C and 6D ) . Although virus was cleared from the liver of WT and single IFN receptor-deficient mice by 5 dpi , RRV persisted in the liver of double IFN receptor-deficient Ifnar1-/-Ifnlr1-/- suckling mice through 8 dpi ( Fig 6C and 6D ) . These data indicate that type I and type III IFNs cooperate to limit RRV spread to and replication in the liver of infected mice . Furthermore , although liver virus titers were similar in 129S6/SvEv WT , Stat1-/- and Rag2-/- mice on 1 , 3 and 5 dpi , both Stat1-/- and Rag2-/- mice had higher liver virus titers than WT controls on 8 dpi ( Fig 6C and 6D ) . Of note , all mouse strains on the 129S6/SvEv background showed higher virus titers in the liver than any mouse strain on the C57BL/6J background ( Fig 6C and 6D ) , indicating that levels of RRV replication in the liver are also affected by strain-specific genetic factors . Recent studies concluded that intestinal antiviral responses are primarily mediated by type III , rather than type I , IFNs during infection with the homologous murine EDIM-RV [22] . In contrast , we observed that murine EW-RV replication was rather insensitive to the antiviral actions of both type I and type III IFNs in the homologous murine host ( Fig 2 ) . On the other hand , the replication of the heterologous simian RRV in suckling mice was substantially restricted by both IFN types ( Figs 4–6 ) . In addition , when either IFN was administered systemically , it was able to efficiently stimulate STAT1 activation in IECs of suckling mice ( Fig 5A and 5B ) and induce antiviral protection against RRV in IFN-pretreated suckling mice ( Fig 5E ) . These findings provided a clear phenotype and biologically relevant RV strain to decipher the relative roles of these two types of IFNs in intestinal innate antiviral responses . To perform transcriptional analysis , small intestines of RRV-infected WT mice , as well as pups lacking receptors for either type I or type III IFNs , or both receptors , were isolated 1 , 2 and 3 dpi and used for microfluidic qRT-PCR analysis of selected antiviral response transcripts at the bulk whole intestinal level ( Fig 7 ) . In these experiments , we also included uninfected animals as well as murine EW-RV-infected WT , Ifnlr1-/- and Ifnar1-/-Ifnlr1-/- mice harvested at a single time point ( 2 dpi ) . Of note , we had previously shown that at 16 hpi , despite their substantially different replication capacity in vivo , both EW-RV and RRV infections result in comparable levels of ISG and type I IFN induction in bulk intestinal tissues of WT suckling mice [6] . Intestinal EW-RV replication was 1 , 000- to 10 , 000-fold greater than that of RRV in WT suckling mice ( Fig 7A ) . Similar to earlier observations at 16 hpi [6] , the overall transcriptional levels of EW-RV-induced antiviral cytokines such as IFN-λ and IFN-β , and several IFN-induced antiviral genes such as ISG15 and IFIT3 , were similar to , or greater than those induced by RRV on 2 dpi ( Figs 7B–7D ) . We found that infection with the IFN-sensitive RRV strain resulted in the robust induction of several well-defined ISGs , including those encoding IFIT1/2/3 , ISG15 , ISG20 , RSAD2 , and Mx2 ( Fig 7C and 7D ) . Notably , transcription of such ISGs was also induced in the absence of either type I or type III IFN receptor in agreement with the ability of both IFN types to trigger STAT1 phosphorylation in IECs , but was almost completely abolished in Ifnar1-/-Ifnlr1-/- animals ( Fig 7D ) . Thus , type I and type III IFNs drive a set of highly similar antiviral intestinal responses to both EW-RV and RRV , but effectively restrict the replication of only heterologous simian RRV . In the absence of type I and type III IFN signaling , the attenuated RRV-induced transcription of certain ISGs such as ISG20 and RSAD2 can be driven by interferon regulatory factors ( IRFs ) directly or mediated by other virus-induced mechanisms [36 , 37] . The absence of types I and type III IFN receptors led to prolonged induction of CXCL10 and CCL5 chemokine genes , correlating with extended and increased viral replication . Of interest , genes encoding the anti-microbial proteins REG3B and REG3G ( S6 Fig ) were induced independently of IFNs by both EW-RV and RRV , with more consistent and higher levels of up-regulation in EW-RV-infected mice . Expression of these genes can be controlled by IL-22 , which was recently implicated in host anti-RV restriction [23 , 38] . Collectively , transcriptional analysis of bulk intestinal tissues revealed a surprising level of redundancy in the induction of intestinal antiviral responses in suckling mice by type I and type III IFNs .
Type I and type III IFNs are important mediators of innate antiviral defenses . Although these IFNs signal through distinct receptor complexes , the signaling cascades , sets of ISGs up-regulated and biological activities induced in response to these cytokines are almost indistinguishable [8 , 9 , 13] . For this reason , the relative contributions of type I and type III IFNs to overall antiviral protection of an entire organism can only be investigated with the use of animals deficient in individual and combined specific IFN receptors . Due to the cell-type specific pattern of type III IFN receptor expression that largely limits action of IFN-λs to epithelial cells [19] , the target organs for type III IFNs are restricted , whereas type I IFN receptors are expressed ubiquitously , and therefore , expected to evoke antiviral defenses in all tissues and cell types . Nevertheless , there have been several reports demonstrating that mice deficient in STAT1 , a transcriptional factor that is critical for signaling of all IFNs , are more susceptible to certain viruses than type I IFN receptor-deficient mice [39 , 40] . For example , influenza virus replicated to much higher titers in STAT1 or STAT2 KO mice than in type I IFN receptor-deficient animals [39] suggesting that type III IFNs may also play a role in protecting mice against influenza virus infection . Indeed , it has been demonstrated that protection against influenza A virus replication in airway epithelium can be mediated by either type I or type III IFNs [41–43] . Similarly , it has been shown that RRV replicates better in Stat1-/- than in Ifnar1-/- suckling mice [6 , 25 , 26] . It was recently reported that the GI epithelium , and particularly IECs , are protected primarily by type III IFNs in suckling and adult mice , based on the observation that mice deficient in IFN-λ signaling had impaired control of murine RV infection when compared with strain-matched WT and Ifnar1-/- mice [22] . In our experiments , on the other hand , the level of murine RV shedding was high and almost indistinguishable in WT or IFN receptor-deficient or STAT1-deficient suckling mice with the exception of slightly delayed but significant clearance differences on 8–9 dpi in Ifnlr1-/- and Stat1-/- mice; with complete virus clearance from all mouse strains on 10 dpi ( Fig 2 ) . In addition , in the current study weight loss and the degree of diarrheal disease were not substantially enhanced in Ifnlr1-/- and/or Stat1-/- mice when compared to WT suckling mice . These data are inconsistent with the results of Pott et al . and Hernandez et al . , which suggested that pathogenesis and susceptibility to murine RV was highly IFN-λ dependent [22 , 23] . The basis of the different findings in the studies ( Summarized in Table 1 ) is not readily apparent . Direct comparison of the two murine RV strains used in the two groups of studies indicates that they are highly related or identical in terms of replication capacity in WT and Stat1-/- suckling mice ( S3 Fig ) . In addition , although mice , which were used in studies of Pott et al . and Hernandez et al . [22 , 23] , have a reconstituted functional Mx1 gene , whereas mice used in other studies possess a non-functional Mx1 gene , kinetics and magnitude of EW-RV replication were similar in WT suckling mice deficient or reconstituted with the functional Mx1 gene ( S2A and S2B Fig ) and profiles of transcriptional IFN and ISG induction were similar ( S2C and S2D Fig ) . Of note , the Mx1-reconstituted mice were generated by breeding the A2G-Mx1+/+ mice onto the Mx1-/- C57BL/6 mice for several generation , however the purity of the genetic background of the resulting B6 . A2G-Mx+/+ strain has not been characterized [44] . Moreover , B6 . A2G-Mx+/- males , starting from F2 generation , were selected for backcrossing with C57BL/6 females based on their survival of the infection with lethal dose of influenza virus infection . This breeding strategy , in addition for maintaining the Mx+/- genotype in breeders , may also put a selective pressure skewing for genes , other than Mx1 , which also enhance virus resistance . These B6 . A2G-Mx+/+ mice were later crossed with the Ifnlr1-/- C57BL/6J mice [41 , 45] . It should also be noticed that mice used in the current studies have only a small alteration within the Ifnlr1 gene , only exon 3 was deleted ( Fig 1 and S1 Fig ) , whereas mice used in studies of Pott et al . and Hernandez et al . [22 , 23] , have the entire the Ifnlr1 gene ( ~20 kb ) removed and replaced with the IRES-LacZ/MC1-Neo reporter gene/selection cassette ( ~5 kb ) [45] . This substantial genomic alteration could potentially affect expression of other neighboring genes , particularly the Il22ra gene that is located downstream of the Ifnlr1 gene and encodes one of the IL-22 receptor chains . IL22RA ( IL-22R1 ) shares epithelial cell specific expression pattern with IFN-λR1 and these two adjacent genes may share co-regulatory elements . Therefore , further studies are required to fully characterize and compare the two currently existing Ifnlr1-/- mice . In addition , animal diet , microbiota and persistent infections with murine norovirus or helicobacter , which are often present in pathogen-free animal facilities , have been shown to alter innate intestinal antiviral responses [21 , 46–48] , and therefore could potentially account for some of the observed differences in this and other studies . Of note , we did observe declining responsiveness of IECs to type I IFNs in adult mice ( Fig 5C and 5D ) , but saw robust IFN signaling in suckling mice IECs following treatment with either IFN-α or IFN-λ ( Fig 5A and 5B ) . We also observed that low constitutive levels of STAT activation are present in IECs of WT adult mice , but not in Ifnlr1-/- mice ( Fig 5D ) , suggesting that IFN-λ signaling seems to be maintained in IECs and may contribute to the well documented decreased ability of murine EDIM-RV to replicate as efficiently in adult as in suckling mice [49] . These findings are interesting but unlikely to fully account for the differences observed between the two sets of studies ( Table 1 ) as both of these were carried out in suckling mice . Recent studies on intestinal antiviral immunity have shown compartmentalized effects of type I and type III IFNs , where LPCs were protected only by type I IFNs , whereas type III IFNs were indispensable for restricting reovirus or RV replication in IECs [20 , 22] . Using a direct immunohistochemical assay of IFN-triggered STAT1 activation in IECs and LPCs , we also observed that LPCs responded only to type I IFNs by STAT1 activation ( Fig 5 ) . However , we observed that administration of either type I or type III IFNs could induce the restriction of RRV replication in the small intestine of suckling mice with one caveat: the responsiveness of IECs to type I IFNs was substantially more pronounced in neonatal mice , where RV disease is present , than in adults ( Fig 5 ) , where RV replication is restricted and RV associated disease is absent [50 , 51] . RV infections are remarkably host specific . Homologous RVs replicate to significantly higher levels in the intestines of homologous hosts , require much lower doses to cause disease and spread more efficiently among non-immune susceptibles than heterologous RVs . As with several other virus infections , it has been shown that RV host-range restriction is , in large part , determined by the different efficiency of homologous versus heterologous RVs in antagonizing the host IFN response [6 , 25 , 26] . Indeed , we also observed that murine EW-RV replicated in mice much more efficiently than heterologous RRV ( Fig 7A ) . However , the higher EW-RV load induced similar magnitude of IFN ( Figs 2E , 4E and 7B ) and ISG ( Figs 2F , 4F , 7C and 7D ) responses as the much lower load of RRV . Similar findings were reported when responses to EW-RV and RRV were compared at 16 hpi in suckling mouse intestines , and reinforce the notion that homologous murine RVs have evolved highly effective measures to circumvent host innate immune responses in order to replicate efficiently and cause diarrheal disease that promotes virus dissemination [6 , 25 , 26] . The robust murine RV replication was not appreciably augmented in IFN receptor or STAT1 deficient suckling mice ( Fig 2 and [6 , 24–27] ) . Therefore , in order to study the relevant importance of type I and type III IFNs in initiating and propagating intestinal innate immune responses , we used heterologous simian RRV that has been previously shown to be much more sensitive than homologous murine RV to innate antiviral defenses in suckling mice [6 , 25 , 26] . RRV replicates but only poorly in the WT suckling mouse intestine and is unable to spread from inoculated to susceptible litter mates while murine EW-RV is more virulent , replicates to much higher levels in the mouse intestine , and spreads very efficiently among litter mates . Our results revealed that RRV replicated much more efficiently in either type I or type III IFN receptor-deficient suckling mice , and the complete lack of IFN responses in Ifnar1-/-Ifnlr1-/- or Stat1-/- mice allowed RRV replication to proceed to even higher titers with delayed clearance in comparison to single IFN receptor-deficient mice ( Fig 4 ) . Accordingly , pretreatment of suckling mice with either IFN-α or IFN-λ 6 h prior to RRV infection suppressed intestinal RRV replication to the similar extent and combined IFN-α or IFN-λ pretreatment provided a similar level of protection as pretreatment with either type of IFN alone ( Fig 5E ) . On the other hand , pretreatment of suckling mice with type I or II interferon had no effect on homologous murine RV replication or diarrheal disease [52] . In addition , transcriptional analysis in the whole intestine of RV-infected suckling mice revealed that classical ISGs were induced to similar levels in either type I or type III IFN receptor-deficient animals ( Fig 7 ) , confirming independent and overlapping actions of type I and type III IFNs in the intestinal antiviral defense of suckling mice . RRV was also able to replicate more efficiently in MLN of Ifnar1-/- or Ifnar1-/-Ifnlr1-/- mice than in WT or Ifnlr1-/- mice ( Fig 6A and 6B ) , suggesting that at this site , type I and not type III IFNs were primarily responsible for controlling RV replication . Both single and double IFN receptor KO mice demonstrated impaired control of RRV replication in the liver ( Fig 6C and 6D ) , but only Ifnar1-/-Ifnlr1-/- mice failed to clear RRV from the liver by 5 dpi ( Fig 6C and 6D ) . Of note , RRV replication was better controlled by type III IFNs at earliest times post infection , because increased viral transcription was detected only on 1 dpi , and was quickly suppressed by 2 dpi in Ifnar1-/- mice , whereas viral transcripts were still elevated on 2 and 3 dpi in Ifnlr1-/- mice ( Fig 4D ) , suggesting a somewhat more prominent role of type III IFNs in controlling intestinal RV replication and this correlated with more efficient Ifnl2/3 induction by RV than those of type I IFNs ( Figs 4E and 7B , and S4 Fig ) . More prolonged intestinal RRV replication in Ifnlr1-/- mice might give virus more time to disseminate to and replicate in other organs . Nevertheless , we observed distinct patterns of RRV spread and replication in MLNs and liver , the former controlled primarily by type I IFNs ( Fig 6A and 6B ) and the latter by both IFN types ( Fig 6C and 6D ) . Collectively , these data demonstrate that neither IFN alone or together play a significant role in regulating the robust replication and disease phenotypes of the homologous murine RV in suckling mice . On the other hand , these studies clearly demonstrate that both type I and type III IFNs are required for optimal antiviral protection of the GI tract of suckling mice against the heterologous simian RRV infection , and that both IFN types independently contribute to innate antiviral defenses within the intestinal mucosal compartment ( Figs 4 , 5 and 7 ) and cooperate to restrict extra-intestinal RRV replication in other tissues ( Fig 6 ) . Our studies also identified a reduced sensitivity of IECs but not LPCs to the effects of type I but not type III IFNs as mice mature . Overall , our findings highlight a multi-faceted complexity of the virus-host interactions and reveal a well-orchestrated spatial and temporal tuning of innate antiviral responses in the intestinal tract where two types of IFNs through distinct patterns of their expression and distinct but overlapping sets of target cells coordinately regulate antiviral defenses . Our findings also highlight the fact that the antiviral capacity of the various IFNs can vary very significantly between strains of the same virus in a host dependent manner .
Conventional specific pathogen-free ( SPF ) WT C57BL/6J mice were purchased from Jackson Laboratory . Mice lacking functional IFN-λ receptor ( Ifnlr1-/- ) were generated in the laboratory . Recombineering techniques were used to create a KO targeting vector that contained exon 3 of the mouse IFNLR1 gene flanked with two LoxP sites and ~10 kb arms for homologous recombination ( Fig 1A ) . A neo ( G418-selection ) cassette flanked with the FRT sites was introduced in front of the LoxP site in intron 4 . The accuracy of all modified sequences within the targeting vector was verified by sequencing . Bruce4 mouse embryonic stem ( ES ) cells from C57BL/6J strain were transfected with the targeting vector , and G418-resistant ES clones were selected and screened by Southern blotting for correct integration of the targeting fragment ( S1A and S1B Fig ) . Chromosomal DNA was obtained from G418-resistant ES clones , digested with EcoRV restriction endonuclease , subjected to Southern blotting with a hybridization probe corresponding to exons 1 and 2 of the mouse IFNLR1 gene that are positioned outside of the left arm for homologous recombination ( S1A Fig ) . Twenty three clones were selected and their DNA was digested with AflIII restriction endonuclease , and Southern blotting was performed with a probe corresponding to exons 5 , 6 and 7 that are outside of the right arm for homologous recombination ( S1B Fig ) . One of the clones with the correct integration pattern at both 3' and 5’ ends was used for the generation of chimeric mice , and subsequently mice homozygous for the integration cassette . First , the neo cassette was eliminated by crossing the chimeric mice with C57BL/6J mice transgenic for the CMV promoter-driven flipase ( Jackson Laboratory , Stock # 009086 ) . Mice homozygous for the deletion of the neo cassette were selected , followed by the selection against the flipase gene . These mice were then crossed with C57BL/6J mice transgenic for the CMV promoter-driven Cre recombinase ( Jackson Laboratory , Stock # 006054 ) , and mice homozygous for the deletion of the IFN-λR1 exon 3 and lacking the Cre gene were selected . These IFN-λ receptor-deficient animals were crossed with C57BL/6J mice lacking functional type I IFN receptor ( Ifnar1-/- mice ) in the laboratory of Jörg Fritz at McGill University; and Ifnar1-/- and Ifnar1-/-Ifnlr1-/- mice were provided for these studies . Congenic B6 . A2G-Mx1 mice carrying intact Mx1 alleles [44] and EDIM-RV isolate were provided by P . Staeheli . All mouse strains on C57BL/6J background were maintained at SPF barrier facility at NJMS , Rutgers . Mouse strains on 129S6/SvEv background were described previously [25] and maintained in the vivarium at the Veterinary Medical Unit of the Palo Alto VA Health Care System . Eight-day-old suckling mice were orally inoculated with 104 DD50 of the murine EW-RV strain or 4x106 FFU of the simian RRV strain . The EW-RV strain was derived following serial suckling mouse passage from the original E . Kraft EDIM-RV isolate [33] . From 2 to 12 dpi for EW-RV infection or from 2 to 8 dpi for RRV infection , animals were examined daily for the occurrence of diarrheal disease . The percentage of diarrhea among inoculated littermates during the course of infection for each group was recorded . To measure the effects of RV infection and IFN deficiency on suckling mouse body weight gain , EW-RV or RRV-infected or non-infected WT 129S6/SvEv , Stat1-/- and Rag2-/- mice were weighed daily during the course of experiments . Daily mouse weight ratio was calculated for each infected mouse as weight of infected mouse ( g ) / mean weight of uninfected control mice ( g ) of the same age . Fecal specimens ( approximately 10–20 μl ) were collected from EW-RV-infected suckling mice into pre-weighed eppendorf tubes . Samples were stored at -80ºC prior to fecal EW-RV shedding detection by ELISA . At indicated day post EW-RV or RRV infection , a number of mice from each experimental group were sacrificed for tissue collection and histology . All IFNs were injected intradermally in adult or suckling mice . Human hybrid IFN-αA/D and mouse IFN-λ2 were used at the concentrations indicated in the figure legends . Human IFN-αA/D was previously shown to be highly active on many mouse cell types in vitro and in vivo [53] . Human SW-1116 cells ( ATCC CCL-233 ) were plated at confluency onto transwell filters and cultured for 56 days ( media was changed every other day ) until epithelial layer of well-polarized epithelial cells with high trans-epithelial resistance ( TER > or = 2000 ohm/cm2 ) was established . In parallel , SW-1116 cells were also grown in continuously proliferating cultures on regular plates . The cells were left untreated or treated at the apical or basolateral surfaces with various amounts of IFN-α or IFN-λ as indicated . At 72 h , the cells were collected , and levels of MHC class I antigen expression were evaluated by flow cytometry . At various time points post RRV infection , suckling mice were anesthetized , and tissue samples from liver , MLN , and small intestine were collected and stored at -80°C . Before assay , the thawed tissue samples were individually weighed and made to 10% ( wt/vol ) suspensions with serum free M199 . Samples were homogenized in 5 ml polypropylene tubes and the homogenates were activated with trypsin ( 10 μg/ml ) for 1 h at 37°C in a 5% CO2 incubator . Total homogenates were centrifuged at 1 , 500 rpm for 10 min and the supernatants were serially diluted in serum free M199 . MA-104 . 1 cells ( ATCC CRL-2378 . 1 ) were inoculated in a 24-well plate with 0 . 1 ml of diluted supernatant . After absorption for 1 h at 37°C in a 5% CO2 incubator , cells were re-fed with 500 μl 10% FBS M199 supplemented with 2 mM L-glutamine and penicillin/streptomycin ( 100 μg/ml / 100 I . U . ) and cultured for 24 h . The cells were then fixed with 10% phosphate-buffered formalin for 30 min . Viral antigenic focus detection was accomplished by incubation with rabbit anti-rotaviral hyperimmune serum for 1 h , then alkaline phosphatase ( AP ) -conjugated goat anti rabbit IgG ( Invitrogen ) for 1 h , then the AP substrate BCIP/NBT ( 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium ) ( Sigma ) . Between each step , wells were washed twice with PBS-TA ( PBS containing with 0 . 1% Triton X-100 , 0 . 1% BSA , 0 . 1% sodium azide ) . The positive cells were enumerated and virus titers were expressed as focus forming unit ( FFU ) per gram of tissue . Fecal EW-RV antigen shedding was measured as previously described [54] . Briefly , fecal samples were made to 10% wt/vol suspensions with PBS . Ninety-six-well polystyrene high binding plates ( E&K Scientific ) were coated with guinea pig anti-rotavirus hyperimmune serum . After washing and blocking with 5% BLOTTO ( wt/vol fat free power milk in PBS ) suspended stool samples were added to the plates for overnight incubation at 4°C . The plates were washed and rabbit anti-rotavirus hyperimmune serum was added to the plates . The plates were washed , and horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit immunoglobulin G ( IgG ) antibody ( γ chain specific , Thermo scientific ) was added to the plates . TMB ( 3 , 3’ , 5 , 5’-Tetramethylbenzidine ) substrate ( Kirkegaard & Perry Laboratories ) was used for the color reaction . A serial dilution of a standard RRV stock was used in each plate to control the level of color development . The absorption at A450 nm was measured with an ELISA reader ( Bio-Tek Instruments ) . The fecal viral antigen shedding data were expressed as optical density ( OD ) values . The small intestines were formalin-fixed and paraffin-embedded . Antigen retrieval was performed on deparaffinized 5 micron sections which were then incubated for 5 min with Super Block ( ScyTek #AAA999 ) , and 10 min in 3% H2O2 to block the endogenous peroxidase activity . Sections were then incubated at 4°C overnight with polyclonal goat anti-rotavirus antiserum ( NCDV; Meridian , LS; 1:500 ) or monoclonal rabbit anti-phospho-STAT1 ( Tyr701; 58D6 ) ( Cell Signaling; 1:500 ) . Slides were washed 2 times in PBST ( 0 . 05% Tween-20 in PBS ) , then incubated at room temperature for 30 min with UltraTek anti-Goat biotinylated antibody ( Ready to Use ) ( ScyTek #AGL125 ) or UltraTek anti-rabbit biotinylated antibody ( Ready to Use ) ( ScyTek #ABK125 ) , followed by a 20 min room temperature incubation with UltraTek Streptavidin/HRP ( Ready to Use ) . NovaRED substrate solution ( Vector , SK-4000 ) was used as a substrate . After immunostaining , tissue sections were washed twice in water and counterstained with Mayer’s haematoxylin and Scott’s bluing buffer . Mice were sacrificed and sections of the small intestines ( all tissues ( bulk ) of the small intestine ) were collected and lysed in Trizol ( Life Technolgies ) on ice . Total RNA was extracted following the manufacturer’s instructions and subjected to DNAse digestion before use in qRT-PCR . Synthesis of cDNA and subsequent microfluidics PCR on the Fluidigm platform was done as described earlier [6] . Serial 10-fold dilutions of mouse reference RNA ( Agilent ) were run in duplicate for each PCR run . Relative gene expression in infected and uninfected mouse intestinal samples was derived using the 2dCt method [6] with reference RNA serving as a calibrator and HPRT as housekeeping control . Cell lysates were collected in lysis buffer containing protease and phosphatase inhibitors . Equal amounts of total protein was separated on 7 . 5% SDS-PAGE gels , transferred to Nitrocellulose 0 . 45 μm membrane ( BIO-RAD ) , and subsequently probed with antibody against phosphorylated STAT1 ( pY701; BD #612133 ) and β-actin ( Sigma #A5441 ) . The supernatants of intestinal homogenates of RRV-infected mice were prepared as described above and assayed for IFN-λ protein using commercially available ELISA ( R&D Systems ) , and for IFN-α/β protein by bioassay as previously described [55] . Sigmaplot 12 . 5 or GraphPad Prism software was used for data analysis . Virus levels in tissue were determined by either focus forming unit assay or real time quantitative RT-PCR , and was analyzed with one-way ANOVA and Bonferroni multiple comparison test with the log-transformed viral titers . All animal studies were approved by the NJMS Institutional Animal Care and Use Committee ( Protocol 13009C0316 ) and the VA Palo Alto Health Care System Institutional Animal Care and Use Committee ( Protocol GRH0022/GRH1397 ) and carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . | Two distinct families of interferons ( IFNs ) , type I ( IFN-α/β ) and type III ( IFN-λ ) IFNs , are quickly produced in response to virus infection and engage distinct receptors to invoke shared rapid and broad-spectrum antiviral mechanisms against invading pathogens . However , the relative importance of type I and type III IFNs in protecting different organ systems against specific viruses or distinct strains of an individual virus remains to be fully explored . Here we demonstrated in suckling mice that neither type I nor type III IFNs are effective in blocking intestinal replication of murine rotavirus , rather , viral clearance is dependent upon adaptive immune responses . In contrast , both IFN types cooperate to control intestinal replication and extra-intestinal spread of simian rotavirus in neonatal mice . Unexpectedly , we found that although intestinal epithelial cells ( IECs ) respond to both types of IFNs in neonatal mice , responsiveness of IECs to type I IFNs , but not type III IFNs , is diminished in adult mice . Transcriptional analysis showed that both types of IFN receptors induced overlapping intestinal antiviral responses , which were abrogated only when both receptor types were deleted . Overall , these findings reveal a well-coordinated spatial and temporal regulation of antiviral defenses by type I and type III IFNs in the gastrointestinal tract that varies significantly depending on the viral strain examined . | [
"Abstract",
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"gastrointestinal... | 2016 | Distinct Roles of Type I and Type III Interferons in Intestinal Immunity to Homologous and Heterologous Rotavirus Infections |
We report the results of a joint human-animal health investigation in a Dene community in northern Saskatchewan , where residents harvest wildlife ( including moose , bear , elk , and fish ) , live in close contact with free roaming dogs , and lack access to permanent veterinary services . Fecal analysis of owned and free-roaming dogs over two consecutive years ( N = 92 , 103 ) identified several parasites of public health concern , including Toxocara canis , Diphyllobothrium spp . , Echinococcus/Taenia , Cryptosporidium spp . and Giardia spp . Administration of pyrantel pamoate to a subset of dogs ( N = 122 ) in the community in the first year was followed by reduced shedding of T . canis and other roundworms in the second year , demonstrating the potential utility of canine de-worming as a public health intervention . Using direct agglutination tests with confirmatory indirect fluorescent antibody test , 21% of 47 dogs were sero-positive for exposure to Toxoplasma gondii . Using enzyme-linked immunosorbent assay ( ELISA ) sero-prevalence rates in 201 human volunteers were as follows: Toxoplasma gondii ( 14% ) , Echinococcus granulosus ( 48% ) , Toxocara canis ( 13% ) and Trichinella spp . ( 16% ) . Overall 65% of participants were sero-positive for at least one parasite . A survey administered to volunteers indicated few associations between widely accepted risk factors for parasite exposure and serological status , emphasizing the importance of environmental transmission of these parasites through soil , food , and waterborne routes .
Northern Indigenous peoples have recently been identified as being at high risk for acquiring parasitic zoonoses due to socioeconomic factors and a close relationship with the land [1] . Hunting and fishing are common activities in northern Saskatchewan where consumption of country foods is an integral part of a traditional Dene diet and a very important contribution to food security in regions where commercial foods are often expensive , unavailable , and nutritionally inadequate [2] . Free-roaming dogs continue to play important roles in Indigenous communities as wildlife deterrents , security , companion animals , and occasionally transport [3] . Human exposure to zoonotic parasites might be above average in these communities if free-roaming dogs have access to raw game or fish and subsequently shed infective stages of parasites in areas frequented by people . Other risk factors for exposure to zoonotic parasites include contaminated or inadequately treated drinking water , handling and consumption of locally caught and inadequately cooked game or fish , challenges of waste disposal in remote environments , and/or absence of veterinary services [4]–[6] . Recently , the prevalence of intestinal parasitic infection in dogs was reported to be as high as 71% in northern Saskatchewan [7] . Several genera of zoonotic parasites have been identified in canine populations including Echinococcus/Taenia , Giardia , Cryptosporidium , Toxocara and Diphyllobothrium , for which dogs may serve as sources or sentinels for human exposure [4] , [7] . Few studies have simultaneously sampled people as well as free-roaming dog populations to determine their role as sources or sentinels for human infection with parasitic zoonoses [8] . A number of human sero-prevalence studies have been conducted in northern and predominantly Indigenous regions of Canada; however , none of these have focused on Dene communities in northwestern Canada , which share many of the same socioeconomic and public health concerns as Inuit in Nunavut , and Inuit and Cree in Nunavik and the James Bay region of northern Quebec [9]–[16] . Zoonotic infectious such as echinococcosis and trichinellosis occur more frequently in northern and Indigenous populations; however , incidence rates of other zoonotic parasites are currently unknown for northern Saskatchewan [17] . We conducted research relating to veterinary public health in one Indigenous community in the Keewatin Yatthé ( KY ) health authority over a two year period ( 2010 , 2011 ) . The KY region is located in the northwestern part of Saskatchewan , and is one of three public health regions that encompass northern Saskatchewan [9] . Approximately 10 600 people reside in this area , of which 94% self-identify as Indigenous ( primarily Métis , Dene and Cree ) ; a proportion similar to that seen in James Bay and Nunavik , Quebec . Social determinants of health significantly contribute to health inequities in this population , and include the high cost of food , housing shortages , low income , and high unemployment . This health region has a shorter life expectancy , higher all-cause mortality , and higher rates of chronic and communicable disease ( including diarrheal outbreaks , tuberculosis , hepatitis C and HIV/AIDS ) than the provincial average [9] . In this paper we study canine endoparasitism and human exposure to four parasites of medical concern: Echinococcus granulosus , Trichinella , Toxocara canis and Toxoplasma gondi . Social and behavioural risk factors for exposure to these parasites are also explored .
In 2011 , we visited one community in northern Saskatchewan with an approximate population of 2400 people and , primarily through word of mouth , recruited 201 volunteers over the age of 4 years ( female N = 77; male N = 124 ) . In addition , we sampled dog feces collected from the ground and samples from client-owned dogs brought to a veterinary service clinic in the community in 2010 and 2011 . Approximately 5 mL of whole blood was collected directly into serum-separator tubes ( BD; Franklin Lakes , NJ ) and kept refrigerated . Tubes were centrifuged at 3000 rpm for 10 minutes within 8 hours of collection , and sera transferred to snap-top mini centrifuge tubes . Serum samples were sent to the National Reference Centre for Parasitology ( McGill University , Montreal , QC ) and tested for IgG antibodies against T . gondii ( Diagnostic Automation/Cortez Diagnostics , Inc , Calabasas , CA ) , Trichinella spp . , Toxocara canis and E . granulosus by using an in-house developed IgG and IVD Research ( Carlsbad , CA ) enzyme-linked immunosorbent assay ( ELISA ) . Criteria for interpretation of serology results are provided in Table 1 . Equivocal results were designated as sero-negative . Each participant was also asked to respond to a survey pertaining to risk factors for parasite exposure . Questions addressed pet ownership , feeding practices , barriers to veterinary care , hunting , fishing and personal consumption of country foods . Not all participants completed the surveys to entirety , and some small children were grouped under their parents' surveys . Approximately 300–400 dogs are estimated to reside in this community . We conducted canine fecal collection and analysis in this community during the month of June over two consecutive years ( 2010: N = 92; 2011: N = 103 ) to test the effectiveness of anthelmintic administration as a public health intervention . Fecal samples were obtained by rectal collection of client-owned dogs brought to a mobile veterinary service clinic ( 2010: N = 31; 2011: N = 34 ) , as well as by ground collection throughout the community ( 2010: N = 61; 2011: N = 69 ) as a measure of environmental contamination . All dogs ( N = 122 ) brought to the mobile clinic in 2010 were treated with pyrantel pamoate as per label dose , and owners were given additional medication along with instructions to repeat the treatment after 7–10 days . The ratio of male to female dogs brought to the clinic was approximately one to one , and all intact animals were desexed . Approximately half of the clinic animals were within one year of age . For ground collected feces around the community , collection of fresh fecal samples was prioritized , with older samples ( grey or white ) being rejected . Samples were stored in sealed plastic bags and kept in coolers with ice during the collection period ( 1–2 days ) . Feces were transported to the University of Saskatchewan ( Saskatoon , SK ) and stored at −80 degrees Celsius for five days to inactivate taeniid eggs . A quantitative sucrose centrifugation flotation was used to quantify and identify parasite eggs and cysts from approximately 5 grams wet weight of feces ( modified from [18] ) . Giardia spp . cysts and Cryptosporidium spp . oocysts were identified using a sucrose gradient flotation and a commercial immunofluorescent assay ( Waterborne Inc . ; New Orleans , LA ) on approximately 1 gram wet weight of feces [19] . All participants provided written informed consent and those under the age of 18 provided written consent from a parent or guardian to participate . Individual serology results were mailed back to the participant and/or their primary care physician . The human study was reviewed and approved by the University of Saskatchewan Biomedical Research Ethics Board ( REB 11-07 ) , as well as by the Keewatin Yatthé Health Region and the community leader . The animal fecal and serology studies were reviewed and approved by the University of Saskatchewan Animal Research Ethics Board ( 2009-0126 and 2010-0159 , respectively ) , which adheres to the Canadian Council on Animal Care ( CCAC ) standards . Dog owners provided consent for their animals to be sampled , while consent for ground collection of dog feces was provided by the community leader . Human serology and survey data were entered into a spreadsheet and analysed using logistic regression to identify associations between outcomes ( sero-status ) and risk factors ( SPSS , Chicago , Illinois , USA ) . The strength of association between an outcome and variables was reported as an odds ratio ( OR ) with 95% confidence intervals ( CI ) ( OpenEpi version 2 . 3 . 1 , Atlanta , GA , USA ) . Risk factors were tested for statistical significance in a multivariate model using manual backward elimination . Risk factors were considered confounders if their inclusion or exclusion changed the effect estimate of another risk factor by more than 10% . In the case of correlated risk factors , only one was included in the final model . A chi-square test was used to determine if proportions were significantly different ( p-value<0 . 05 ) .
Of the 77 women and 124 men ( N = 201 ) sampled in the Keewatin Yatthé public health region , 65% had been exposed to at least one of four zoonotic parasites ( Table 2 ) . The participation rate was approximately 8% , however a number of potential volunteers were turned away due to limited phlebotomy supplies . The prevalence of diagnostically relevant titres was as follows: Echinococcus granulosus 47 . 8% ( 96/201 ) , Toxocara canis 13 . 4% ( 27/201 ) , Trichinella 16 . 4% ( 33/201 ) and Toxoplasma gondii 13 . 9% ( 28/201 ) . Of those who were sero-positive , 24% had been exposed to 2 parasites , and 8% had been exposed to 3; no person had been exposed to all 4 zoonoses . Co-exposure occurred most commonly between E . granulosus and Trichinella ( 19/201; 9 . 5% ) , with similar proportions between E . granulosus and the remaining parasites: T . canis ( 17/201; 8 . 5% ) and T . gondii ( 14/201; 7% ) . Analysis of the survey identified several practices that could potentially expose people to zoonotic parasites ( Table 3 ) . Nearly all participants ate locally acquired foods including meat , fish , mushrooms and berries . Popular methods of wild game and fish preparation included drying , smoking or cooking; while raw foods were rarely consumed . Of pet owners , 74% fed raw meat and 70% fed fish to pets on a regular basis . Participants aged 5–17 had higher odds of exposure to T . canis ( OR 3 . 4 95% CI 1 . 2-10 ) than those over the age of seventeen; and feeding pets non-commercial dog food increased the odds of exposure by 15 times ( 95% CI 1 . 8-126 ) . Increased odds of exposure to T . gondii were observed in participants older than fifty ( OR 9 . 4 95% CI 1 . 1-77 ) and those who did not own pets ( OR 3 . 8 95% CI 1 . 3-11 . 3 ) , however gender and hunting/trapping are probable confounders for pet ownership . Examination of canine feces identified five parasite genera of relevant zoonotic potential in this community , including Diphyllobothrium , Toxocara , Echinococcus/Taenia , Cryptosporidium and Giardia . Ground collected fecal samples were observed to have more parasites ( 2010: 51% 31/61: 2011: 35% , 24/69 ) than fecal samples of dogs brought to the clinic ( 2010: 48% , 13/31; 15% , 5/34 ) . Chi-squared analysis indicates that the decrease in overall prevalence of endoparasitism from 2010 ( 48%; 42/92 ) to 2011 ( 28%; 29/103 ) is statistically significant ( p-value 0 . 005 ) ( Table 4 ) . During this time period overall decreases were noted in roundworms ( Toxocara 9% , Toxascaris 5% , Uncinaria 11% ) ; while the prevalence of tapeworms increased ( Taeniid 4% , Diphyllobothrium 13% ) . Examination of client-owned dogs in this region in 2011 demonstrated an exposure prevalence of 21% ( 10/47 ) to T . gondii .
This study shows that the prevalence of exposure to zoonotic parasites for residents of northwestern Saskatchewan is higher than previously reported in other Canadian sero-prevalence studies . As well , dogs residing in this area appear to encounter and be infected by potentially zoonotic parasites at higher levels than dogs residing in Saskatoon ( a provincial urban centre ) [20] . Exposure to T . canis , T . gondii and possibly E . granulosus was observed in both people and dogs , indicating that dogs may act as sources and sentinels for human infections . Wild meat consumption , pet ownership and hunting/trapping are generally considered to increase the risk of exposure to zoonotic parasites , however our analysis indicated that there might be a slight overall protective effect . This demonstrates the complexity of parasite transmission routes and the possibility of protective immunity and/or traditional knowledge regarding harvesting and preparation of wild foods . Echinococcus granulosus is a cyclophyllid cestode with a worldwide distribution , causing serious veterinary , medical and economic concerns for highly endemic regions [21] . Human infection with E . granulosus causes hydatid disease , or echinococcocis , which is generally characterized as the formation of larval cysts in the liver and lungs . The average annual incidence rate of hydatid disease in Canada is 0 . 72 cases per million people , and is higher in women than men ( RR 1 . 92 , 95% CI 1 . 29-2 . 87 ) and north of the 55th parallel ( RR 4 . 88 , 95% CI 2 . 52-9 . 44 ) [17] . Hospital records in both Canada and the United States show Indigenous people to be at higher risk of infection [22] , [23] . In another recent study conducted in a Saskatchewan Indigenous community , 11% of 103 people were sero-positive for E . granulosus , and at least two cases of hydatid disease were identified [8; S . Skinner , unpubl . data ) . The sero-prevalence of 48% to E . granulosus in the current study is substantially higher than the 0–4% reported in other Indigenous communities of similar northern latitude , analysed using the same test and by the same laboratory , the National Reference Centre for Parasitology [10]–[16] . We are not aware of any clinical cases in this community at the current time; however , there is no formal surveillance for this parasite in Canada . There is a strong possibility that the unexpected level of exposure is due to cross-reactions with other helminths . Diphyllobothriasis cases are relatively common in this region [J . Irvine , unpubl . data] , and other possibilities include the liver fluke , Metorchis conjunctus , and various Taenia species . We know that Diphyllobothrium is present in dogs in this community ( Table 4 ) , and Metorchis has historically been reported in dogs , wolves and people in SK [5] , [24]–[26] . Trichinella nematodes have long been associated with consumption of undercooked pork; however , livestock production practices have virtually removed this parasite from the domestic Canadian swine herd [27] , [28] . North American wildlife may be infected with one of five zoonotic genotypes of Trichinella , and consumption of these animals has been the primary cause of Canadian trichinellosis outbreaks since the 1970s [27]–[32] . In northern Saskatchewan , exposure is most commonly attributed to the consumption of wild bear meat; while in Inuit regions of Nunavut and Nunavik , exposure is associated with consumption of marine mammals such as walrus [30]–[31] , [33]–[36] . The national annual incidence rate of trichinellosis is only 0 . 09 cases per million people , however rates are significantly higher in Nunavut and Nunavik ( 42 cases per million people ) [17] . In Canadian northern and Indigenous communities the sero-prevalence for Trichinella in people ranges between 0–5 . 5% , which is far lower than our reported exposure prevalence of 16 . 4% . Antibodies to this parasite can persist up to 19 years , making it difficult to detect recent changes in exposure frequency [37] . Toxocara canis is an ascarid nematode that cycles primarily among canids , and commonly infects domestic dogs in Canada and around the world . People may become exposed through accidental ingestion of eggs shed in dog feces , or by ingestion of tissue cysts in the undercooked meat of paratenic hosts . Toxocariasis , characterized by visceral or ocular larval migrans , is not commonly reported in Canada , but may cause serious health effects . In our study youth were more likely to be exposed than adults , consistent with observations that children are at highest risk for infection when they play in sand or soil contaminated by dog feces , or due to pica [38]–[40] . We found that dog ownership was not a risk factor for exposure to T . canis , similar to one other study in Canada [41] , thus supporting the importance of environmental ( versus direct ) transmission of this parasite . Feeding non-commercial diets to family dogs significantly increased the odds of human exposure to T . canis . This may be due to increased transmission to dogs via the paratenic host route , followed by human contact with eggs shed in dog feces . Alternatively , feeding non-commercial pet diets may correlate with other variables , such as poverty and occupational exposures to soil , that put people at risk of exposure [42]–[43] . Sero-prevalence for Toxocara was between 0 . 7–4% in recent studies in Inuit and Cree communities in northern Canada [11]–[16] . Our reported prevalence of 13 . 4% is therefore much higher than that observed in Canadian communities north of the 60th parallel , consistent with observations of restricted survival of T . canis eggs at colder temperatures [38] , [44]–[46] . It is on par with the 13 . 9% reported in the general population of the United States between 1988 and 1994 , although this was dominated by samples from the southern USA where this parasite may have increased levels of transmission [42] . Reducing risk of exposure to this parasite could focus on regular deworming of dogs , timely disposal of feces ( the eggs are not immediately infective ) , and preventing dogs from defecating in areas where children play . Toxoplasma gondii has a global distribution , and is one of the most important parasites in the Canadian North [1] . This protozoan has a complex lifecycle involving felids as definitive hosts and a wide variety of vertebrate species as intermediate hosts . In our study population , routes for dog exposure include feeding raw meat to dogs , ingestion of garbage and wildlife . As well , sero-positive status in dogs is associated with age , diet , hospitalization , and health status; a sample of young , stray dogs had the lowest level of sero-positivity [47]–[48] . We observed a lower level of exposure to T . gondii in our population ( 21% ) than dogs tested in Alberta , the Northwest Territories and Ontario ( 33–63% ) [4] , [48] , which may be due to the relatively young population sampled . Dogs are not known to spread T . gondii to people , however , our finding suggest that people in the community may be at risk due to share exposure routes . People become infected by ingesting or handling raw meat , ingesting contaminated drinking water , handling infective cat feces , or by congenital transmission , blood transfusion or organ transplant [49] . We report a sero-prevalence of 13 . 9% , which is comparable to the NHANES estimate of 10 . 8% in the United States [50] , and generally higher than that reported elsewhere in Canada using the same test in the last 6 years ( 5–10% ) . Inuit in Nunavik , Quebec have one of the highest sero-prevalences reported ( 30–60% ) , and are thought to have a unique constellation of risk factors ( Table 2 ) . Risk factors include gender ( female>male ) , drinking water sources , regular disinfection of water reservoirs , and limited education [10]–[16] , [51] . Exposure to T . gondii in the Keewatin Yatthé region was statistically higher with age ( >50 years ) , and with those who did not own a pet; however , confounding variables might nullify the effect of pet ownership on sero-status . Saskatchewan currently has the highest incidence rate of Human Immunodeficiency Virus ( HIV ) in Canada , at double the national average . Indigenous patients are disproportionally affected , and represented 79% of HIV/AIDS cases in 2009 [52] , [53] . HIV/AIDS is a serious risk factor for development of clinical toxoplasmosis . Mortality attributed to toxoplasmosis in AIDS cases in Europe and the United States is estimated to be 30% and 10% , respectively [49] , [54] . The higher proportion of immune-compromised individuals in northern Saskatchewan combined with limited veterinary services , frequent contact with wildlife , and lifting of previously restrictive climate conditions , may lead to emergence of previously uncommon zoonotic pathogens as public health concerns ( T . gondii and Cryptosporidium ) . The prevalence of endoparasitism in client-owned dogs from this community was similar to levels previously found in remote areas of Saskatchewan [5] , [55] . Ground-collected fecal samples did not represent the true parasite prevalence in this community as multiple samples may have originated from the same animal . However this method is an effective tool for estimating the overall level of environmental contamination as well as for identifying local parasites of zoonotic concern; in this case T . canis , Taeniid , Diphyllobothrium , Giardia , and Cryptosporidium . The voluntary nature of human and canine recruitment was another limitation of this study; however , we considered this strategy as crucial in building trust with the community . The purpose of blood testing was not revealed during recruitment , and only 17% of participants were aware that pathogens could move between animals and people . Thus , people with concerns of parasite exposure were not more likely to participate . Sampling of client-owned animals was biased towards pets with owners who considered veterinary services important . However , we considered this effect minimal because all dog owners permitted blood and/or fecal collection , all veterinary services were cost-free , and local volunteers rounded up stray dogs . Shedding of roundworm eggs ( T . canis , Toxascaris and Uncinaria ) decreased in 2011 , following administration of pyrantel pamoate to dogs brought to the mobile veterinary service unit in 2010 . This could reflect drug effectiveness , decreased transplacental and transmammary transmission of T . canis due to spaying female dogs , and/or the effect of having fewer puppies , which are the primary source of environmental contamination . Alternately , the observed concomitant decreases in protozoa , which are not affected by pyrantel pamoate , suggest that changes in parasite prevalence may result from factors such as annual climate variations and altered animal husbandry practices . Whatever the cause , the overall decrease of parasitism in dogs bought to the clinic and in environmental contamination is a benefit to public health; however , the increased prevalence of cestode eggs demonstrates the additional need for cestocidal treatment to reduce risks to human health . Finally , this study reinforces that surveillance and management of zoonoses in remote areas requires a One Health approach incorporating both veterinary and public health interventions , tailored to concerns at the local level . | Parasites are ubiquitous , and while some parasitize only one host , others are capable of crossing species barriers . Zoonotic parasites move between animals and people , and in some cases cause significant veterinary , medical and/or public health problems . Such parasites may be more prevalent in areas where veterinary and medical services are scarce , and especially if sanitation infrastructure is suboptimal . Additional risk factors include reliance on country foods , proximity to pets that come in contact with wildlife , and eating undercooked or raw fish and game . We visited one northern Indigenous community over two consecutive years to determine the prevalence of internal parasites in dogs , as well as to demonstrate the effect of selective deworming on reducing environmental contamination by zoonotic parasites . In addition , we collected blood samples and administered surveys to human volunteers in order to explore the relationship between exposure to four zoonotic parasites and several widely accepted risk factors for exposure ( e . g . pet ownership ) . Our findings indicate that levels of parasite exposure in this community were higher than similar studies conducted in other Canadian Indigenous communities . Public health interventions that utilize a one health strategy by integrating medical , veterinary and environmental expertise may be the most effective approach in reducing human and animal exposure to parasites in this community . | [
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"veter... | 2013 | Parasitic Zoonoses: One Health Surveillance in Northern Saskatchewan |
Imatinib mesylate induces complete cytogenetic responses in patients with chronic myeloid leukemia ( CML ) , yet many patients have detectable BCR-ABL transcripts in peripheral blood even after prolonged therapy . Bone marrow studies have shown that this residual disease resides within the stem cell compartment . Quiescence of leukemic stem cells has been suggested as a mechanism conferring insensitivity to imatinib , and exposure to the Granulocyte-Colony Stimulating Factor ( G-CSF ) , together with imatinib , has led to a significant reduction in leukemic stem cells in vitro . In this paper , we design a novel mathematical model of stem cell quiescence to investigate the treatment response to imatinib and G-CSF . We find that the addition of G-CSF to an imatinib treatment protocol leads to observable effects only if the majority of leukemic stem cells are quiescent; otherwise it does not modulate the leukemic cell burden . The latter scenario is in agreement with clinical findings in a pilot study administering imatinib continuously or intermittently , with or without G-CSF ( GIMI trial ) . Furthermore , our model predicts that the addition of G-CSF leads to a higher risk of resistance since it increases the production of cycling leukemic stem cells . Although the pilot study did not include enough patients to draw any conclusion with statistical significance , there were more cases of progression in the experimental arms as compared to continuous imatinib . Our results suggest that the additional use of G-CSF may be detrimental to patients in the clinic .
The existence of cancer stem cells , a rare subpopulation of cancer cells responsible for tumor initiation and maintenance , was first postulated in the 1960s [1] . In leukemia in particular , increasing evidence suggests that leukemic stem cells are the only cells within the tumor capable of propagating the disease [2]–[6] . Leukemic stem cells share many properties – such as self-renewal , pluripotency , and quiescence – with tissue stem cells , and they appear to remain untouched by both conventional chemotherapy and targeted drugs [6] . Since the repopulating capabilities of leukemic stem cells necessitate their eradication for a cure of the disease , the development of new therapeutic approaches targeting leukemic stem cells would have a profound impact on cancer management . Chronic myeloid leukemia ( CML ) is associated with the Philadelphia chromosome , which results from a reciprocal translocation between chromosomes 9 and 22 generating the BCR-ABL fusion oncogene [7] , [8] . The ABL kinase inhibitor imatinib greatly improves outcome in CML patients [9]; however , some evidence suggests that it cannot eradicate the disease since it preferentially targets progenitor cells while being incapable of depleting leukemic stem cells [10] . Surviving leukemic stem cells are a potential source of relapse , as demonstrated by the dynamics of leukemic cells in patients who discontinue imatinib after a prolonged administration of therapy [11]–[17] . Several mechanisms of leukemic stem cell insensitivity to ABL kinase inhibitors have been suggested [18]; those mechanisms include quiescence of leukemic stem cells , drug export from the cytoplasm , overexpression of BCR-ABL in stem cells as compared to differentiated cells , and a lack of immune responses against leukemic stem cells . Under normal circumstances , a fraction of hematopoietic stem cells is quiescent [19]–[24] . Cycling and quiescent hematopoietic stem cells display major functional differences , mostly reflected in their homing and mobilization abilities [25]–[29] . Quiescent stem cells are mobilized by Granulocyte-Colony Stimulating Factor ( G-CSF ) and other agents and show preferential homing to the bone marrow as compared to dividing hematopoietic stem cells [29] . In contrast , CML stem cells are constitutively present in the circulation , but also contain a subpopulation of quiescent cells [30] . This quiescent subpopulation has been shown to be insensitive to imatinib therapy in vitro and might therefore represent a therapeutically relevant subpopulation of cancer cells [31] . However , the extent of quiescence of CML stem cells and the in vivo response of such cells to imatinib have not yet been established . In this paper , we present a novel mathematical model to investigate the response of cycling and quiescent leukemic stem cells to treatment with imatinib alone or imatinib combined with G-CSF . We use clinical data of a Phase II trial administering imatinib and G-CSF and study the effects of various treatment strategies on the leukemic stem cell pool [32] . This study is part of a growing literature of theoretical approaches to CML [16] , [33]–[39] .
Consider a differentiation hierarchy of hematopoietic cells . On top of the hierarchy , there are cycling and quiescent stem cells . Quiescent stem cells can become cycling stem cells , while the latter can become quiescent stem cells . Cycling stem cells give rise to progenitor cells , which produce differentiated cells , which in turn produce terminally differentiated cells ( Fig . 1A ) . This differentiation hierarchy applies to normal and leukemic cells [40] . The abundances of normal cycling and quiescent stem cells , progenitors , differentiated , and terminally differentiated cells are denoted by , , , , and ; the respective abundances of leukemic cells are denoted by , , , , and . Normal and leukemic cycling stem cells divide at rates and , respectively . The rates at which cycling stem cells produce quiescent stem cells and vice versa are denoted by and . The rate constants for the production of progenitors , differentiated cells and terminally differentiated cells are given by a , b , and c . Cycling stem cells die at rate , progenitors at rate , differentiated cells at rate , and terminally differentiated cells at rate per day . Here we assume that cells at all levels may reproduce symmetrically and/or asymmetrically; the limited replication potential of more differentiated cell types can be considered as part of the differentiation rates . The BCR-ABL oncogene is present in all leukemic cells and leads to a slow clonal expansion of cycling leukemic stem cells; the latter effect is assumed since otherwise , leukemic stem cells could not make up a significant fraction of the stem cell compartment at diagnosis [30] , [41] . Furthermore , BCR-ABL increases the rate at which leukemic progenitors are produced . The basic model is displayed in Table 1 . Density dependence is achieved by the functions and ; these functions ensure that normal and leukemic stem cells remain at a constant abundance once the system has reached a steady state . To achieve realistic equilibrium conditions , we set the constants and , where and are the equilibrium abundances of normal and leukemic stem cells , respectively . In the absence of leukemic cells , the differentiation hierarchy of normal cells is in equilibrium , i . e . the abundances and proportions of different cell types do not change . Once the first leukemic stem cell arises , it produces a differentiation hierarchy of leukemic cells . The BCR-ABL oncogene is assumed to increase the rate at which progenitors and differentiated cells are being produced , and . We assume that diagnosis occurs and treatment initiates once the leukemic cell burden reaches a threshold of cells . Imatinib therapy counteracts the effects of BCR-ABL by reducing the differentiation rates to and , and possibly reducing the growth rate of cycling leukemic stem cells to . These effects result in distinct phases of exponential decline: ( i ) the first phase , with a slope of , represents the depletion of terminally differentiated leukemic cells; since these cells have an average lifespan of about a day , their depletion cannot be observed in clinical data ( IRIS trial ) for reasons of low resolution [16]; ( ii ) the second phase , with a slope of , corresponds to the decline of differentiated leukemic cells; this slope can be observed in the IRIS data and has an average of per day , suggesting that differentiated cells live approximately 20 days during therapy; ( iii ) the third phase , with a slope of , signifies the depletion of leukemic progenitors; this slope is about , representing a lifespan of 125 days for progenitors during treatment; and ( iv ) the fourth phase reflects the effect of imatinib on cycling stem cells . If imatinib is unable to deplete cycling leukemic stem cells ( ) , then the leukemic cell burden increases during the fourth phase , while it decreases if imatinib is capable of depleting cycling leukemic stem cells . The slope of the fourth phase of decline represents the rate of decline of leukemic stem cells and is calculated in the following . Denote by the ratio of cycling to total leukemic stem cells; the asymptotic value of this ratio during treatment is given by . Note that if , then is not the same as the frequency of cycling stem cells at equilibrium ( at which the system is initiated ) , since in that case imatinib therapy decreases the growth rate of cycling leukemic stem cells and disturbs the equilibrium of the system . To calculate the fourth slope , we isolate the two-dimensional system for and consider the differential equation in terms of the ratio . As treatment is administered and time increases , approaches , which is given bywhere is the asymptotic value of for large times . The equilibrium carrying capacity of the healthy cycling stem cells is given by . The slope of the fourth phase of decline of terminally differentiated cancer cells is then given by . In Fig . 1B , we show the dynamics of cells observed in peripheral blood if cycling leukemic stem cells are slowly depleted by imatinib ( lower line ) and if cycling leukemic stem cells continue to increase during therapy ( upper line ) .
Since the extent of quiescence among hematopoietic stem cells is the subject of controversy [21]–[24] , we test the sensitivity of the model's predictions to changes in parameters . The parameter specifies the frequency of normal cycling stem cells if the system is in equilibrium; if and , then also denotes the equilibrium fraction of cycling leukemic stem cells . The effects of varying are shown in Fig . 2A where we plot the abundances of various cell types prior to and during imatinib therapy . Here we assume that diagnosis and treatment occur once the leukemic cell burden reaches a threshold of cells . We may also investigate the case in which the proportion of cycling stem cells to total stem cells is different for leukemic ( ) and normal ( ) cells . In Figure S1 we investigate the dynamics of the system during imatinib therapy and during concurrent imatinib and G-CSF treatment when . While changes in alter the abundance of cycling and quiescent stem cells , they do not modify the abundance of terminally differentiated leukemic cells by a significant amount . A choice of specifies the relationship between the rate at which cycling stem cells produce quiescent stem cells , and ( for healthy and leukemic cells , respectively ) , and the rate at which quiescent stem cells produce cycling stem cells , and . The effects of varying these turnover rates while keeping fixed prior to and during imatinib therapy are shown in Fig . 2B . The abundance of terminally differentiated cells is even less sensitive to such changes than to variation of . Therefore , despite insufficient knowledge about the extent and regulation of quiescence , a sensitivity analysis of the system determines that the model's predictions are very robust with regard to changes in parameters that determine the abundance and turnover of quiescent stem cells . In light of in vitro data supporting such a strategy [42] , a randomized , phase II pilot clinical trial was commenced in 2004 to establish the safety of pulsed imatinib in combination with G-CSF . Imatinib interruption ( for 1 week every 4 weeks ) was deemed to be necessary based upon in vitro data suggesting an anti-proliferative effect of this strategy [31] . The trial was performed as a three arm , multi-center study which enrolled 15 patients in each arm: continuous imatinib ( cIM ) vs . pulsed IM ( pIM ) vs . pIM plus G-CSF ( pIM-G ) given three times weekly during the period of dose interruption . Results of the study have recently been published [32] . Monthly Q-PCR monitoring was performed over the 12 month study period and the results are summarized in Figs . 3A and 3B ( unpublished observations ) . Although not powered to detect small differences between the treatment arms over time , no significant changes in BCR-ABL/ABL ratios ( % ) were noted over the study period . Furthermore , 6 patients in the experimental arms exhibited disease progression or loss of response , as compared to a single patient in the control arm ( cIM ) . While this was not in itself statistically significant , the majority of these patients ( 5/7 ) had disease control re-established by reintroduction of daily TKI therapy suggesting that the experimental approach had contributed to the loss of response . Let us now use our mathematical model to predict the outcome of the clinical trial described above [32] . First , we investigate the dynamics of the system during combination therapy with imatinib and G-CSF . G-CSF functions by mobilizing both healthy and leukemic quiescent stem cells [29] . In the context of our model , this effect corresponds to increasing and , to an even larger extent , . More specifically , we assume that G-CSF has a stronger effect on leukemic stem cells than on healthy stem cells [42] . The treatment response to imatinib alone and imatinib in combination with G-CSF is shown in Fig . 4A , where the abundances of each cell type are plotted as a function of time elapsed since the start of therapy . We assume that imatinib has no effect on the normal hematopoietic system ( Fig . S2 ) and study two scenarios: ( i ) the majority of stem cells ( both normal and leukemic ) are initially quiescent [21] , [22] , , and ( ii ) the majority of stem cells are cycling [23] , [24] , . In the latter case , the addition of G-CSF does not change the dynamics of terminally differentiated cells during therapy at all . If most stem cells are quiescent , however , then the addition of G-CSF leads to an increase in cycling leukemic stem cells since there is an enhanced production of cycling stem cells from the quiescent pool; this effect translates into higher levels of terminally differentiated cells during therapy . Additionally , administration of G-CSF leads to an enhanced production of normal cycling stem cells from the quiescent pool , albeit to a lesser extent than in the leukemic system . Next , we investigate the dynamics of the system during pulsed imatinib therapy with and without G-CSF . In Fig . 4B , we compare the effects of different treatment options on the abundance of terminally differentiated cells . We perform the comparison for the two cases in which the initial fraction of cycling stem cells is 10% and 90% , respectively ( and 0 . 9 ) . The outcome of such treatment choices is similar to the continuous treatment scenarios shown in Fig . 4A: if , then the effects of adding G-CSF are negligible for the abundance of terminally differentiated leukemic cells; if , however , then the level of terminally differentiated leukemic cells is higher when G-CSF is administered than when imatinib is used alone . So far we have found that depending on the abundance of quiescent stem cells , the addition of G-CSF to the imatinib treatment protocol either increases or does not affect the level of terminally differentiated leukemic cells in the first 1500 days of therapy . Note that these results are dependent upon our assumption that imatinib treatment leads to a decline of leukemic stem cells . We next investigate the long-term effects of continuous imatinib treatment with and without G-CSF ( Fig . 5A ) . The addition of G-CSF is ultimately beneficial for the patient since it decreases the leukemic cell burden at a faster rate than imatinib therapy alone . However , for an intermediately long treatment horizon , the additional administration of G-CSF increases the leukemic cell burden since G-CSF enhances the production of cycling leukemic stem cells from their quiescent counterparts . This increased frequency of cycling stem cells allows for an enhanced effect of imatinib , leading to an exhaustion of both cycling and quiescent stem cells . The evolution of point mutations in the BCR-ABL kinase domain conferring resistance represents a limitation for the usefulness of imatinib therapy [43]–[47] . We sought to investigate whether the addition of G-CSF to imatinib treatment modulates the risk of resistance in treated patients . Denote by the number of cell divisions of leukemic stem cells until time t; this quantity can be calculated as , which depends on the effects of imatinib ( and G-CSF ) therapy . The probability that a point mutation conferring resistance arises per stem cell division is denoted by . Here we assume that only leukemic stem cells can accumulate resistance mutations since they are the only cells that have self-renewal potential; if a resistance mutation arises in a progenitor or differentiated cell , then this resistant cell cannot produce a self-sufficient clone and will eventually die out . We also only consider resistance arising due to BCR-ABL kinase domain mutations and exclude other reasons for the emergence of resistance from our analysis . Then the probability that at least one resistant leukemic stem cell has arisen in a patient before time t is given by . Thus a larger results in a higher risk of developing resistance by time . Note that this probability includes only newly emerging resistance and does not capture the risk of resistance due to mutations present prior to the initiation of therapy; since we are interested only in the effects of different treatment protocols on the risk of resistance and we assume complete resistance , we can neglect the latter type of mutations . In Fig . 5B we show the number of leukemic stem cell divisions , , as a function of time for treatment with imatinib alone or in combination with G-CSF . There exists a crossover point in time , prior to which patients have a lower probability of developing resistance during imatinib therapy . Beyond the crossover point , combination therapy leads to a lower probability of resistance . As a numerical example , consider the risk of resistance for patients 2500 days ( ∼6 . 8 yrs ) after the initiation of therapy . If resistance can emerge due to any one out of 90 different point mutations in the BCR-ABL kinase domain and the baseline mutation rate is per base per cell division [48] , then the probability that a resistance mutation arises per cell division is . The model predicts that 89 out of every 100 patients would develop resistance if treated with imatinib alone , whereas 96 out of every 100 patients would develop resistance if treated with imatinib and G-CSF . If we assume a baseline point mutation rate of per cell division , then 20 out of 100 patients on imatinib and 27 out of 100 patients on imatinib and G-CSF would develop resistance during the first 2500 days of therapy . Our model predicts that patients on combination therapy who do not develop resistance will benefit from the later effects of G-CSF that reduce the levels of leukemic cells in the blood; however , they will remain at prolonged elevated risk of resistance in comparison to patients on imatinib therapy until the crossover point . Similarly to the risk of resistance , we can also consider the risk of progression to accelerated phase and blast crisis during different treatment options . Using the model designed in [38] , we predict that the rate of progression is lowest during treatment strategies that most effectively deplete progenitor cells if blast crisis stem cells arise from the progenitor pool [49] . Therefore , a treatment strategy that increases cycling leukemic stem cells and progenitors even transiently ( such as G-CSF therapy ) will increase the initial risk of progression to blast crisis . If G-CSF administration additionally protects progenitor cells from being inhibited by imatinib therapy [50] , then the risk of progression is even more pronounced .
In this paper , we have presented a novel mathematical model of the hematopoietic system of CML patients during therapy . We have used this model to investigate the response to treatment with imatinib and/or the growth factor G-CSF . We have studied four different treatment strategies: ( i ) continuous administration of imatinib alone; ( ii ) continuous administration of combination therapy with imatinib and G-CSF; ( iii ) pulsed imatinib followed by a treatment break; and ( iv ) pulsed imatinib followed by G-CSF therapy . The capability of G-CSF therapy to modulate the leukemic cell burden depends on the extent of quiescence among leukemic stem cells: if the majority of leukemic stem cells are quiescent , then the addition of G-CSF to imatinib therapy temporarily increases the leukemic cell burden in peripheral blood . Eventually this treatment strategy leads to a more rapid decline of leukemic cells . However , this effect is only observed if imatinib is capable of depleting cycling leukemic stem cells . If cycling stem cells are insensitive to imatinib in vivo , then the addition of G-CSF will only increase the leukemic cell burden . Furthermore , we have not considered a protective effect of G-CSF on leukemic stem cells [50] since cytokines seem to protect only bulk CD34+ cells from tyrosine kinase inhibition and about 10% of primitive CML stem cells survive a 12 day exposure to dasatinib in the absence of any added cytokines [51] . However , the inclusion of such an effect would make our conclusion even stronger that the addition of G-CSF to tyrosine kinase inhibitor ( TKI ) therapy may not be beneficial in the clinic . The effects of therapy with continuous and pulsed imatinib as well as pulsed imatinib with G-CSF have been investigated in a pilot study in 2009 [32] . Forty-five patients were randomized between three treatment arms: continuous imatinib , pulsed imatinib , and pulsed imatinib with G-CSF . Since the patients recruited to participate in this pilot study were not newly diagnosed but already pre-treated with imatinib , their cell counts had already reached the fourth phase in the dynamics ( see Fig . 1B ) . Our mathematical model predicts differences in the leukemic cell burden between patients on imatinib therapy with and without G-CSF only if the majority of leukemic stem cells are quiescent ( Fig . 4 ) . If most leukemic stem cells are cycling , then the addition of G-CSF to imatinib therapy is expected to have no appreciable effect on the BCR-ABL RQ-PCR levels . This situation was observed in the pilot study [32] , suggesting that the majority of leukemic stem cells are cycling . This conclusion , however , can only be drawn if imatinib is capable of inhibiting cycling leukemic stem cells . Lastly , we have determined the risk of resistance arising during imatinib therapy with and without G-CSF . While treatment with imatinib alone eventually leads to a higher risk of resistance , combination therapy with imatinib and G-CSF initially confers a larger probability of acquired resistance . A similar conclusion can be drawn regarding the risk of progression to accelerated phase and blast crisis . Since the trial was designed to investigate the safety of this treatment protocol and was not powered for efficacy , the conclusions about response and disease progression should not be over-interpreted . Although the PCR values over time were not significantly different between the three treatment arms , the continuous imatinib arm tended downwards during the duration of the trial , while the other two arms did not show any increases or decreases in leukemic cell burden ( Fig . 3B ) . This observation is expected from the model since discontinuous administration of imatinib should show a smaller effect on the disease burden than continuous administration if imatinib therapy is capable of inhibiting leukemic stem cells . Also , 6 of 7 cases showing disease progression were in the pulsed treatment arms; this effect is also in concordance with our model's prediction since the risk of resistance is directly correlated with the number of leukemic stem cells which , if sensitive to imatinib , are less abundant in patients receiving continuous therapy than in those receiving pulsed doses . Our findings regarding the risk of resistance and progression together with the absence of a clinical response with the addition of G-CSF suggest that this treatment option may not be beneficial for CML patients in the clinic . | Imatinib mesylate ( Gleevec ) is currently the standard treatment for chronic myeloid leukemia ( CML ) and elicits a large reduction in leukemic cell burden in most patients . However , strong evidence suggests that imatinib does not cure the disease; approximately 20% of patients relapse within three years , and discontinuation of imatinib therapy often leads to a rebound of the leukemic cell burden . Laboratory studies have suggested that there exists a subpopulation of “quiescent” leukemia cells ( i . e . , cells that do not divide ) that may be insensitive to imatinib treatment . It has been postulated that the disease outcome may be improved by administering imatinib in conjunction with the Granulocyte-Colony Stimulating Factor ( G-CSF ) , a growth factor which “wakes up” the quiescent stem cells and sensitizes them to imatinib . In this study , we design a novel mathematical model of stem cell quiescence to investigate the treatment response to imatinib and G-CSF . We find that adding G-CSF to an imatinib treatment protocol leads to observable effects only if the majority of leukemic stem cells are quiescent . Our model also predicts that adding G-CSF leads to a higher risk of resistance , since it increases the number of leukemic stem cell divisions and thus the probability of acquiring a resistance mutation . | [
"Abstract",
"Introduction",
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"Discussion"
] | [
"computational",
"biology/evolutionary",
"modeling",
"mathematics",
"oncology/hematological",
"malignancies",
"hematology/myeloproliferative",
"disorders,",
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"chronic",
"myeloid",
"leukemia"
] | 2009 | Eradication of Chronic Myeloid Leukemia Stem Cells: A Novel Mathematical Model Predicts No Therapeutic Benefit of Adding G-CSF to Imatinib |
Filoviruses , including Marburg virus ( MARV ) and Ebola virus ( EBOV ) , cause fatal hemorrhagic fever in humans and non-human primates . All filoviruses encode a unique multi-functional protein termed VP35 . The C-terminal double-stranded ( ds ) RNA-binding domain ( RBD ) of VP35 has been implicated in interferon antagonism and immune evasion . Crystal structures of the VP35 RBD from two ebolaviruses have previously demonstrated that the viral protein caps the ends of dsRNA . However , it is not yet understood how the expanses of dsRNA backbone , between the ends , are masked from immune surveillance during filovirus infection . Here , we report the crystal structure of MARV VP35 RBD bound to dsRNA . In the crystal structure , molecules of dsRNA stack end-to-end to form a pseudo-continuous oligonucleotide . This oligonucleotide is continuously and completely coated along its sugar-phosphate backbone by the MARV VP35 RBD . Analysis of dsRNA binding by dot-blot and isothermal titration calorimetry reveals that multiple copies of MARV VP35 RBD can indeed bind the dsRNA sugar-phosphate backbone in a cooperative manner in solution . Further , MARV VP35 RBD can also cap the ends of the dsRNA in solution , although this arrangement was not captured in crystals . Together , these studies suggest that MARV VP35 can both coat the backbone and cap the ends , and that for MARV , coating of the dsRNA backbone may be an essential mechanism by which dsRNA is masked from backbone-sensing immune surveillance molecules .
Marburg virus ( MARV ) is an enveloped virus that belongs to the family Filoviridae and has a non-segmented , single-stranded , negative-sense RNA genome . Within filoviridae are genus marburgvirus which incudes two viruses , Marburg virus ( MARV ) and Ravn virus ( RAVV ) , and genus ebolavirus which includes five viruses , Ebola virus ( EBOV , formerly known as Zaire ebolavirus ) , Reston virus ( RESTV , formerly known as Reston ebolavirus ) , Sudan virus , Taï Forest virus and Bundibugyo virus [1] . Filoviruses cause a severe viral hemorrhagic fever ( VHF ) in humans and non-human primates [2]–[4] . Large outbreaks of Marburg VHF have occurred in recent years in Angola and Republic of Congo , with case fatality rates close to 90% in Angola [4] . In general , outbreaks of the pathogenic filoviruses occur with 20–90% lethality , depending on the viral species , and likely , on the strength of host innate immune responses against the invading pathogen [5]–[7] . Interestingly , RESTV is non-pathogenic to humans , but highly lethal to non-human primates , and has recently been discovered among herds of domesticated swine in the Phillipines [8] . The World Health Organization has classified the filoviruses as Risk Group 4 pathogens and the development of protective vaccines or therapeutics against these viruses is a high priority . Double-stranded RNA ( dsRNA ) is a unique product of viral infection and a key pathogen-associated molecular pattern ( PAMP ) . Detection of dsRNA by host immune pattern recognition receptors activates signaling cascades that lead to production of interferons ( IFNs ) . Survival from a filovirus infection may be determined , at least in part , by the strength of such innate immune responses mounted early in infection [5] , [9] . In response , multiple families of viruses , including filoviruses , have evolved different strategies to counteract recognition of dsRNA [10]–[14] . Filoviruses encode a viral protein termed VP35 which is important for nucleocapsid assembly , replication and transcription and also plays a critical role in host immunosuppression [15] . VP35 binds to and likely masks viral dsRNA from host factors , which leads to suppression of RNA silencing , inhibition of phosphorylation/activation of interferon regulatory factor 3 ( IRF-3 ) and antagonism of the type I IFN response [16]–[20] . EBOV VP35 has a flexible N-terminal region ( amino acids ∼1–90 ) , a central coiled-coil oligomerization domain ( ∼91–130 ) , a flexible linker region ( ∼131–210 ) and a C-terminal dsRNA-binding domain ( ∼211–340 ) [21] , [22] . In EBOV , VP35 contains an 11-residue N-terminal extension . As a result , equivalent positions of the C-terminal dsRNA-binding domain ( RBD ) of EBOV are numbered 11 higher than the corresponding amino acids in RESTV and MARV . For example , Arg 312 in EBOV is equivalent to Arg 301 in either RESTV or MARV . Crystal structures of the VP35 RBD are available for EBOV and RESTV . These structures reveal that the RBD contains two subdomains , one of which is α-helical and the other is primarily β-sheet [22]–[24] . Each of these subdomains contains a basic patch that is conserved across the filovirus family . The “first basic patch” is contained in the α-helical subdomain , while the “central basic patch” is contained in the β-sheet subdomain [15] . A 170-residue version of the RESTV VP35 RBD was crystallized in complex with an 18-base pair oligomer [22] . In this asymmetric unit , there are four copies of the VP35 RBD bound to each oligomer , with two RBDs bound to each end of the dsRNA . The RESTV complex confers an overall appearance of a barbell with two copies of the VP35 RBD at each end and bare dsRNA in between . A ∼125 residue version of the EBOV VP35 RBD was crystallized in complex with a shorter , 8-base pair dsRNA oligomer and , in the crystallographic asymmetric unit , two copies of VP35 bind each end of the oligomer [10] . The shorter length of the crystallized dsRNA confers an overall appearance of a square of VP35s bound around central nucleic acids . The additional residues in the RESTV structure constitute a linker between the N-terminus of the RBD and the putative coiled-coil domain of VP35 . These 60 residues are disordered and a functional role for this site is not yet known . Importantly , in both crystal structures , the pair of VP35 RBD monomers at each end together forms an asymmetric dimer , which binds to dsRNA using a bimodal strategy [10] , [22] . One of the monomers , termed the “backbone-binding” VP35 , binds both strands of the dsRNA backbone using residues in and around its central basic patch ( EBOV/RESTV numbering: R305/294 , S272/261 , Q274/263 , and I340/329 ) . The other monomer , termed the “end-capping” VP35 , binds bases at the end of the dsRNA , as well as a portion of end-proximal backbone . This end-capping VP35 uses a hydrophobic patch to bind the terminal nucleotides and uses residues located in its central basic patch to bind the end-proximal portion of dsRNA backbone . Residues of the central basic patch that are used to bind dsRNA in the end-capping interaction are different from residues of the central basic patch that are used to bind dsRNA in the backbone-binding interaction . Mutation of key residues involved in either these end-capping or backbone-binding interactions has been shown to abrogate binding of dsRNA and restore host IFN signaling [10] . The protein–protein dimer interface between the backbone-binding and end-capping monomers is formed by central basic patch residues R312/301 , R322/311 and K339/328 of the backbone-binding VP35 and E262/251 , E269/D258 and D271/260 of the end-capping VP35 ( EBOV/RESTV numbering ) . Note that in the end-capping monomer , R312/301 and R322/311 bind dsRNA backbone while in the backbone-binding monomer , R312/301 and R322/311 form the dimer interface . Together , the pair masks the end of the dsRNA oligo and occupies the recognition site of host immune sensors like RIG-I , MDA5 and LGP2 [25]–[27] . A question remaining from these previous structures is whether VP35 only binds the ends and end-proximal backbone of dsRNA molecules or if it is also able to coat the possible expanses of dsRNA backbone between the ends . Note that the backbone of dsRNA is the target of other viral IFN antagonists , such as the NS1 protein of influenza virus , B2 of flock house virus and P19 of Tombusvirus , and is a key recognition site for the host immune sensors RIG-I and MDA-5 that bind to dsRNA in a length-dependent manner [11]–[13] , [28] , [29] . Here , we report crystal structures of the MARV VP35 RBD , unliganded and in complex with a 12-base pair ( bp ) palindromic dsRNA . The structures and accompanying dsRNA-binding analysis performed by dot-blot assays and isothermal titration calorimetry reveal that MARV VP35 RBD coats the sugar-phosphate backbone along the length of dsRNA in addition to capping at the terminus , suggesting an alternate mechanism of dsRNA masking for immunosuppression . In addition , we demonstrate that MARV VP35 is a weaker IFN antagonist than EBOV or RESTV VP35 , possibly due to the lower affinity of the MARV VP35 RBD for dsRNA . Suppression of IFN responses by VP35 is likely just one factor in virulence , as MARV can be just as lethal as EBOV . Indeed , recent outbreaks of MARV have occurred with 80–90% lethality [4] .
MARV VP35 RBD was crystallized in space group C2221 with one molecule of the VP35 RBD in the asymmetric unit ( Table 1 and 2 ) . Interpretable electron density was observed for residues 208–329 of the RBD . Like the VP35 RBDs of EBOV and RESTV , the RBD of MARV VP35 contains α-helical and β-sheet subdomains connected by a short loop ( Fig . 1A ) . The β-sheet subdomain is formed by a three-stranded mixed β-sheet and an α-helix , and features the conserved , central basic patch critical to dsRNA binding , that contains residues R271 , R294 , K298 , R301 , K311 , R325 and K328 ( Fig . 1B ) . The α-helical subdomain contains a four-helix bundle followed by a short fifth helix and features the conserved first basic patch containing residues K211 , K237 and K241 ( Fig . S1 ) . The RBD of MARV VP35 is 43% and 41% identical in sequence to those of EBOV and RESTV , respectively , and the crystal structures align with a root mean square deviation ( r . m . s . d . ) of 1 . 07 Å and 0 . 92 Å , to EBOV and RESTV respectively ( Fig . 1C ) . The location and in general , the contents of both the central and first basic patches are conserved among the structures . MARV VP35 RBD was crystallized in complex with a 12-bp dsRNA oligonucleotide . Data were complete to 2 . 5 Å resolution and the structure was determined by molecular replacement in space group P21 ( Table 1 and 2 ) . The asymmetric unit contains four molecules of the VP35 RBD bound to one molecule of 12-bp dsRNA . Interpretable electron density was observed for residues 208–329 of the RBD and all 24 bases of the dsRNA . Residues 205–207 are disordered in all four subunits . The MARV VP35 RBD protomers from the complex align with the unliganded MARV VP35 RBD with an r . m . s . d . of 0 . 44 Å suggesting no appreciable secondary structural changes occur upon dsRNA binding . In the previously reported EBOV and RESTV VP35 RBD structures bound to dsRNA , the ends of the dsRNA are bound by the VP35 RBD and do not interact with each other . By contrast , in the MARV VP35 complex structure , the terminal bases of each dsRNA in the asymmetric unit stack with those of the dsRNA in a neighboring asymmetric unit . Thus , in these crystals , the dsRNA molecules together assemble one , pseudo-continuous dsRNA helix ( Fig . 2 ) . The four MARV VP35 RBDs in the asymmetric unit do not end-cap , but instead , solely bind along the sugar-phosphate backbone , forming a continuous spiral of VP35 RBDs along the dsRNA . This arrangement results in a complete coating of the dsRNA by VP35 RBDs , with each of the conserved , central basic patches of VP35 RBD facing the dsRNA sugar-phosphate backbone ( Figs . 2A and 2B ) . Each VP35 RBD–dsRNA interaction involves ∼520 Å2 of buried surface and is accomplished by direct hydrogen bonds between N261 , Q263 , T267 , R271 , S299 , and I329 and the ribose-phosphate dsRNA backbone ( Fig . 3 ) . N261 makes additional water-bridged hydrogen bonds to the dsRNA backbone , as do residues N224 , R294 , P295 , R301 and K328 . In addition , the side chain of K298 is positioned 4 . 5 Å from the backbone phosphate and may form favorable longer-range interactions . In EBOV and RESTV , the backbone-binding VP35 makes similar interactions with dsRNA although the identity of some of the residues differs by viral species . Along the dsRNA , the α-helical subdomain of each bound RBD interacts with the β-sheet subdomain of a neighboring bound RBD , using an interface different from that observed between end-capping and backbone-binding VP35s in the RESTV and EBOV structures . For MARV , the neighbor interactions involve ∼340 Å2 of buried surface , a hydrogen bond between the amide nitrogen atom of A302 and the carbonyl oxygen atom of T219 and ∼20 non-bonded interactions ( Fig . S2 ) . It is interesting that we observe no “end-capping” interactions in this MARV VP35 RBD - dsRNA complex . We performed biochemical experiments to determine whether the end-capping type of VP35 RBD binding could occur for the MARV VP35 RBD in solution , even if it did not appear in these crystals . Dual-filter dot-blot binding assays with radiolabeled dsRNA were used to investigate binding of MARV VP35 RBD to dsRNA . MARV VP35 RBD binds to 18-bp RNA with blunt ends or a 3′ or 5′ overhang with similar affinity , in a cooperative manner ( Fig . 4A , and Table 3 ) . Multiple residues were mutated separately to alanine and tested for binding to an 18-bp blunt-ended dsRNA oligomer . We find that R271A and R301A mutations ( central basic patch residues that contact dsRNA ) each disrupt binding to dsRNA . Interestingly , a K298A mutation , which is 4 . 5 Å from dsRNA phosphate backbone , also disrupts dsRNA binding ( Fig . 4B ) . Thus , although it is not readily apparent in the crystal structure , the residue K298 appears to be important for dsRNA recognition and binding . Mutation of K311A ( a central basic patch that does not contact dsRNA ) has no effect on binding 18-bp dsRNA as one might expect ( Fig . 4B ) . Interestingly , the corresponding residues of the end-capping monomers in RESTV ( R311 ) and EBOV ( R322 ) make hydrogen bonds to dsRNA , and those of the backbone-binding monomers in RESTV and EBOV form hydrogen bonds to E251 ( E262 in EBOV ) across the dimer interface . An R322A mutation in EBOV VP35 does abrogate binding to an 8-bp dsRNA [10] . In RESTV and EBOV , the protein carboxy terminus at residue I340/329 and the side chain of the penultimate residue K339/328 form critical hydrogen bonds to the dsRNA backbone [10] , [22] . These particular contacts appear not to be critical for MARV , as MARV VP35 truncated at position 327 ( MARV VP35205–327 ) , immediately prior to these residues , binds to dsRNA with an affinity similar to that of wild-type ( Fig . S3 ) . Additional dot-blot binding data suggest that filovirus VP35 RBDs bind blunt-ended dsRNA with µM affinity: ∼2 µM for EBOV , ∼2 . 5 µM for RESTV and ∼8 . 5 µM for MARV ( Table 3 , Fig . S4 ) . Note that MARV VP35 RBD binds blunt-ended dsRNA with 3–5 fold lower affinity than the EBOV and RESTV VP35 RBD . The presence of a 5′ overhang on dsRNA increases the affinity of MARV VP35 RBD to dsRNA by 2-fold . The presence of a 3′ overhang only slightly diminishes affinity of MARV VP35 RBDs for dsRNA , but greatly diminishes binding of EBOV and RESTV VP35 RBDs ( Table 3 , Fig . 4 and S4 ) . In the crystal structures of the EBOV and RESTV VP35 RBDs bound to dsRNA , a key Phe of the end-capping molecules ( F239 in EBOV and F228 in RESTV ) makes hydrophobic interactions with neighboring residue I340 and also with the terminal base of dsRNA . It was thought that contact of this Phe to the RNA base could be important as an F239A mutation in EBOV abrogates binding to dsRNA [10] . However , in the backbone-binding copy of EBOV and RESTV VP35 and in all four MARV VP35s , F228/239 makes no contact to dsRNA . The backbone-binding VP35s would presumably bind dsRNA just as well if F228/239 were mutated , yet dsRNA binding is blocked . It is possible that the importance of this residue does not lie in a critical aromatic stacking interaction with dsRNA , but instead in maintenance of the VP35 structure . The residue F228 is located in a hydrophobic pocket that bridges the α-helical subdomain to the β-sheet subdomain . In addition , F228 is located adjacent to residues Q263 and T267 that make direct bonds to dsRNA and might play a role in positioning the residues for hydrogen bonds with dsRNA . Indeed , dot-blot binding assays reveal that while MARV F228A VP35 is unable to bind dsRNA , F228L binds dsRNA at wild-type levels ( Figs . 4B , S3 ) . 2D NOESY NMR experiments indicate that no significant global structural change occurs upon F228A mutation ( Fig . S5 ) , and hence , subtle differences in structure or position of the side chains around F228 appear sufficient to block dsRNA binding . Interestingly , although the monomeric EBOV VP35 RBD is observed to both backbone-bind and end-cap dsRNA in the EBOV crystal structure , the monomeric MARV VP35 RBD is only observed to backbone-bind in the crystal structure presented here . We wondered if MARV VP35 is also able to end-cap in solution , but that end-capping was precluded by this particular crystal packing arrangement , or if MARV VP35 does not end-cap at all . We conducted further experiments to gain insights into the ability of MARV VP35 to end-cap dsRNA . Control ITC experiments show that binding of MARV VP35 to single-stranded RNA ( as opposed to dsRNA ) is negligible , and also , that no heat is produced upon simple dilution of an 18-bp dsRNA into a buffer identical to that in which the VP35 RBDs was stored ( Fig . S6 ) . These control experiments indicate that any heat generated during the mixing of VP35 RBD with dsRNA results from a direct interaction between the two molecules . By isothermal calorimetry , MARV VP35 RBD interacts with blunt-ended , 18-bp dsRNA via a complex thermodynamic profile . Two major , distinct types of binding events take place that each possess a specific thermodynamic signature . Analysis of the data via a two-set-of-sites model indicates that both of the binding events observed ( A and B ) are energetically favorable and have binding affinities in the µM range . From the thermodynamic values derived from a two-set-of-sites model , it appears that binding event A is endothermic and driven by entropy , whereas binding event B is exothermic and driven by enthalpy . Binding event A has a stoichiometry of ∼1 MARV VP35 RBD binding to one 18-bp dsRNA , whereas binding event B has a stoichiometry of 2–3 MARV VP35 RBDs binding to each 18-bp dsRNA ( Fig . 5 and Table 4 ) . In order to reveal the identity of the two events , the RBD was titrated into solutions of 3′ overhang-containing 18-bp dsRNA , blunt-ended 18-bp dsRNA , or blunt-ended 12-bp dsRNA . When the MARV VP35 RBD is titrated into 3′ overhang dsRNA instead of blunt-ended dsRNA , binding event A almost completely disappears . Using a two-set-of-binding-sites model to fit this data , the stoichiometry of binding event A to 3′-overhang dsRNA is 0 . 1∶1 ( vs . 1 . 1∶1 for blunt-ended dsRNA ) . The stoichiometry of binding event B remains the same within experimental error ( ∼3 . 4∶1 for 3′ overhang dsRNA vs ∼2 . 6∶1 for blunt-ended dsRNA ) . When binding of the shorter , blunt-ended 12-bp oligo is compared to binding of the longer , blunt-ended 18-bp oligo , we note no changes to the binding event A within experimental error ( 1 . 0 vs 1 . 1 for 12-bp vs . 18-bp dsRNA ) . However , we do observe that in binding event B , fewer MARV VP35 RBDs bind to the shorter 12-bp dsRNA than to the longer 18-bp dsRNA . Specifically , ∼1–2 MARV VP35 RBDs are observed to bind 12-bp dsRNA while ∼2–3 bind 18-bp dsRNA . The endothermic binding event A likely represents end-capping because it is blocked by the presence of a 3′ overhang at the end of the dsRNA and is unchanged by the length of the dsRNA . In addition , binding event A is seen to be driven by entropic effects , which are generally associated with the interaction of hydrophobic patches . Indeed , in the ebolavirus VP35 RBD-dsRNA crystal structures , end-capping is mostly mediated by hydrophobic residues . On the other hand , the exothermic binding event B likely represents backbone-binding because it is unaffected by the presence of a 3′ overhang and because more molecules can bind as the length of the oligo is increased . Hence , ITC suggests that an end-capping interaction does occur for MARV VP35 RBD in solution , even though it was not observed in this particular crystal packing arrangement . We compared the abilities of full-length VP35 and the VP35 RBD of MARV , EBOV and RESTV to antagonize activation of the IFNβ promoter in a firefly luciferase reporter assay . Negative controls for the IFNβ assay included R301A point mutations in MARV and RESTV and the equivalent R312A point mutation in EBOV . These point mutations knock out dsRNA binding , and it has been previously shown for EBOV , that loss of dsRNA binding by R312A diminishes IFN antagonism [10] . In the assay reported here , full-length MARV VP35 is ∼5 fold less effective in inhibition of the human IFNβ promoter than the full-length VP35s of EBOV or RESTV ( Fig . 6 ) . Interestingly , the MARV VP35 RBD is slightly more effective as an IFN antagonist than the full-length protein . By contrast , the VP35 RBDs of EBOV and RESTV are ∼4 and ∼2-fold less effective than their full-length proteins . For all three proteins , the point mutation R301A in RESTV and MARV and its equivalent R312A in EBOV are detrimental to IFN antagonism . However , the mutations in EBOV and RESTV VP35 still result in some weak level of IFN antagonism ( Fig . 6 ) .
VP35 plays multiple roles in the viral life cycle . One of these roles , IFN antagonism , involves its C-terminal dsRNA-binding domain . Previous crystal structures of the VP35 RBDs from RESTV and EBOV , in complex with blunt-ended dsRNAs , have revealed that two molecules of VP35 RBD bind to the end of the dsRNA , each molecule by a different mechanism [10] , [22] . The first , “backbone-binding” VP35 interacts solely with the backbone of dsRNA using the residues in the central basic patch . The second , “end-capping” VP35 packs against the terminus of dsRNA using residues in a hydrophobic patch , and also interacts with a portion of end-proximal dsRNA backbone using residues in the central basic patch . Together the backbone-binding and the end-capping VP35s form an asymmetric dimer held together by a network of hydrogen bonds . The crystal structure of MARV VP35 RBD alone and in complex with a 12-bp palinodromic dsRNA shows that the individual dsRNA oligonucleotides have self-assembled into an essentially continuous dsRNA that is completely coated by copies of the VP35 RBD with no significant structural changes in the RBD upon binding dsRNA . No end-capping interactions are observed . This structure illustrates an arrangement by which a filovirus VP35 RBD could indeed coat the dsRNA backbone between the ends to block backbone-sensitive immune surveillance molecules like RIG-I and MDA5 . The MARV VP35 RBD – dsRNA interaction is similar to that of the backbone-binding VP35 RBDs of EBOV and RESTV in complex with dsRNA ( Fig . 7 ) . However , some differences between MARV and EBOV/RESTV occur in individual residues of the central basic patches of the different viruses . Dot-blot binding analysis reveals that MARV R301 , K298 and R271 are critical to binding dsRNA . By contrast , K298 in RESTV and its equivalent K309 in EBOV , are not critical for dsRNA binding [10] . In MARV , the residues K311 , K328 and I329 are not critical for dsRNA binding ( Table 5 ) . By contrast in EBOV , residues R322 and K339 are critical for dsRNA binding [10] . Of additional interest is position F228 in MARV ( F239 in EBOV ) . F228 forms an aromatic contact with the final dsRNA base in the end-capping VP35s in both EBOV and RESTV structures , but makes no interaction with dsRNA in the backbone-binding VP35s in EBOV , RESTV or MARV . We wondered if F228 is instead important for maintenance of the VP35 structure , rather than formation of a specific dsRNA contact . A leucine at this position may be able to maintain the structural integrity of the hydrophobic pocket , yet would not be able to form the same aromatic base-stacking interactions with the 3′ dsRNA base . Indeed , dot-blot experiments indicate that in MARV , F228A abrogates dsRNA binding , but that the alternate mutation , F228L , retains wild-type levels of dsRNA binding . Similarly , H240 in EBOV ( equivalent to H229 in MARV ) is located in the hydrophobic pocket adjacent to the phenylalanine . This histidine does not contact dsRNA in any of the structures determined , yet its mutation abrogates dsRNA binding [10] . Although no major structural rearrangement occur with an F228A mutation ( Fig . S5 ) , mutation within this hydrophobic pocket may cause subtle conformational changes or residue re-positioning that are nonetheless sufficient to abrogate dsRNA binding . Our isothermal calorimetry experiments indicate that in solution , as well as in crystals , MARV VP35 RBDs can coat the dsRNA sugar-phosphate backbone between the ends . Although end-capping also occurs in solution , it was not observed in crystals and does not seem to be a requirement of the MARV VP35 RBD to bind dsRNA . What would MARV VP35 end-capping look like ? In infected cells , we would expect that MARV VP35 RBD could continuously coat any exposed backbone of dsRNA . At the terminus of the dsRNA , two VP35 RBD molecules likely collapse to cap the end . At this end cap , the residues in the partially exposed central basic patch of the final backbone-binding RBD could be neutralized by extensive hydrogen bonding with acidic residues from the end capping subunit stabilizing the end cap assembly ( Fig . 7 ) . The favorable interactions of these otherwise unsatisfied residues could form an asymmetric dimer as observed in both EBOV and RESTV structures . Recognition of dsRNA , a unique product of viral infection by host proteins RIG-I and MDA5 leads to a potent immune response involving production of type I IFNs and proinflammatory cytokines [30]–[32] . Recent crystal structures of RIG-I in complex with dsRNA reveal that the C-terminal repressor domain caps the hydrophobic terminus and 5′-ppp of dsRNA , while the helicase domains of RIG-I bind the backbone of dsRNA [25] , [26] , [33] . Further , both RIG-I and MDA5 have been shown to cooperatively oligomerize on dsRNA in a length-dependent fashion [28] , [29] , with stoichiometry of binding dependent on the length of the dsRNA [34] . These recent findings suggest that evading host detection of viral dsRNA could be best achieved if VP35 can both coat the backbone and cap the ends of dsRNA . The results presented here indicate that MARV VP35 can indeed , both cap the ends and coat the expanses of dsRNA backbone between the ends . The critical question of how full-length VP35 interacts with dsRNA is still to be answered . Full-length VP35 from all the filoviruses oligomerizes via an N-terminal coiled-coil domain [21] , [35] , [36] . It is unclear if the individual RBDs interact with each other as observed in EBOV and RESTV crystal structures , or if they exist as independent monomers as seen in the MARV crystal structure . Our IFN suppression assay suggests that for MARV , the RBD confers IFN suppression that is similar to that conferred by the full-length VP35 oligomer . By contrast , the EBOV or RESTV RBDs alone confer IFN suppression that is weaker than that achieved by the full-length protein . These results suggest that in the ebolaviruses , the rest of the VP35 molecule controls oligomerization in some way that does not occur for MARV , or that the rest of the ebolavirus VP35 contributes to IFN inhibition in some other way that does not occur for MARV . Indeed , the extent of IFN antagonism achieved by MARV VP35 is lower than that achieved by the ebolaviruses . This difference is possibly due to the 3–5 fold lower affinity of MARV VP35 for dsRNA . However , binding of dsRNA by VP35 does not appear to be the sole determinant of virulence , as RESTV is nonpathogenic to humans while recent outbreaks of MARV have occurred with up to 90% human lethality . Indeed , VP35 has other functions in the infected cell as well , numerous host and viral factors determine the kinetics of viral replication , and additional avenues of immunosuppression are facilitated by separate proteins in the different viruses . In the ebolaviruses , the VP24 protein contributes to immunosuppression , while in MARV , additional immunosuppressive functions are instead contributed by the VP40 protein [37] , [38] . In summary , the crystal structures , biochemical and biophysical experiments reported here illustrate coating of the dsRNA backbone as a likely mechanism of IFN antagonism for MARV . This mode of dsRNA recognition is in contrast to the end-only recognition observed in crystal structures of VP35 RBD from the ebolaviruses . This additional mode of dsRNA recognition by backbone coating explains how MARV can avoid length-dependent sensing of its dsRNA , and provides an alternative template for design of antiviral therapeutics .
The construct for expression of MARV VP35 RBD contains residues 205–329 and was subcloned into a pET46b vector . This construct is similar in length to that crystallized for EBOV . A slightly shorter construct of MARV VP35 comprising residues 205–327 was made using a pET-46 Ek/LIC vector kit . Site-directed mutagenesis using a Phusion Site-directed mutagenesis kit was employed to introduce point mutations in the clones . Synthetic RNA oligomers were obtained from Integrated DNA Technologies . The sequences of RNA oligos used are as follows: ( for crystallization ) 12-bp palindrome 5′ CUA GAC GUC UAG 3′; ( for dot blot and ITC experiments ) 18-bp blunt ended sense 5′ AGA AGG AGG GAG GGA GGA 3′; 18-bp blunt ended anti-sense 5′ UCC UCC CUC CCU CCU UCU 3′; 18-bp with 3′ overhang sense 5′ AGA AGG AGG GAG GGA GGA GAG 3′; 18-bp with 3′ overhang anti-sense 5′ CUC UCC UCC CUC CCU CCU UCU 3′; 18-bp with 5′ overhang sense 5′ GAG AGA AGG AGG GAG GGA GGA 3′; 18-bp with 5′ overhang anti-sense 5′ UCC UCC CUC CCU CCU UCU CUC 3′; 12-bp blunt ended sense 5′ GAC ACC UGA UUC 3′; 12-bp blunt ended anti-sense 5′ GAA UCA GGU GUC 3′ . The protein encoding the MARV VP35 RBD was expressed in E . coli R2 cells . The cells were grown in a 50 mL overnight culture supplemented with ampicillin and chloromphenicol at 37°C with shaking at 300 rpm . The overnight culture was introduced into 1 L LB broth media supplemented with ampicillin and grown to an OD600 nm of 0 . 6 and induced with 1 . 0 mM IPTG . The protein was expressed over 5 h with shaking at 37°C . The cells were harvested by centrifugation and lysed using a sonicator in a wash buffer containing 20 mM Tris , pH 7 . 5 , 50 mM NaCl and 10 mM imidazole . The lysate was separated from the cell debris by centrifugation at 16 , 000 rpm and applied to a His-Trap column ( GE healthcare ) pre-equilibrated with wash buffer . The column was washed with 10 column volumes of wash buffer , followed by another wash , with wash buffer containing 30 mM imidazole . The protein was eluted in wash buffer containing 300 mM imidazole . The 6x-His Tag was cleaved by incubating the protein with Tobacco Etch Virus protease overnight in buffer containing 25 mM Bis Tris pH 6 . 5 , 50 mM NaCl , 5 mM DTT . The protein was further purified using ion exchange chromatography . A Mono S column was equilibrated with buffer containing 25 mM Tris , pH 7 . 5 , 50 mM NaCl , 5 mM TCEP and the protein was eluted with a gradient of NaCl . The protein fractions were further purified and buffer exchanged into 10 mM Tris , pH 8 . 0 , 200 mM NaCl , 2 mM TCEP by Superdex 75 size exclusion . The shorter construct of MARV VP35 RBD ( construct containing residues 205–327 ) and mutants were expressed and purified in a similar fashion . EBOV and RESTV VP35 RBDs used in RNA binding assays were expressed and purified from constructs containing residues 216–340 and 205–329 , respectively , similar to MARV VP35 RBD . The MARV VP35 RBD was incubated in 1∶1 ratio with various crystallization solutions from sparse matrix screens in a hanging drop format . The protein crystallized as clusters of needles in 2–2 . 4 M ammonium sulfate , 100 mM sodium acetate , pH 4 . 6 . The individual needles were harvested and cryoprotected in sequential soaks of well solution containing 5% , 10% , 15% and 20% glycerol and were flash cooled under liquid nitrogen for diffraction experiments . For crystallization of the MARV VP35 RBD-dsRNA complex , MARV VP35 RBD was incubated with 12-bp dsRNA in a RNA∶protein molar ratio of 1∶1 . 2 for 2 h . An initial crystallization hit of the complex was obtained in the PEG/Ion sparse matrix screen ( Hampton Research ) condition 0 . 1 M Bis-Tris , pH 6 . 5 , 2% v/v Tacsimate , pH 6 . 0 , and 20% PEG 3350 using sitting drop vapor diffusion . Needle-like crystals of dimensions 200 µm×30 µm×30 µm were obtained after optimization in condition 0 . 1 M Bis-Tris , pH 6 . 2 , 2% v/v Tacsimate , pH 7 . 0 , and 18% PEG 3350 and grew over a period of 3–4 days . The crystals were cryoprotected in well solution supplemented with 15% glycerol prior to flash cooling in liquid nitrogen for diffraction experiments . Data for the uncomplexed MARV VP35 RBD were collected on beamline 8 . 3 . 1 of the ALS ( Advanced Light Source Berkeley , CA ) . Data for the MARV VP35 RBD – dsRNA complex were collected on beamline 5 . 0 . 2 of the ALS . For MARV VP35 RBD , data were collected over a rotation range of 180° with an oscillation range of 1° and 4 s exposure per frame , at a crystal-to-detector distance of 200 mm . For MARV VP35 RBD-dsRNA complex , data were collected over a rotation range of 180° with an oscillation range of 1° and 1 s exposure per frame , at a crystal-to-detector distance of 350 mm . The data were integrated and scaled using the programs D*TREK and HKL2000 for the unbound MARVP35 RBD and the dsRNA complex , respectively [39] , [40] . Data collection statistics are summarized in Table 1 . The structure of the unbound MARV VP35 RBD was determined by molecular replacement with the structure of the RESTV VP35 RBD ( PDB code 3KS4 ) as the search model using the program Phaser in the Phenix suite [41] , [42] . The asymmetric unit contains one molecule of the MARV VP35 RBD . The structure of MARV VP35 bound to 12-bp RNA was determined by molecular replacement with the structure of the MARV VP35 RBD as the search model , using the program Phaser . The initial model from molecular replacement contained four copies of the MARV VP35 RBD in the asymmetric unit . All the models were refined using Refmac and Phenix , followed by model building using the program Coot [43]–[45] . Initial difference Fourier maps clearly showed positive density for the bound 12-bp dsRNA in the complex structure as well as water molecules that were subsequently built into the model . The final refinement statistics are shown in Table 2 . The 5′ end of the sense strand was labeled with 32P using γ-32P-ATP and T4 Polynucleotide kinase ( New England Bio Labs ) . The labeled oligo was duplexed using a 1 . 5 molar excess of the anti-sense strand by heating for 5 min at 90°C in 10 mM Tris , pH 8 . 0 , 200 mM NaCl and 2 mM TCEP ( binding buffer ) followed by slow cooling down to room temperature . Trace amounts of RNA were incubated with increasing amounts of protein ( in binding buffer ) in a volume of 50 µl for 2 h [46] . The reaction mixture was pulled through successive layers of protein-binding membrane ( Protran-BA85 from Whatman ) and RNA-binding membrane ( Hybond-N+ from Amersham ) using a Minifold I Dot-Blot system – 96 well apparatus under vacuum . The membranes were separated , dried in air for 10 min , wrapped in transparent plastic wrap and exposed overnight to a phosphor screen ( Amersham ) . The screen was imaged using a Typhoon phosphorimager and the images were quantified using Imagequant software ( GE Healthcare ) . The ratio of RNA bound to protein versus RNA bound to the membrane was obtained and the data were fit to one-site specific binding Hill equation using GraphPad Prism software version 5 . 0d ( www . graphpad . com ) . Binding experiments were done at least in duplicate . Isothermal titration calorimetry ( ITC ) experiments were carried out using a MicroCal iTC200 instrument ( GE ) . Extensive dialysis of all proteins and RNA molecules against 10 mM Tris , pH 8 . 0 , 200 mM NaCl , 2 mM TCEP buffer was performed prior to ITC experiments . Protein and RNA concentrations were determined by UV absorbance using calculated extinction coefficients ( ProtParam , Gasteiger , 2005 ) . All ITC experiments were carried out in duplicate . MARV VP35 RBD was in the syringe at concentrations ranging between 1–2 mM , while the RNA molecules were in the cell at concentrations ranging between 15–40 µM . One experiment consisted of either 16 injections of 2 . 5 µl each or 32 injections of 1 . 25 µl each with injection interval of 180 s and reference power of 5 µcals . For the experiment involving 12-bp dsRNA , duplicate experiments were also done with time intervals of 300 s and 500 s between injections to allow the system to reach equilibrium . Affinity constants ( Kd ) and molar reaction enthalpy ( ΔH ) were calculated by fitting the integrated titration peaks with Origin 7 . 0 software using a “two-sets-of-sites” binding model , as deemed appropriate by visual inspection of the raw data . The first data point corresponding to a 0 . 5 µl injection was discarded in the analysis of all the experiments . The change in Gibbs free energy , ΔG , was then calculated as ΔGbinding = RTlnKd [47] . To construct the IFNβ promoter firefly luciferase reporter plasmid , the human IFNβ promoter sequence was amplified from human genomic DNA using Phusion DNA polymerase ( New England Biolabs ) and the following primers: IFNβ −125 ( 5′-CAG GGT ACC GAG TTT TAG AAA CTA CTA AAA TG-3′ ) and IFNβ +34 ( 5′-GTA CTC GAG CAA AGG CTT CGA AAG G-3′ ) . The primers correspond to the human IFNβ promoter sequence from −125 to +34 relative to the transcription start site ( +1 ) . The PCR fragment was digested with the restriction enzymes KpnI and XhoI and then ligated into a similarly digested pGL4 . 10 ( luc2 ) vector ( Promega ) . HEK-293T cells were grown in complete medium ( Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum ) and plated in 24-well poly-D-lysine coated plates at 95 , 000 cells per well . Using LT-1 ( Mirus ) , cells were transfected with 0 . 5 µg of the IFNβ promoter reporter plasmid ( pGL-IFNβ luc ) and 1 . 0 µg of the indicated pCAGGS VP35 protein expression plasmids or empty pCAGGS plasmid . The full-length VP35 constructs comprise residues 1–329 for MARV and RESTV and residues 1–340 for EBOV . The VP35 RBD constructs comprise residues 205–329 for MARV and RESTV and residues 216–340 for EBOV . Twenty-four h after transfection , cells were infected with Sendai virus for 1 h at a multiplicity of infection of 5 . Cells were washed three times after infection and complete medium was replaced onto cells . Twenty-four h after infection , cells were lysed with Glo Lysis Buffer ( Promega ) and firefly luciferase expression was determined using Steady-Glo Assay Buffer ( Promega ) . Data were obtained from three independent experiments carried out in triplicate . The relative fold increase of firefly luciferase expression was determined by comparing Sendai virus infected cells to uninfected control cells . The protein samples were dialyzed overnight into 10 mM Tris , pH 6 . 5 , 200 mM NaCl , 5 mM TCEP and concentrated to a final concentration of ∼380 µM for the MARV VP35 RBD wild type protein and ∼1 . 2 mM for the F228A mutant . D2O was added to each sample to 5% v/v . A homonuclear 2D NOESY with a mixing time of 100 ms was obtained at 298K using a Bruker DRX600 MHz using a 5 mm TXI cryo-probe with Z-gradient . The data matrix consisted of 8192 complex points in t1 and 1024 complex points in t2 . The data was processed using NMRPipe [48] and analyzed using NMRView [49] . | Filoviruses , Marburg virus and five ebolaviruses , cause severe hemorrhagic fever that is characterized by suppression of the innate immune system . Important to immunosuppression is the viral protein VP35 , which binds to and masks double-stranded ( ds ) RNA , a key signature of virus infection that is recognized by host sentry proteins like RIG-I and MDA-5 . Previous crystal structures of VP35 from two ebolaviruses showed it to form an asymmetric dimer to cap the ends of dsRNA molecules . However , the question remained whether VP35 could mask remaining lengths of dsRNA between the ends from immune surveillance . Here we present the crystal structure of the dsRNA-binding domain ( RBD ) of Marburg virus VP35 , alone and in complex with dsRNA . This crystal structure presents a very different arrangement of VP35s on dsRNA . Rather than binding only the ends , the Marburg virus VP35s spiral around the dsRNA backbone , continuously coating it . Additional biochemical experiments indicate that this continuous coating occurs in solution , and that like the ebolaviruses , Marburg virus VP35 is also able to cap the dsRNA ends , even though this was not apparent in the crystal structure . Together , this work illustrates how Marburg virus VP35 prevents recognition of dsRNA by backbone-sensing immune sentry molecules and provides an additional avenue for antiviral development . | [
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"medicine",
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] | 2012 | Marburg Virus VP35 Can Both Fully Coat the Backbone and Cap the Ends of dsRNA for Interferon Antagonism |
The essential mammalian gene TACC3 is frequently mutated and amplified in cancers and its fusion products exhibit oncogenic activity in glioblastomas . TACC3 functions in mitotic spindle assembly and chromosome segregation . In particular , phosphorylation on S558 by the mitotic kinase , Aurora-A , promotes spindle recruitment of TACC3 and triggers the formation of a complex with ch-TOG-clathrin that crosslinks and stabilises kinetochore microtubules . Here we map the Aurora-A-binding interface in TACC3 and show that TACC3 potently activates Aurora-A through a domain centered on F525 . Vertebrate cells carrying homozygous F525A mutation in the endogenous TACC3 loci exhibit defects in TACC3 function , namely perturbed localization , reduced phosphorylation and weakened interaction with clathrin . The most striking feature of the F525A cells however is a marked shortening of mitosis , at least in part due to rapid spindle assembly . F525A cells do not exhibit chromosome missegregation , indicating that they undergo fast yet apparently faithful mitosis . By contrast , mutating the phosphorylation site S558 to alanine in TACC3 causes aneuploidy without a significant change in mitotic duration . Our work has therefore defined a regulatory role for the Aurora-A-TACC3 interaction beyond the act of phosphorylation at S558 . We propose that the regulatory relationship between Aurora-A and TACC3 enables the transition from the microtubule-polymerase activity of TACC3-ch-TOG to the microtubule-crosslinking activity of TACC3-ch-TOG-clathrin complexes as mitosis progresses . Aurora-A-dependent control of TACC3 could determine the balance between these activities , thereby influencing not only spindle length and stability but also the speed of spindle formation with vital consequences for chromosome alignment and segregation .
Formation of a functional mitotic spindle is a pre-requisite for equal distribution of chromosomes between two daughter cells . Mitotic spindles in animal cells consist of three major microtubule ( MT ) classes: centrosome-associated astral MTs , inter-polar MTs and kinetochore-fibres ( k-fibres ) . During mitosis MTs are produced by multiple simultaneously acting pathways that include centrosomal MT nucleation , Ran-GTP-driven chromatin-dependent MT generation and MT-dependent MT amplification through the Augmin complex [1] . When present the centrosomes act as dominant sites of spindle pole formation , but in all animal cells MT motors and MT-associated proteins play important roles in stabilizing , organising and transporting MTs generated elsewhere in the cytoplasm for incorporation into the mitotic spindle [2 , 3] . Co-operation of these spindle assembly pathways seems important for the timely establishment of a bipolar mitotic spindle [4] . Aurora-A is a Ser/Thr protein kinase that regulates key mitotic events such as centrosome maturation , mitotic entry and spindle assembly [5 , 6] . In particular , Aurora-A function is essential for bipolar spindle formation; loss of Aurora-A kinase activity through gene targeting , RNA interference-mediated depletion or inhibition results in the assembly of characteristic monopolar spindles . A range of additional phenotypes such as short bipolar and multipolar spindles , MT hyper-stabilisation , centrosome and spindle pole fragmentation or chromosome alignment and segregation defects have also been reported in several model systems [7–12] . As a consequence of these spindle defects anaphase onset is delayed in many of these models by an active spindle assembly checkpoint ( SAC ) , which ensures that all chromosomes have bi-oriented and aligned at the metaphase plate in a cell before sister chromatid separation is initiated [12 , 13] . Aurora-A exerts its control over spindle assembly through phosphorylation of several substrates , including TACC3 [14 , 15] . TACC3 , an essential protein in mammals , is a member of the transforming acidic coiled coil ( TACC ) family of centrosomal proteins that bind MTs and interact with the MT polymerase ch-TOG , highly conserved from yeast to mammals [16–20] . TACC3 and its lower eukaryotic homologues , D-TACC in Drosophila and Maskin in Xenopus laevis , have been implicated in both centrosome- and chromatin-driven MT assembly pathways during mitotic spindle assembly [15 , 21–29] . Although the molecular details of TACC3 function remain poorly understood , reports tend to support a central role of TACC3 in promoting MT stability in mitosis [16] . In humans , Aurora-A phosphorylates TACC3 on three residues ( S34 , S552 and S558 ) ; these sites are conserved in Maskin and the S558 equivalent site is also present in D-TACC [26 , 27 , 30] . In mammalian cells , phosphorylation of S558 promotes accumulation of TACC3 on spindle MTs [7] and regulates its binding to clathrin , a protein with a well-established function in endocytosis [31–34] . TACC3 in a complex with clathrin and ch-TOG forms bridges between k-fibres , potentially cross-linking and stabilising these mitotic MT species [35] . Phosphorylated TACC3 binds to the ankle region of clathrin , generating a MT-binding surface that involves the coiled-coil region of TACC3 and the β-propeller domain of clathrin [36] . Intriguingly , this combined MT binding domain mediates association of the TACC3-clathrin-ch-TOG protein complex with MTs despite the high binding affinity of ch-TOG for tubulin and MTs [37] . TACC3 and ch-TOG can interact independently of Aurora-A , and it is thus possible that TACC3 exists both in TACC3-ch-TOG and TACC3-clathrin-ch-TOG complexes in mitotic cells [38] . TACC3 function has been implicated in many different tumour types [39] . In addition to various mutations and amplifications in the gene , oncogenic fusion products between FGFR3 and TACC3 have been identified in glioblastoma , bladder , lung and nasopharyngeal carcinomas [40–44] . These oncogenic fusion products contain the C-terminal TACC domain of TACC3 but lack the clathrin-binding domain inclusive of the Aurora-A phosphorylation site , indicating that these sequences might serve to keep TACC3 activity under check . The activity of Aurora-A is controlled through phosphorylation of the activation loop on Thr288 [45 , 46] . In addition , several activating binding partners of Aurora-A have been identified , many of which are also substrates of the kinase [6] . In the case of TPX2 , the mechanism of activation , and how this synergizes with phosphorylation of T288 , has been resolved [47–49] . The first 43 amino acids of TPX2 stimulate Aurora-A activity , whether the kinase is phosphorylated or not . Thus , TPX2-binding and Aurora-A autophosphorylation work together to stabilize the activation loop in a fully ordered conformation similar to that observed in other Ser/Thr kinases , such as protein kinase A ( PKA ) . Although the function of Aurora-A phosphorylation on TACC3 has been extensively studied , the reciprocal effects of TACC3 on Aurora-A have not received attention . In this study , we investigated the molecular basis of the interaction between Aurora-A and TACC3 . We discover a new Aurora-A binding site in TACC3 that has functions independent of the phosphorylation site . Mutation of either site results in a similar reduction of spindle localization of TACC3 , but has distinct consequences for mitotic progression and chromosome segregation .
TACC3 has three notable conserved regions: the N-terminal region ( NTR ) that includes S34 , the clathrin interaction domain ( CID ) that includes S552 and S558 , and the C-terminal TACC domain ( Fig 1A ) . GST co-precipitation assays were performed to identify the TACC3 binding domain in Aurora-A ( Fig 1B ) . Binding occurs between the catalytic domain of Aurora-A ( residues 122–403 , GST-AurA-DN ) and TACC3-H6c . We investigated whether TACC3 influenced the activity of phosphorylated , active Aurora-A catalytic domain , as other substrates do , using an in vitro kinase assay to monitor the incorporation of 32P into a generic substrate ( myelin basic protein , MBP ) , which was quantified using scintillation counting ( Fig 1C ) . TACC3 enhanced Aurora-A activity by a factor of ~3 , very similar to the effect of the first 43 amino acids of TPX2 . We mapped the region of TACC3 responsible for Aurora-A activation using TACC3 fragments . The NTR stimulated Aurora-A activity , but to a lesser extent than the full-length TACC3 . The TACC domain did not contribute to Aurora-A activation . The central region ( residues 519–563 ) stimulated Aurora-A activity to an even greater extent than full-length TACC3 , as was observed for TPX21-43 versus full-length TPX2 [48] . We therefore focused on this region of TACC3 , which includes most of the CID but lacks the di-leucine motif that is critical for clathrin binding [36] . To avoid confusion with the CID region of TACC3 , we refer to this region as TACC3act hereafter . In addition to the allosteric activation of phosphorylated Aurora-A , TPX2 regulates the phosphorylation of Aurora-A through two mechanisms: stimulation of autophosphorylation and protection from dephosphorylation by PP1 . We therefore asked whether TACC3act modulates Aurora-A phosphorylation in vitro . Like TPX2 , TACC3act stimulated autophosphorylation of initially unphosphorylated Aurora-A catalytic domain , as measured by western blotting using a phospho-specific T288 Aurora-A antibody ( Fig 1D ) . Unlike TPX2 , TACC3act did not protect initially phosphorylated Aurora-A catalytic domain from dephosphorylation by PP1 ( Fig 1E ) . Although TACC3act resembles TPX21-43 , as both are relatively small protein fragments capable of stimulating Aurora-A activity , this result raised the question of whether they might have different binding sites on Aurora-A . We proceeded to characterize the interaction between the catalytic domain of Aurora-A and TACC3act . We investigated whether the interaction of TACC3act with Aurora-A was competitive with TPX21-43 by GST co-precipitation assay . A catalytically inactive mutant ( D274N ) of the Aurora-A catalytic domain fused to GST was used as bait and we established a robust interaction with TACC3act , where TPX21-43 was used as a positive control for Aurora-A interaction ( Fig 2A ) . GST alone was used as a negative control to show that binding of TACC3act and TPX21-43 to GST-Aurora-A was specific . We observed that TACC3act and TPX21-43 were able to bind simultaneously to Aurora-A , with no apparent reduction in interaction when the concentration of TPX21-43 was increased to a 10-fold excess over TACC3 . Size exclusion chromatography ( SEC ) analysis of TACC3act and TPX21-43 showed that the retention volume of the two activators is decreased in the presence of Aurora-A kinase domain , and that the three proteins co-eluted ( S1B Fig ) . Taken together , these observations indicate simultaneous interactions of TACC3 and TPX2 with Aurora-A and suggest that the two activators have different , non-competitive binding sites on the kinase . In brief , we have shown that TACC3 stimulates Aurora-A activity through a binding region , TACC3act ( residues 519–563 ) , which overlaps with the CID . We further investigated the molecular determinants of this interaction using structural biology approaches . We employed NMR spectroscopy to map the precise binding site of Aurora-A on TACC3act . Our first NMR experiments probed the changes in the chemical environment of the backbone residues of TACC3act in the presence or absence of unphosphorylated Aurora-A 122–403 D274N ( AurA-DN; Fig 2B and S1C Fig ) . A 1H-15N HSQC spectrum of TACC3act alone showed reasonable peak separation ( Fig 2B ) , and the backbone was assigned using CCPN analysis [50] . Secondary structure specific data were collected , which showed residues 520–527 and 529–542 on human TACC3 form a pair of nascent α-helices separated by P528 ( Fig 2C and S2 Fig ) . A 1H-15N HSQC spectrum of 15N-labelled TACC3act in the presence of AurA-DN showed that a subset of residues within TACC3act experience chemical shift perturbations when bound to Aurora-A ( Fig 2B ) . The change in chemical shift of the backbone amide of each residue was measured and mapped onto the primary sequence of TACC3act ( Fig 2C and S1C Fig ) . The majority of the chemical shift perturbations were restricted to the N-terminal region of TACC3act , between residues 521 and 535 . The greatest values were found close to P528 , at residues D527 and 529–533 , consistent with a strong involvement of this region of TACC3 in binding to Aurora-A . We next studied whether the presence of TPX21-43 affected the binding of TACC3act to Aurora-A . We added unlabeled TPX21-43 to AurA-DN along with 15N-labeled TACC3act and collected a 1H-15N HSQC spectrum ( Fig 2D ) . The presence of TPX21-43 did not significantly affect the chemical shifts of residues in the N-terminal region of TACC3act , which remained at their values obtained in the TACC3act/AurA-DN mixture . This confirms that TPX21-43 does not displace Aurora-A from TACC3act , consistent with our GST co-precipitation data ( Fig 2A ) . We conclude that TPX21-43 and TACC3act must have largely distinct binding sites on Aurora-A . Notably , the additional chemical shift changes in TACC3act upon addition of TPX21-43 were restricted to the vicinity of S558 , which is the principal site phosphorylated by Aurora-A ( Fig 2C and S1D Fig ) . In addition , chemical shift perturbations observed for the side-chain amides of Q557 indicate their involvement in the structural changes in TACC3act associated with the addition of TPX21-43 . The interaction with Aurora-A involves more residues in TACC3 in the presence of TPX21-43 , but does this enhance the affinity of their interaction ? We tracked the chemical shift changes of TACC3act as Aurora-A was titrated in , both in the presence and absence of TPX21-43 ( Fig 2E ) . We calculated a Kd of 8 . 2 ± 0 . 8 μM in the absence of TPX21-43 . In the presence of TPX21-43 , the Kd decreased to 1 . 1 ± 1 . 2 μM ( Fig 2F ) . The overall enhancement of binding between TACC3act and Aurora-A in the presence of TPX21-43 , and the observation of binding at the phosphorylation site of TACC3 , are consistent with the known role of TPX21-43 in increasing the catalytic activity of Aurora-A . Taken together , structural and biophysical studies reveal two Aurora-A binding sites on TACC3act: the first between residues 520–542 that binds independently of TPX2 , and a second region centered on the S558 phosphorylation site that has TPX2-dependent binding . To corroborate these findings , we aimed to identify point mutations in TACC3act that disrupt the interaction with Aurora-A . We performed alanine-scanning mutagenesis of the six conserved aromatic residues within TACC3act to identify the key binding residues . This strategy was based on the observation that aromatic residues form key interactions at the binding interface of protein-protein complexes , and are important in the function of intrinsically-disordered proteins , such as the Aurora-A binding region of TPX2 [48 , 51–53] . We found that F525 was the only aromatic amino acid within TACC3act that contributed to Aurora-A activation ( Fig 3A ) . The F525A mutation also reduced the activation of Aurora-A by full-length TACC3 , from 5-6-fold to 2-3-fold . Deletion of the entire region flanking the phosphorylation site ( Δ519–546 and Δ564–629 , TACC3ΔΔ ) resulted in further reduction of Aurora-A activation . These findings suggest that residues additional to F525 within these deleted regions are involved in Aurora-A activation . We then investigated whether the F525A mutation affected the binding affinity of TACC3 for Aurora-A . First , using GST co-precipitation assays , we found that TACC3act carrying the F525A mutation exhibited markedly reduced binding relative to wild-type ( WT ) TACC3act ( Fig 3B ) . Since residual binding was detectable , we next quantified the affinity of the interaction . We established an assay to quantify the interactions of Aurora-A with binding partners using microscale thermophoresis ( MST ) . We used a mutant version of the Aurora-A catalytic domain that has both surface-exposed cysteine residues ( C290 , C393 ) mutated to alanine [54] . These mutations stabilize the protein in solution , but do not significantly affect catalytic activity or structure [54 , 55] . The Kd of the interaction with WT TACC3act was determined to be 5 . 7 ± 0 . 4 μM ( Fig 3C ) , a similar value to that determined using NMR spectroscopy , whereas the interaction with the F525A mutant had a Kd of 151 . 8 ± 9 . 3 μM ( Fig 3C ) . We conclude that F525 makes an important contribution to the binding and activation of Aurora-A by TACC3 . The human TACC3 residues implicated in complex formation with Aurora-A are well-conserved in mouse , chicken and Xenopus homologues ( Fig 3D ) . All four TACC3 homologues have a phenylalanine residue at the position equivalent to the human F525 . Indeed , this phenlyalanine residue is essential for binding of full-length Xenopus TACC3 ( maskin ) to Aurora-A ( S3A Fig ) . All four also have a basic residue at the position equivalent to R526 and a proline residue nearby , although the position varies across the four organisms , and is either immediately C-terminal to the basic residue or one residue further along . The region of TACC3 responsible for mediating Aurora-A activation is distinct from , albeit close to , the phosphorylation site at S558 , raising the question of whether these two functions are independent . We investigated whether mutants of TACC3 that were deficient in Aurora-A activation were efficient substrates . Recombinant full-length TACC3 WT , F525A and ΔΔ were incubated with Aurora-A kinase and the extent of phosphorylation was determined by quantitative immunofluorescence using a anti-phospho-S558 TACC3 antibody ( Fig 4A ) and autoradiography to measure 32P-label incorporation ( Fig 4B ) . All TACC3 protein variants were equally well phosphorylated on Ser-558 but total phosphorylation of TACC3ΔΔ was reduced compared to TACC3 WT and F525A . Therefore , at least in vitro , the activation of Aurora-A by TACC3 can be disrupted by the F525A mutation , whilst maintaining phosphorylation of TACC3 . To address whether the phosphorylation state of TACC3 influenced Aurora-A binding and activation , we generated TACC3 variants in which all three Aurora-A phosphorylation sites ( S34 , S552 and S558 ) were mutated to either alanine ( SA , phospho-null mutant ) or glutamic acid ( SE , phospho-mimic mutant ) . The SA mutant had strongly reduced levels of phosphorylation compared to the individual mutations . ( Fig 4B ) . Binding of TACC3-H6c to GST-Aurora-A was assayed by GST co-precipitation assay using GST as a control ( Fig 4C ) . We observed specific interaction of all three TACC3 variants to the Aurora-A catalytic domain . TACC3 phospho-null and phospho-mimic mutants enhanced Aurora-A kinase activity by a similar amount , using an in vitro kinase assay ( Fig 4D ) . Similar results were obtained using recombinant Xenopus proteins ( S3B and S3C Fig ) . In summary , biochemical and structural studies have identified a sub-region of the TACC3 CID centered on F525 that binds Aurora-A and stimulates its kinase activity , independent of the phosphorylation state of S558 . This raises the question of what might be the function of the TACC3 dependent Aurora-A activation during mitosis since it is well established that Aurora-A is efficiently activated by TPX2 . To test the respective contributions to TACC3 function by the F525 and S558 residues , we have introduced defined mutations in the Tacc3 gene using homologous gene targeting in the chicken B cell line , DT40 ( S4–S6 Figs ) . Fig 5A depicts domain organization and numbering of key residues in human and chicken TACC3 . Homozygous mutant cell lines were generated in which either F543 or S574 ( equivalent to F525 and S558 in human TACC3 , respectively ) were replaced with alanine ( referred to as F543A or S574A modifications ) . A further cell line was created in which the CID of TACC3 was removed through deletion of exons 5–9 encoding amino acids 486–701 ( referred to as DEL modification ) . It is worth noting that the ch-TOG interaction domain is intact in the TACC3 mutants ( Fig 5A ) [36] . In all three cases , sequential gene targeting was employed to edit both alleles of Tacc3 . We have obtained multiple independently derived homozygous clones of DEL , S574A and F543A cells , all of which were morphologically normal and viable . F543A was similar to WT , whereas DEL and S574A cells grew more slowly ( Fig 5B ) . To further evaluate proliferation rates in WT and F543A cells , we turned to a recently developed mass-spectrometry-based method that measures levels of 5-Hydroxymethylcytosine ( hmC ) in DNA; previous studies revealed an inverse correlation between hmC levels and cell proliferation [56] . Remarkably , the F543A cells exhibited reduced hmC , indicative of faster proliferation ( Fig 5C ) . Western blots revealed comparable TACC3 expression levels in WT , S574A and F543A cells , and a reduction of ~50% in DEL cells ( Fig 5D ) . The point mutant TACC3 products , TACC3S574A and TACC3F543A , and WT TACC3 , ran at ~100kDa , whereas DEL cells expressed a shorter product of ~75kDa , termed TACC3DEL . Subcellular localisation of TACC3 in DT40 cells is similar to what has been described in other model systems; TACC3 is centrosomal in G2 , and from nuclear envelope breakdown ( NEBD ) onwards it accumulates at the mitotic spindle poles and decorates spindle MTs ( Fig 5E and S7A and S7B Fig ) . In human cells TACC3 localisation to the mitotic spindle is markedly enhanced by phosphorylation of S558 , most likely because this modification promotes interaction with clathrin , which in turn generates a composite MT-binding interface between TACC3 and clathrin [36] . Indeed , TACC3DEL that lacks the CID , including the S558-equivalent Aurora-A phosphorylation site , exhibited a very weak signal restricted to spindle poles ( Fig 5E ) . Although much reduced compared to wild-type TACC3 , TACC3S574A and TACC3F543A were readily detectable on the mitotic spindle . Quantification of TACC3 signal intensities on mitotic spindles confirmed these findings ( Fig 5E ) . As in WT cells , TACC3 was detectable on centrosomes in G2 and prophase S574A , DEL and F543A cells , but at much reduced levels ( S7A Fig ) . In human cells TACC3 is essential for localising ch-TOG to the mitotic spindle but not to centrosomes [57] . Consistently , in S574A , F543A and DEL cells ch-TOG levels followed TACC3 levels on the spindle , with little change in centrosomal amounts ( S7C and S7D Fig ) . To test the MT-binding capacity of the mutant TACC3 proteins , MT-pelleting assays were performed using paclitaxel-stabilised MTs from cell extracts . TACC3S574A , TACC3F543A , and even TACC3DEL that lacks the CID , all co-sedimented with MT polymers , although less efficiently than wild-type TACC3 ( S7E Fig ) . Thus , significant amounts of TACC3 can interact with MTs independently of CID at least in vitro , most likely through the TACC domain . In summary , we find that F543 is required for normal mitotic spindle localization of TACC3 , similar to S574 , prompting us to investigate the mechanism underlying reduced binding of TACC3F543A to spindles . TACC3 is found in a complex with ch-TOG and clathrin [32 , 33 , 35] . Recent studies suggest that clathrin and TACC3 are mutually dependent for efficient spindle localization , and binding between clathrin and TACC3 requires phosphorylation at S558 by Aurora-A [31–33 , 35] . Therefore , we next asked if reduced spindle localization of TACC3F543A was due to impaired phosphorylation and/or clathrin binding . S588/S574 phosphorylation status was assayed using a phospho ( P ) -TACC3 antibody raised against P-S558 . This antibody is specific , since no signal is detected when TACC3 is immunoprecipitated from S574A and DEL cells ( S8A Fig ) . Like WT , F543A cells contained P-TACC3 albeit at reduced levels . Because immunoprecipitation could introduce a bias , we wanted to further examine the phosphorylation status of TACC3F543A in cytoplasmic cell lysates . By increasing the concentration of lysates to over 5 mg/ml , we were able to detect P-TACC3 in nocodazole-blocked mitotic WT cells and observed a major reduction in P-TACC3F543A levels in F543A cells ( Fig 6A ) . Reduction in P-TACC3F543A was sustained after releasing cells from nocodazole block into the proteasome inhibitor , MG132 , which leads to accumulation of metaphase cells . These findings indicate that the F543A mutations negatively impacts on phosphorylation at S574 both in prometaphase and metaphase . In immunofluorescence analysis , the P-TACC3 antibody exhibited weak spindle pole staining in F543A cells ( S8B Fig ) . Since TACC3F543A showed reduced phosphorylation , we next asked if the F543A mutation also affected the binding and localization of clathrin . Immunoprecipitations of clathrin revealed reduced TACC3 binding both in S574A and F543A cells ( Fig 6B ) . A complete loss of interaction was seen in DEL cells , which lack the CID . Despite the major reduction in P-TACC3 levels in F543A cells , clathrin co-immunoprecipitated with P-TACC3F543A , indicative of intact P-TACC3-clathrin complexes in these cells ( Fig 6C ) . Consistent with these findings , we noted a significant decrease in spindle localization of clathrin in all three mutant lines , but the effect was again greater in S574A and DEL than in F543A cells ( Fig 6D ) . However , even in DEL cells , detectable levels of clathrin remained on the spindle , confirming results from human cells that at least some clathrin can localize to the spindle independently of TACC3 [31–33] . To further interrogate the effect of the F543A mutation on TACC3 protein complexes , we performed sucrose gradient sedimentation using cytoplasmic extracts of WT and F543A cells ( Fig 6E ) . When compared to WT , in F543A cells we noted an accumulation of TACC3 , ch-TOG and Aurora-A , but not clathrin , in fraction 13 that corresponds to protein complexes of ~400–600 kDa . The actual stoichiometry of the individual components is not known , but since TACC3 is likely to form a dimer , we estimate the size of TACC3-ch-TOG-Aurora-A complex to fall within the range of ~350–500 kDa . By contrast , the molecular weight of a complex containing clathrin would be nearer to 1 MDa , if clathrin indeed formed a triskelion in this context . These results suggest that the F543A mutation could instigate a shift towards a TACC3-ch-TOG-Aurora-A complex away from the clathrin-TACC3-ch-TOG-Aurora-A MT-crosslinking complex . Our in vitro data suggested that the F543A point mutation significantly reduced the ability of TACC3 to activate and bind Aurora-A , prompting us to investigate whether TACC3 also plays a key role in activating Aurora-A kinase in vivo . We demonstrated reduced phosphorylation and clathrin-binding of TACC3F543A ( Fig 6 ) , and both these phenotypes could result from a decrease in global Aurora-A activity . Aurora-A kinase is autophosphorylated at T288 of the T loop and this phosphorylation event is widely considered as a surrogate marker for kinase activity [45 , 46] . Thus , we tested the effects of RNA interference-mediated depletion of TACC3 on T288 phosphorylation of Aurora-A in Jurkat lymphocytes . We observed no correlation between TACC3 levels and the extent of T288 autophosphorylation in these cells ( S8C Fig ) . Specificity of antibodies was confirmed by treatment of cells with the ATP-competitive Aurora-A inhibitor , MLN8054 [7] . Similar stainings could not be performed in DT40 cell lines due to lack of antibodies specific to chicken phospho-Aurora-A and our multiple attempts to generate such antibodies also failed . However , we have recently shown that Aurora-A in which T288 has been mutated to alanine still exhibits activity when bound by TPX2 , suggesting that T288 phosphorylation may not be the best surrogate marker for Aurora-A catalytic activity [49] . To assay Aurora-A activity independently of its phosphorylation status , the kinase was immunoprecipitated from extracts of WT , DEL and F543A DT40 cells ( S8D Fig ) . We found no difference between the cell lines in the levels of phosphorylation of a surrogate substrate , MBP . Moreover , Aurora-A localization to the spindle , a process known to depend on TPX2 and Aurora-A kinase activity , was normal in the TACC3 mutant cells ( S8E Fig ) [10 , 15 , 58] . However , in agreement with our in vitro data on human TACC3 , co-immunoprecipitation experiments showed reduced binding between TACC3F543A and Aurora-A ( S8F Fig ) . Taken together , TACC3 is not a major regulator of global Aurora-A activity , but this does not exclude a more local role for the Aurora-A-TACC3 interaction . To assess the functional consequences of the F543A mutation in TACC3 , we examined mitotic spindle morphology in the mutants . TACC3 depletion in human cell lines causes shorter mitotic spindles , a phenotype observed in all three mutant DT40 cell lines ( Fig 7A ) [57] . Spindles were shorter in DEL and S574A than in F543A cells , consistent with more TACC3 being retained on spindle poles in the latter . Since all three mutants show reduced S574 phosphorylation and clathrin binding , Aurora-A-dependent formation of the TACC3-clathrin-ch-TOG MT crosslinking complex could be important for normal spindle length . Alternatively , TACC3 may promote MT stability through associating with ch-TOG , and phosphorylation in this case could help targeting of TACC3 onto the mitotic spindle . Although mitotic spindles were largely intact and bipolar in fixed cells , time-lapse microscopy of DT40 cells expressing GFP-α-tubulin revealed transient abnormal spindle organisation both in S574A and DEL cells ( Fig 7B ) . The category ‘poorly organized spindle' describes mitotic cells that contain a mitotic spindle and additional cytoplasmic MT asters , the latter lasting for 1–2 frames ( 36% in DEL and 16% in S574A ) . The category ‘multipolar spindle' depicts cells with extra spindle poles that are sustained for a minimum of 3 frames ( 15% in DEL and 5% in S574A ) ( Fig 7B ) . Despite these abnormal structures , only a single DEL cell out of 33 exhibited multipolar cell division . Upon disruption of TACC3 function , a mitotic delay has been observed in several model systems [33 , 57 , 59] . Indeed , a marked delay in NEBD-anaphase duration was seen in DEL cells and a slight delay was also detected in S574A cells , but this failed to reach statistical significance ( Fig 7C ) . By contrast , F543A cells exhibited significantly accelerated mitosis , a phenotype confirmed in two distinct F543A cell clones . Similar results were obtained in EB3-GFP-expressing F543A cells ( Fig 7D ) . Reduced mitotic duration can be a consequence of sub-optimal functioning of the SAC , and in human cells this is observed upon loss of activity of the SAC components , Mad2 and BubR1 [13 , 60] . However , multiple lines of evidence suggest that fast mitosis in F543A cells is not caused by a failure of SAC . First , BubR1 accumulates at kinetochores similarly between F543A and WT cells ( S9A Fig ) , and second , F543A cells arrest in mitosis upon treatment with low doses of nocodazole and paclitaxel ( S9B Fig ) . Third , unlike DEL and S574A cells , F543A cells showed no lagging chromatids in anaphase and minimal aneuploidy based on the number of autosomes 1–4 and sex chromosome Z in metaphase spreads ( Fig 7E ) . The most common source for lagging chromatids is merotelic kinetochore attachments , and our results therefore expose a role for Aurora-A-dependent phosphorylation of TACC3 in reducing merotely and thus chromosome missegregation . TACC3 has been reported to localize to the nuclear envelope , and thus we wondered if the nuclear envelope and its cell cycle-dependent regulation are intact in the mutants [61] . However , we found no evidence of impaired coordination of NEBD and chromosome condensation in these cell lines ( S9C Fig ) . Another way to ‘speed up’ mitosis is via regulation of centrosome separation . Centrosome separation that is completed before NEBD shortens the time between NEBD and anaphase onset , accelerates bipolar spindle formation and increases the fidelity of chromosome segregation in HeLa cells [62–64] . Nevertheless , time-lapse imaging revealed no evidence for a function of TACC3 in centrosome separation ( S9D Fig ) . While analyzing these time-lapse experiments , we however noted a difference in the timing of appearance of bipolar spindles in the TACC3 mutant cell lines . It occurred particularly early in F543A cells , only 2 . 2 ± 1 . 3 minutes after NEBD , compared to 3 ± 1 . 8 minutes in WT and 5 . 7 ± 2 . 8 minutes in DEL cells ( Fig 7F ) . Fast bipolar spindle assembly in F543 cells could in part account for the early anaphase onset . Mitotic spindle assembly relies on the rapid formation of MTs generated primarily by centrosome- and chromatin-dependent pathways and to a lesser extent by spindle MT-driven MT nucleation followed by their subsequent organization into a bipolar array [1] . Cooperation of the MT assembly pathways considerably speeds up bipolar spindle assembly and kinetochore capture [1 , 4] . In addition to its role in centrosomal MT assembly , TACC3 has been recently described as a regulator of acentrosomal chromatin-dependent MT formation and kinetochore capture [22] . To assess if TACC3 could bind chromatin-associated MTs in DT40 cells , MTs were depolymerized and regrowth assays were performed . 3 minutes after initiating regrowth , centrosomal MT asters and chromatin-associated MT foci became apparent in WT and mutant DT40 cells ( Fig 8A ) . The sizes of these asters were highly variable and thus we could not decipher genotype-specific changes . By 15 minutes however , unlike DEL and S574A cells that seemed to lag behind WT cells in achieving spindle bipolarity , F543A cells could form robust bipolar spindles as efficiently as WT ( Fig 8B ) . In line with our finding that the mutant TACC3 products co-pelleted with MTs in vitro ( S7E Fig ) , TACC3S574A , TACC3F543A and to a lesser extent , TACC3DEL , all accumulated on nascent centrosomal and chromatin-associated MT foci at the 3-minute time point . However , by 15 minutes after regrowth , they adopted their respective patterns as seen in untreated mitotic cells ( Fig 8A ) . This suggests that TACC3 associates with centrosomal and chromatin-nucleated MTs independent of Aurora-A phosphorylation and clathrin binding , but its retention on MTs requires these factors .
The role of TACC3 in cross-linking and stabilizing k-fibres in a complex with clathrin is now well established [32 , 33 , 35 , 36] . We have dissected the CID of TACC3 , which consists of two distinct regions: a previously reported domain centered on the Aurora-A phosphorylation site at S558 and the di-leucine motif [36]; and a new Aurora-A binding region centered on F525 . In particular , our in vivo studies suggest that the S558 phospho-residue and F525 site play comparable roles both in TACC3 localisation and clathrin binding . Intriguingly , however , the findings that TACC3S574A , TACC3DEL and TACC3F543A are defective in binding clathrin , yet i ) co-sediment with MTs in vitro , ii ) decorate nascent MTs during regrowth experiments and iii ) associate with spindle MTs to varying degrees , collectively argue against an essential role for clathrin in the recruitment of TACC3 to spindle MTs ( Fig 9A ) . This is consistent with reports that unlike depletion of TACC3 , which abolishes clathrin recruitment to spindle MTs , depletion of clathrin causes only a moderate reduction in TACC3 levels on spindle MTs [35] . While TACC3S574A , TACC3DEL and TACC3F543A all showed significantly reduced levels on mitotic spindles , they were nearly as efficient as the wild-type protein to localize to nascent MTs following MT depolymerisation and regrowth . Importantly , all three mutants contain an intact ch-TOG-binding domain within their TACC domains . Our findings therefore suggest that in early prometaphase , TACC3 is loaded onto nascent MTs where it promotes MT growth , possibly via interaction with ch-TOG at MT plus ends , and generates centrosomal and acentrosomal MT asters that facilitate kinetochore capture [16 , 22 , 65 , 66] . This function of TACC3 is likely to be independent of clathrin . However , as mitosis progresses , an increasing number of kinetochores get captured by MTs , sequestering TACC3 along with clathrin and ch-TOG into inter-MT bridges . Aurora-A can be thought of as an incomplete kinase , requiring phosphorylation and co-factors to adopt a fully ordered structure , which is important for its optimal activity [48 , 49] . Recent work suggests that allosteric activation of the kinase by TPX21-43 may activate Aurora-A in the absence of autophosphorylation , raising the possibility that allosteric activation by different co-factors could be employed to fine-tune Aurora-A activity in a temporally and spatially controlled manner [49] . Here we uncover that human TACC3 also acts as an allosteric activator of Aurora-A in vitro in addition to its well-characterized role as a substrate of the kinase . A similar role has been previously described for the Xenopus TACC , Maskin [67] . We mapped this activity on TACC3 to the TACC3act domain and generated a point mutant in human TACC3 ( TACC3 F525A ) that disrupts binding to and activation of Aurora-A in vitro . In chicken cells , the equivalent F543A mutation does not influence the overall levels of Aurora-A activity , yet it impairs TACC3 phosphorylation and localization to the mitotic spindle . While there are alternative explanations for these phenotypes , such as a role for the F543 residue in protecting TACC3 from phosphatase activity , we prefer the explanation that TACC3 is a local activator of Aurora-A , as opposed to TPX2 , which is responsible for overall Aurora-A activity . Our working hypothesis is that TACC3 can activate Aurora-A locally , thereby increasing its own affinity to clathrin and subsequently to kinetochore MTs . How the TACC3-clathrin-ch-TOG MT-crosslinking complex accumulates specifically on k-fibres is still unclear . The need for local Aurora-A activation is likely to arise if there is insufficient autophosphorylated Aurora-A . Phosphorylated Aurora-A is mostly confined to centrosomes and spindle poles [7 , 68] , and at these locations Aurora-A could certainly phosphorylate TACC3 . However , along k-fibres the quantity of phosphorylated Aurora-A might be limiting , perhaps due to the activity of protein phosphatase 6 ( PP6 ) in the cytoplasm [68] . Therefore , a specific need for TACC3 to act as an activator of the kinase might exist on k-fibres . It remains to be established whether this allosteric activation of Aurora-A serves solely to enhance phosphorylation of S558 on TACC3 , or the kinase may have other targets in the vicinity , such as clathrin , ch-TOG , or other proteins associated with spindle MTs . TACC3 has clathrin-independent and clathrin-dependent roles during mitosis , and its phosphorylation by Aurora-A has distinct functions dependent on the context: enhanced centrosome- and spindle targeting to promote MT assembly by the TACC3-ch-TOG complex [26 , 27 , 30 , 69]; and stabilization of the interaction with clathrin , leading to formation of inter-MT bridges on k-fibres [31–34 , 36] . TACC3S574A cannot be phosphorylated , hence this protein is less active both in promotion of MT assembly and formation of inter-MT bridges . By contrast , TACC3F543A can be phosphorylated by TPX2-activated Aurora-A and supports MT assembly via the ch-TOG polymerase , but it cannot locally activate Aurora-A , which results in reduced S574 phosphorylation and weak complexation with clathrin . Consequently , TACC3F543A is mostly absent from clathrin-containing high molecular weight inter-MT bridges and is more abundant as a low molecular weight complex with ch-TOG . F543A cells undergo rapid yet faithful mitosis , approximately 20% faster than wild-type cells . This is an unusual phenotype in the literature , and we are not aware of any reported point mutations with a similar effect . We postulate that this phenotype can be explained by the absence of local Aurora-A activation by TACC3F543A , and the consequent reduction in complex formation with clathrin . In normal cells the more TACC3 is integrated into inter-MT bridges , the less TACC3 is available to promote MT assembly . Thus , MT-crosslinking and stabilization of k-fibres may occur at the expense of rapid MT formation , and thus the overall balance of these two pools of TACC3 could impact on timing and efficiency of bipolar spindle assembly ( Fig 9B ) . In F543A cells reduced binding of TACC3F543A to clathrin , resulting from impaired local Aurora-A activation on k-fibres , would lead to excess TACC3F543A being available for MT formation , which may accelerate bipolar spindle assembly . While excess TACC3S574A is also available in S574A cells , this mutant cannot be phosphorylated by Aurora-A and has , therefore , a more limited ability to stimulate MT production by ch-TOG . Consequently , mutating S574 to alanine in TACC3 does not shorten mitotic duration . Our data also suggests that phosphorylation of TACC3 by Aurora-A is important for mitotic fidelity , although it remains unclear whether it is the MT growth promoting or crosslinking activities of TACC3 that contribute to faithful chromosome segregation . Aurora-A and TACC3 are frequently overexpressed in human cancers , and it has been proposed that this might contribute to tumourigenesis by reducing the fidelity of spindle assembly , thereby increasing the rate of genetic errors [5] . We have identified a binding interface between the two proteins that modulates the rate of spindle assembly in a vertebrate cell line . Interestingly , a number of mutations map to this region in cancer cell lines as well as primary tumours [70] , suggesting that deregulation of Aurora-A and TACC3 interaction might benefit the mitotic fitness of cancer cells . The Aurora-A-TACC3 binding interface could therefore be explored as a potential drug target through the generation of protein-protein interaction inhibitors .
DT40 cells were cultured in suspension in RPMI-1640 medium ( Invitrogen ) which was supplemented with 10% FBS , 1% chicken serum , 110 U/ml penicillin , 10 mg/ml streptomycin and 50 μM β-mercaptoethanol at 40°C with 5% CO2 . Nocodazole ( Sigma-Aldrich ) was used at 1 μg/ml for microtubule regrowth experiment . To induce mitotic arrest DT40 cells were treated with 5 nM of paclitaxel and 100 ng/ml of nocodazole for 6 hours . For immunoprecipitation experiments in Fig 6A–6C and Fig 6E cells were treated with 100 ng/ml nocodazole for 16 hours . Cell lysates were prepared from nocodazole-blocked cells ( Fig 6A ) , or from cells released from nocodazole block for 20 minutes ( Fig 6B and 6E and S8A Fig ) , or from cells that were incubated with 100 nM MG132 ( Sigma ) for 1 hour following release from nocodazole block ( Fig 6A and 6C ) . For Aurora-A kinase inhibition , cells were incubated with 1 μM MLN8054 ( Selleckchem ) for 2 hrs . For live imaging experiments DT40 cells were transfected with GFP-tubulin or EB3-GFP ( a kind gift of A . Akhmanova ) using Neon electroporation system ( Life Technologies ) according to the manufacturer’s instructions . Genomic DNA was extracted using phenol-chloroform followed by ethanol precipitation . 1 μg of extracted DNA was then incubated with 5 U of DNA Degradase Plus ( Zymo ) according to manufacturer's protocol . Analysis was performed using HPLC-MS/MS as described in Bachman et al [56] . Full-length human TACC3 was PCR amplified from cDNA as stated for TACC3 629–838 in earlier work [36] . Full-length TACC3 was subcloned into pET-44C3 to allow expression with a N-terminal NusA-tag . A C-terminal non-cleavable His-tag was appended to the coding sequence of full-length TACC3 by primer extension PCR . The expression construct for TACC3act-H6c was produced by the same method . Truncates of TACC3 were subcloned from full-length TACC3 cDNA into pETM6T1 for expression with an N-terminal His-NusA-tag . Site-directed mutagenesis was carried out to introduce a stop codon at residue 547 within pETM6T1 TACC3act to produce the expression construct for TACC3 519–546 . Point mutations and internal deletion TACC3 constructs were generated by site-directed mutagenesis . The expression construct , pGEX-cs TPX21-43 was produced in previous work [48] and subcloned into pET30TEV for expression with an N-terminal His-tag . The vectors , pETM11 Aurora-A 122–403 , pET30TEV Aurora-A 122–403 D274N ( AurA-DN ) and pET30TEV Aurora-A 122–403 C290A , C393A were generated in previous work [48 , 54] . AurA 1–129 and AurA-DN were subcloned into pGEX-6P1 and pGEX-cs , respectively for expression with a N-terminal GST-tag . The vector , pGEX-2T was used for the expression of GST . Lambda phosphatase was cloned into pCDF-Duet as stated previously [71] . The expression vector for PP1α was a gift from Prof . David Barford ( MRC Laboratory of Molecular Biology , Cambridge , UK ) . All constructs were confirmed by restriction digest and DNA sequencing ( GATC; Eurofins MWG Operon ) . All human TACC3 constructs in pETM6T1 were expressed and purified as previously described [36] . All proteins were either dialysed or subject to size-exclusion chromatography ( SEC ) into 50 mM Tris pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 & 5 mM β-mercaptoethanol prior to storage at -80°C . 15N- and 15N/13C-labelled TACC3act was expressed following the Marley protocol [72] . Cultures were induced with 0 . 6 mM IPTG and incubated overnight at 21°C prior to harvesting . Labelled TACC3act was purified as stated above and subject to SEC on a HiLoad 16/600 Superdex 200 pg column ( GE Healthcare ) equilibrated in 20 mM potassium phosphate pH 7 . 0 , 50 mM NaCl , 1 mM DTT & 0 . 02% ( w/v ) sodium azide ( NMR buffer ) . TPX21-43 was expressed and purified as previously described ( 42 ) . In the final SEC step , the protein was exchanged into NMR buffer . PP1α was produced as stated previously [73] . Aurora-A 122–403 ( alone or co-expressed with lambda phosphatase ) , 122–403 D274N and 122–403 C290A , C393A were expressed and purified as described earlier [48] . Chelating Sepharose was used in replace of TALON resin using conditions recommended by the manufacturer ( GE Healthcare ) . GST-AurA 1–129 , GST-AurA-DN and GST were expressed under the same conditions and purified by affinity chromatography using Gluthathione Sepharose 4B as per the manufacturer’s instructions ( GE Healthcare ) . All Aurora-A proteins were subject to a final SEC step using a HiLoad 16/600 Superdex 200 column ( GE Healthcare ) equilibrated in 20 mM Tris pH 7 . 0 , 200 mM NaCl , 5 mM MgCl2 , 5 mM β-mercaptoethanol and 10% glycerol . For NMR studies , AurA-DN was dialysed into NMR buffer overnight . The protein was concentrated to ~500 μM and 5 mM ADP/MgCl2 was added to increase kinase stability . XTACC3 cDNAs were cloned into pGEX4T-1 and Xenopus Aurora A cDNAs into pET-28a . Recombinant Xenopus proteins were expressed and purified as previously described [15 , 26] . GST-TPX21-39 recombinant protein was expressed and purified as previously described [48] . DT40 cells were lysed in 20 mM sodium phosphate buffer pH 7 . 4 , 150 mM sodium chloride , 2 mM EGTA , 2 mM MgCl2 , 0 . 5% Triton X100 , 1 mM DTT , 20 mM sodium fluoride , 5 μM Microcystin-LR ( Enzo Lifesciences; ALX-350-012 ) and protease inhibitors ( Sigma , P8340 ) followed by centrifugation at 16 , 000g for 15 min at 4°C . Lysates were separated on NuPAGE Novex 4–12% Bis-Tris gels ( Life Technologies ) and transferred onto nitrocellulose membrane for Western blot analysis . For each 200 μl IP reaction , 9 μg of mouse anti-Aurora A ( Sigma , A1231 ) , rabbit anti-clathrin heavy chain ( Abcam , ab21679 ) or rabbit anti-chickenTACC3 ( 60 ) antibody was cross-linked on 30 μl of Dynabeads Protein G ( Life Technologies Ltd , 10003D ) and blocked with 5% BSA in PBS with 0 . 05% Tween20 . Mouse ( Sigma , I5381 ) or rabbit ( Sigma , I5006 ) serum IgG was used as control . DT40 wild-type or TACC3 variant cells were treated with 100 ng/ml of nocodazole for 16 hrs and released for 20 min before harvesting them . Cells were lysed on ice for 15 min in 50 mM sodium phosphate buffer pH 7 . 4 , 150 mM sodium chloride , 2 mM EGTA , 1 mM MgCl2 , 0 . 5% NP40 , 1 mM DTT , 20 mM sodium fluoride , 5% glycerol , 5 μM Microcystin-LR , phosSTOP ( Roche , 04906845001 ) and protease inhibitors followed by centrifugation at 16 , 000g for 15 min at 4°C . The lysates were pre-cleared with 50 μl of Dynabeads Protein G for 1 hr on a rotary mixer at 4°C . 600 μg of total protein was used per reaction and binding was carried out at 4°C for 1 hr on a rotary mixer . The beads were washed in the above buffer and bound proteins were eluted using 25 μl of 100 mM glycine pH 2 . 2 followed by neutralization with 5 μl of 1M Tris pH 8 . A quarter of the eluted sample was loaded on the gel and processed for Western analysis using mouse anti-clathrin heavy chain ( BD Biosciences , 610500 ) , mouse anti-Aurora A , rabbit anti-chicken TACC3 and rabbit phospho-TACC3 ( S558 ) ( Cell Signaling , 8842 ) antibodies . HRP-conjugated goat anti-mouse or anti-rabbit ( Dako , P0447 and P0448 , respectively ) secondary IgGs were used and proteins were detected by chemiluminescence with ECL Western blotting substrate ( Thermo , 32106 ) or SuperSignal West Femto ( Thermo , 34094 ) . 100 μg GST-AurA 1–129 , GST-AurA-DN or GST was immobilized on 20 μl Gluthathione Sepharose 4B ( GE Healthcare ) beads equilibrated in 50 mM Tris pH 7 . 5 , 150 mM NaCl , 5 mM MgCl2 , 5 mM β-mercaptoethanol and 0 . 02% TWEEN 20 . The proteins were incubated with resin for 2 hrs at 4°C . The resin was pelleted by centrifugation and washed twice with buffer . The beads were resuspended in buffer to which 100 μg TACC3 or TPX21-43 was added and incubated for a further 2 hrs at 4°C . The reactions were washed twice with buffer prior to the addition of 20 μl SDS-loading buffer . The reactions were separated by SDS-PAGE . Western blots were performed using an anti-His6-tag antibody ( 1:5000 dilution , Clontech ) . For co-precipitation assays performed with a gradient of 0–50 μM His6-TPX21-43 , 2 μM GST-AurA-DN was immobilized on Gluthathione Sepharose 4B beads and 5 μM TACC3act-H6c was used in reactions and performed as above . High concentrations of Aurora-A 122–403 can be easily produced for crystallization trials . However , difficulties were encountered when trying to generate a sample of the kinase in a buffer amenable to NMR spectroscopy at a high protein concentration ( ~500 μM ) for interaction studies with TACC3act . NMR studies were in progress with labelled TACCact in 20 mM potassium phosphate pH 7 . 0 , 50 mM NaCl , 1 mM DTT and 0 . 02% sodium azide ( NMR buffer ) . The absence of glycerol in the NMR buffer resulted in significant kinase precipitation within hours at room temperature making this unsuitable for NMR spectroscopy as data collection occurs over a number of days . The high level of precipitation upon exchange of Aurora-A 122–403 into NMR buffer meant this had to be carried out by dialysis rather than SEC . A comparison of the stability of Aurora-A 122–403 and Aurora-A 122–403 D274N in NMR buffer showed the latter exhibited less precipitation and was selected for further optimisation . NMR buffer trials investigating the effect of salt concentration ( 50–150 mM ) did not reduce kinase precipitation . The effect of nucleotide on kinase stability was investigated by comparing samples incubated at room temperature in the presence of 5 mM adenosine/MgCl2 , 5 mM ADP/MgCl2 or no additive . These nucleotides were selected as they are frequently used in the crystallization of Aurora-A and would likely improve the stability of the kinase by binding in the ATP-binding pocket of the protein . The addition of nucleotide significantly improved the stability of the kinase . ADP/MgCl2 was selected over adenosine/MgCl2 as the kinase was stable at a higher concentration for a longer period . All spectra were recorded on Bruker Avance spectrometers at 500 and 700MHz fitted with TCI type cryoprobes . The assignment data for TACC3act were obtained using watergate 15N HSQC 2D , 15N resolved 3D NOESY-HSQC and 3D TOCSY-HSQC spectra recorded on a sample of 0 . 8 mM 15N-labelled TACC3act in NMR buffer at 298K . These were supplemented with 3D watergate HNCACB , HNCO as well as 2D 13C gradient coherence selection sensitivity enhanced HSQC , CAN , CON and ( H ) NCO experiments recorded on a 0 . 35 mM sample of 15N/13C-labelled TACC3act . Interaction of 15N-labelled TACC3act with unlabelled AurA-DN was monitored by recording 15N watergate HSQC or TROSY 2D experiments at temperatures of 283 , 298 and 303K and fields of 500 and 700 MHz in NMR buffer augmented with 5 mM ADP/MgCl2 . The HSQC/TROSY experiments were supplemented with 13CO detected NCO and ( H ) NCO experiments to combat exchange broadening observed for TACC3act 1H resonances in the TACC3act/AurA-DN complex . The concentration range for TACC3act was 100–300 μM and 150–600 μM for AurA-DN . The ternary complex of TACC3act , AurA-DN and TPX21-43 was characterised by 2D watergate 15N HSQC , NCO and ( H ) NCO experiments . To measure binding affinities , titrations were performed with the concentration of TACC3act kept constant at 75 μM and AurA-DN or the complex of AurA-DN/TPX21-43 added at concentrations of 0 , 22 . 5 , 45 , 75 , 150 and 225 μM . All pulse sequences were used as supplied by the spectrometer manufacturer ( Bruker ) with some in-house modifications . Spectra were processed with Topspin 3 . 0 and analysed with CCPN analysis 2 . 1 . 5 [50] . Sequence specific assignments were obtained using standard procedures in conjunction with 15N resolved 3D and triple resonance spectra . Amino acids were identified using a combination of peak patterns in TOCSY spectra and 13C chemical shifts . Secondary chemical shifts were calculated within CCPN analysis using the default reference chemical shift library . Binding affinities of TACC3act for AurA-DN and AurA-DN/TPX21-43 were extracted from fitting plots of chemical shift changes for selected amino acids against the concentration of added AurA-DN or AurA-DN/TPX21-43 complex . Fitting was performed using a standard binding isotherm ( Eq 1 ) in R [74]: fb=b* ( ( v+c+k ) -sqrt ( abs ( ( v+c+k ) ^2−4*v*c ) ) ) / ( 2*c ) ( 1 ) In Eq ( 1 ) , the fraction of bound protein is represented by fb , b indicates Bmax , the maximum fraction bound , v is the observed fraction bound and c is the concentration of TACC3act used . k is the dissociation constant , Kd . The affinity of Aurora-A to binding partners was quantified by measuring the ratio of unbound Aurora-A:complex at a range of concentrations using MST . This method utillises the differential mobility of a fluorescently-labelled protein and its complexes along a temperature gradient [75] . Aurora-A 122–403 C290A , C393A was labelled with NT-647-NHS dye using a Monolith NT Protein labelling kit as per the manufacturers’ instructions ( NanoTemper Technologies GmbH ) . The kinase was eluted in 50 mM Tris pH 7 . 6 , 150 mM NaCl , 10 mM MgCl2 , 5 mM β-mercaptoethanol and 0 . 05% TWEEN 20 ( MST buffer ) and used at a final concentration of 50 nM . TACC3act constructs were dialysed into MST buffer and used in binding assays at concentrations ranging from 0 . 2–3950 μM , which were produced by serial dilution of the TACC3act stock . TACC3act and labelled Aurora-A reactions were incubated for 30 mins in the dark at room temperature prior to measurement . Reactions were loaded into Monolith NT Hydrophilic glass capillaries and MST measurements were recorded using a Monolith NT . 115 ( NanoTemper Technologies GmbH ) at 50% LED power and 20% MST power at 25°C . Each capillary was read 3 times . Baselines were subtracted from Temperature Jump data and normalized to fraction bound . Data were averaged for the 3 readings and fit to a one-site specific binding equation ( Eq 2 ) in Prism 6 ( GraphPad ) to calculate Kd: y=Bmax*x/ ( Kd+x ) ( 2 ) In Eq ( 2 ) , y is the fraction of TACC3act bound to Aurora-A , Bmax is the maximum fraction bound , x is the concentration of TACC3act and Kd is the dissociation constant . To generate the DEL , F543A and S574A cells sequential gene targeting was employed to edit both alleles of Tacc3 , which involved homologous recombination of antibiotic resistance genes into intronic regions of the Tacc3 gene . Cre-lox recombination was then used to excise these cassettes from homozygously targeted cell lines . To make the targeting construct for generating TACC3 variant cells , left and right homology arms were PCR amplified from DT40 genomic DNA . Please refer to S1 Table for the sequence of oligos . TACC3-S574A: The left arm was amplified by nested PCR and primer extension by TaKaRa LA Taq polymerase ( Clontech ) using primers SA-LA-fwd , S574A-mutR , S574A-mutF and SA-LA-rev . The amplified PCR product was cloned into pGEM-T vector ( Promega ) , then released as a NotI-SpeI fragment which was then sub-cloned into the same restriction sites in pBluescript II SK ( - ) containing selection markers ( neomycin or blasticidin resistance cassette flanked by LoxP and BamHI sites ) . The PCR amplified right arm using Phusion DNA polymerase ( New England Biolabs ) with primers SA-RA-fwd and SA-RA-rev was then inserted as a blunt-end fragment into the above construct that was digested with SmaI . The neomycin resistance cassette in the final construct was then swapped with a puromycin resistance cassette by BamHI digestion . TACC3-F543A: The left arm was PCR amplified by Phusion DNA polymerase using primers FA-LA-SalI-fwd and FA-LA-BamHI-rev followed by cloning into pJET1 . 2 as blunt-end fragment . Mutagenesis ( F543A ) was carried out on this construct by inverse PCR using primers F543A-mutF and F543A-mutR that also incorporate a NheI restriction site through silent mutation . The right arm was PCR amplified using primers FA-RA-BamHI-fwd and FA-RA-NotI-rev followed by cloning into pJET1 . 2 . The final construct was prepared by sequential sub-cloning of SalI-left arm-F543A-BamHI followed by BamHI-right arm-NotI and finally neomycin or puromycin resistance cassette as a BamHI fragment . TACC3-DEL: Primers used to amplify the left arm were DEL-LA-KpnI-fwd and DEL-LA-BamHI-STOP-rev and the right arm were DEL-RA-BamHI-fwd and DEL-RA-NotI-rev . Homology arms were cloned into pBluescript II SK ( - ) , with the left arm between KpnI and BamHI sites and the right arm between BamHI and NotI sites . Selection markers ( puromycin or blasticidin resistance cassette ) were cloned into these vectors between BamHI sites . Restriction digestions and DNA sequencing confirmed all the constructs . Two rounds of targeting to modify the two alleles were performed by electroporation using a gene pulsar ( Bio-Rad Laboratories ) . In brief , 60 μg of linearized DNA was mixed with 2 × 107 cells in chilled PBS in a 4-mm cuvette and electroporated at 550 V and 25 μF , followed by dilution into six 96-well plates . After 24 h , antibiotics were added for 7–10 days at the following concentrations: 1 . 5 mg/ml neomycin ( Invitrogen ) , 0 . 5 μg/ml puromycin ( Acros Organics ) , and 50 μg/ml blasticidin ( Acros Organics ) . Antibiotic-resistant clones were expanded , and genomic DNA was extracted . Targeted integrations were screened by PCR amplification using the primers given in S1 Table ( see S4–S6 Figs ) . Positive clones were subsequently electroporated with Cre-recombinase encoding plasmid to excise out the antibiotic resistance cassettes from the gene alleles . cDNA sequencing validated the final clones . Cytosolic-factor-arrested Xenopus egg extracts ( CSF extract ) and GST-pull downs were performed as previously described [26] . In short , 20 μl protein A-conjugated Dynabeads 280 ( Invitrogen ) were washed 3 times with PBS-Triton ( 0 . 1% ) , incubated for 30 mins at room temperature with anti-GST antibodies and washed twice with PBS-Triton ( 0 . 1% ) and twice with CSF-XB ( 10 mM Hepes , 100 mM KCl , 0 . 1 mM CaCl2 , 3 mM MgCl2 , 50 mM Sucrose , 5mM EGTA pH to 7 . 7 with KOH ) . Beads were then incubated for 60 mins on ice with 60 μl of CSF-egg extract preincubated with 3 μM of recombinant protein for 15 mins at 20°C . Beads were retrieved and washed twice with CSF-XB and twice with PBS-Triton ( 0 . 1% ) , in the presence of phosphatase inhibitors . Proteins were eluted with Laemmli buffer and analyzed by Western blot . For in vitro GST-pull downs , anti-GST coated beads prepared as above were incubated with 5–10 μg of GST recombinant proteins . Beads were retrieved , washed and incubated with 0 . 25 μM His-AurA in kinase buffer ( 20 mM Hepes pH 7 . 5 , 200 mM KCl , 30 mM MgCl2 , 0 . 5 mM EGTA , 1 mM DTT , 0 . 05% Triton X-100 , 0 . 2% β-mercaptoethanol , and 1 mg/ml BSA ) for 30 mins at 22°C . Beads were retrieved and washed three times with PBS , 0 . 5% Triton X-100 , 0 . 25 mM Na3VO3 , 10 mM NaF . Proteins were eluted with Laemmli buffer and analyzed by Western blot . Multiple sequence alignments of Homo sapiens ( NCBI accession code: NP_006333 ) , Gallus gallus ( NCBI accession code: NP_001004429 . 2 ) , Xenopus laevis ( NCBI accession code: NP_001081964 . 1 ) and Mus musculus ( NCBI accession code: NP_001035525 . 1 ) TACC3 were performed using ClustalW2 [76] . Kinase assays performed with recombinant TACC3 consisted of 0 . 625 μM Aurora-A 122–403 , 5 μM TACC3 and/or 0 . 25 mg/ml MBP ( Sigma ) . Reactions were carried out in kinase buffer ( 20 mM Tris pH 7 . 5 , 25 mM NaCl , 1 mM MgCl2 , 1 mM DTT & 0 . 01% TWEEN 20 ) . Reactions were initiated by the addition of 32P-ATP ( Perkin Elmer ) , incubated at room temperature for 10 mins and were analysed by either SDS-PAGE followed by autoradiography or scintillation counting , in which case reactions were stopped by the addition of 2% orthophosphoric acid ( Sigma ) . Samples were transferred onto P81 paper ( Whatman ) , unincorporated 32P-ATP removed by extensive washing with 0 . 2% orthophosphoric acid and incorporation of 32P quantified . Statistical analysis was carried in Prism ( Graphpad Software , Inc . ) . One-way ANOVA was performed to identify significant changes followed by Dunnett’s post-hoc analysis . Kinase reactions for analysis by quantitative immunofluorescent Western blot were performed with cold ATP only . Analysis was carried out as per the manufacturer’s instructions ( LI-COR GmbH ) using an anti-phospho-S558-TACC3 antibody ( 1:1000 dilution [77] ) and IRDye 800CW secondary antibody ( LI-COR GmbH ) . Blots were resolved using Image Studio on an Odyssey CLx Infrared imaging system ( LI-COR GmbH ) . Statistical analysis was carried in Prism ( Graphpad Software , Inc . ) . One-way ANOVA was performed to identify significant changes followed by Dunnett’s post-hoc analysis . To analyse endogenous Aurora-A activity in WT , F543A and DEL cells , Aurora-A was immunoprecipitated from the corresponding cell extracts . Beads were retrieved on a magnet , washed twice with 500 μl of lysis buffer ( 20 mM sodium phosphate buffer pH 7 . 4 , 150 mM sodium chloride , 2 mM EGTA , 2 mM MgCl2 , 0 . 5% Triton X100 , 1 mM DTT , 20 mM sodium fluoride , 5 μM Microcystin-LR ( Enzo Lifesciences; ALX-350-012 ) and protease inhibitors ( Sigma , P8340 ) ) and twice with kinase buffer and the assay was performed as described above . In vitro kinase assays were performed ( as above with cold ATP ) with Aurora-A 122–403 co-expressed with lambda phosphatase alone and on addition of TACC3act and TPX21-43 . Reactions were quenched by the addition of SDS-loading buffer and separated by SDS-PAGE . Western blots were performed using an anti-Phospho-Thr288 Aurora-A antibody ( 1:1000 dilution , Cell Signaling Technology ) . 50 μM Aurora-A 122–403 was incubated with 0 . 1 μM PP1α alone and in the presence of 10 μM TACC3act or TPX21-43 for 1 hr at room temperature . Assays were carried out in 50 mM Tris pH 7 . 5 , 0 . 1 mM EDTA , 2 mM MnCl2 , 5 mM DTT and 0 . 025% TWEEN 20 . Reactions were stopped by the addition of SDS-loading buffer and separated by SDS-PAGE . Western blots were performed using an anti-Phospho-Thr288 Aurora-A antibody ( 1:1000 dilution , Cell Signaling Technology ) . DT40 cells were treated with 100 ng/ ml of nocodazole for 16 hrs and released for 20 min before harvesting . Cells were lysed on ice for 15 min in 50 mM sodium phosphate buffer pH 7 . 4 , 150 mM sodium chloride , 2 mM EGTA , 1 mM MgCl2 , 0 . 5% NP40 , 1 mM DTT , 20 mM sodium fluoride , phosSTOP ( Roche ) and protease inhibitors ( Sigma ) followed by centrifugation at 16 , 000g at 4°C for 15 min . A 5 to 40% ( w/v ) step sucrose gradient ( 900 μl each ) was prepared in the above buffer and layered on 900 μl of a 2M sucrose cushion ( approx . 70% ) in Beckman Coulter Ultra-Clear 14 ml tubes . 5 mg of total cell lysate in 900 μl was loaded on the gradient and subjected to ultracentrifugation using SW40Ti rotor at 23 , 700 rpm for 16 hr at 4°C in a Beckman Coulter Optima L-100 XP ultracentrifuge . 450 μl fractions were collected and TCA-precipitated before being subjected to western blot analyses . Primary antibodies used in this study were anti-CDK5RAP2 [78] , anti-TACC3 against aa 126–442 of Gallus gallus TACC3 [78] , anti-phospho-S558-TACC3/P-TACC3 ( Cell Signaling ) , anti-ch-TOG ( QED Bioscience , 34032 ) , anti-Clathrin Heavy Chain ( Abcam; 21679 and BD Biosciences , 610500 ) , anti-Aurora-A ( 35C1; Sigma ) , anti-α-tubulin ( Dm1α; Sigma ) , anti-α-tubulin-FITC ( Sigma ) , anti-γ-tubulin ( GTU88; Sigma ) anti-phospho-Histone H3 ( Millipore ) , anti-BubR1 ( kind gift of W . Earnshaw ) and phospho-Aurora-A/B/C ( Cell Signaling ) . For visualization of mitotic spindles and centrosomal proteins , cells were fixed and immunostained as described in [79] . P-TACC3 antibody staining was carried out in cells fixed in 4% paraformaldehyde ( PFA ) in PBS . For clathrin antibody , cells were fixed in warm 3% PFA in PHEM buffer ( 60 mM Pipes , 25 mM Hepes , 10 mM EGTA , 2 mM MgCl2 pH 6 . 8 ) for 15 mins , followed by extraction with PBS/0 . 5% TritonX-100 for 15 mins . Imaging of fixed cells was performed on a scanning confocal microscope ( Eclipse 90i; Nikon , Leica SP5 or Nikon A1 ) . Cells were mounted in anti-fade medium ( ProLong Gold or Mowiol 4–88 ) and imaged with 100X , 1 . 4 NA objective ( Nikon ) or 60X , 1 . 4 NA objective ( Nikon or Leica ) . Images presented here are 3D projections of z sections taken every 0 . 5 μm across the cell . Images of any individual figures were acquired using the same settings and were imported into Volocity 6 . 3 ( Perkin Elmer ) or Photoshop CS6 ( Adobe ) . Time-lapse images of DT40 cells expressing GFP–α-tubulin or EB3-GFP were acquired as described in [79] . Images were acquired every 3 min for 2–3 hrs . For quantifications of TACC3 spindle intensity and spindle length , non-saturated images of randomly selected mitotic cells were taken using identical acquisition settings . Images were uploaded in Volocity and 3D measurements were performed using analysis protocols allowing batch processing . For TACC3 and clathrin intensity quantification , fluorescence intensities were measured within two half spindle volumes as determined by tubulin fluorescence . The 3D object definition was improved using de-noising and size filters . TACC3 and clathrin intensity values were normalized against tubulin intensity . For spindle length quantification , the centrosome distance was measured in mitotic cells using a fully automated protocol . Three populations of 3D objects were defined as “nuclei” , “mitotic” and “centrosomes” and detected using DNA , phospho-histone H3 and γ-tubulin staining , respectively . The proper segmentation of the nuclei was achieved using the “separate touching object” tool . Fine filter de-noising was used to improve definition of phospho-histone H3 objects . We then used the compartmentalizing tool to associate “mitotic” and “centrosomes” objects to individual “nuclei” objects in order to only measure the 3D distance between centrosomes belonging to the same cell . As false positive or false negative objects could be detected within the “centrosomes” population , we filtered the results to only consider nuclei associated with exactly two centrosomes . For the microtubule pelleting experiment , cells were lysed in 50 mM Tris pH 7 . 4 , 5 mM MgCl2 , 0 . 1 mM EGTA and 0 . 5% Triton X-100 ( to a final total protein of 2 . 5 mg ) . Cell lysate was incubated for 5 min at 30°C followed by pre-clearing by centrifugation at 70 , 000 rpm for 10 minutes at 40°C . Paclitaxel-stabilized microtubules were prepared in BRB80 buffer ( 80 mM PIPES-K pH 6 . 8 , 1 mM EGTA , 1 mM MgCl2 ) and added to the cell lysate ( 250 ng ) . In control samples the same amount of tubulin was added without paclitaxel . After 30 minutes at 30°C , the samples were centrifuged at 60 , 000 rpm for 20 min at 30°C in a TLA-100 rotor through a 1 M sucrose cushion in BRB80 buffer . The pellets were washed three times with warm BRB80 and analyzed by Western blotting . For microtubule regrowth experiments , cells were treated as described in [79] . Cells on coverslip were fixed for immunostaining after incubation for 3 and 15 min at 40°C to polymerise microtubules . DT40 cells were processed as described in [79] . Jurkat E6 . 1 cell line was grown in RPMI1640 containing 5% FBS supplemented with penicillin and streptomycin . TACC3 shRNA constructs were designed , prepared and transduced into Jurkat cells as described in [80] . shRNAs sequences are shown in S1 Table . The shRNA encoding fragments were cloned into XhoI-EcoRI sites in MSCV-miR30-puro vector . Statistical analysis and graphs were carried out using Prism ( Graphpad Software , Inc . ) . Numbers of experimental repeats ( n values ) are reported for each dataset in figures and figure legends . T-test was performed on all data with normal distribution . When normal distribution could not be confirmed , the non-parametric Mann-Whitney test was used . | Maintenance of genomic fidelity depends on the faithful division of chromosomes by the mitotic spindle , a molecular system comprising microtubules and associated proteins . The timely establishment of a functional bipolar spindle requires co-operation between several assembly pathways , coordinated by protein kinases such as Aurora-A . A key substrate of Aurora-A in mitosis is TACC3 . Phosphorylation of TACC3 triggers its binding to clathrin to form a complex that promotes crosslinking and stabilization of spindle microtubules . We identify a new binding interface between Aurora-A and TACC3 that is crucial for assembly of TACC3-clathrin complexes and therefore proper spindle localization of TACC3 . Disruption of this interface results in an accelerated yet faithful mitosis . We conclude that mutual regulation between Aurora-A kinase and its substrate TACC3 constitutes a molecular switch between different spindle assembly pathways , thereby influencing the speed of spindle formation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Aurora-A-Dependent Control of TACC3 Influences the Rate of Mitotic Spindle Assembly |
Understanding the mechanisms that promote the assembly and maintenance of host-beneficial microbiomes is an open problem . Empirical evidence supports the idea that animal and plant hosts can combine ‘private resources’ with the ecological phenomenon known as ‘community bistability’ to favour some microbial strains over others . We briefly review evidence showing that hosts can: ( i ) protect the growth of beneficial strains in an isolated habitat , ( ii ) use antibiotics to suppress non-beneficial , competitor strains , and ( iii ) provide resources that only beneficial strains are able to translate into an increased rate of growth , reproduction , or antibiotic production . We then demonstrate in a spatially explicit , individual-based model that these three mechanisms act similarly by selectively promoting the initial proliferation of preferred strains , that is , by acting as a private resource . The faster early growth of preferred strains , combined with the phenomenon of ‘community bistability , ’ allows those strains to continue to dominate the microbiome even after the private resource is withdrawn or made public . This is because after a beneficial colony reaches a sufficiently large size , it can resist invasion by parasites without further private support from the host . We further explicitly model localized microbial interactions and diffusion dynamics , and we show that an intermediate level of antibiotic diffusion is the most efficient mechanism in promoting preferred strains and that there is a wide range of parameters under which hosts can promote the assembly of a self-sustaining defensive microbiome . This in turn supports the idea that hosts readily evolve to promote host-beneficial defensive microbiomes .
Vertical and pseudo-vertical transmissions fall into this category [1 , 19–23] . In strict vertical transmission , host germline cells are infected with symbionts [22 , 24] . Less strict transmission ( ‘pseudo-vertical’ ) is achieved by keeping non-colonised host offspring in isolation after birth until the parental microbiome can colonise it , which then shapes the composition of subsequent colonists from the environment [9 , 11 , 22] . In either case , the host ensures a competitor-free space for inherited microbes , which are allowed time and resources to grow on a new-born host before being exposed to competition with other colonists . For example , newly emerging Acromyrmex leafcutter ants are inoculated with antibiotic-producing Pseudonocardia bacteria within a 24-hour window after hatching [8 , 25] . Mature worker ants serve as the source by carrying Pseudonocardia on their propleural plates , which grow to a high density around specialised exocrine glands that likely provide nutrients for bacterial growth [26 , 27] ( thus also serving as an example of a resource that can be metabolized by the preferred bacteria , discussed in 3 . below ) . Similarly , female beewolf digger wasps ( Philanthus , Philanthinus , Trachypus ) inoculate their brood cell walls with a species of Streptomyces that they maintain in their antennal glands [28–30] . These bacteria become directly incorporated into the larval cocoon , where they dominate and produce an array of antibiotics that protect the developing larva against infection [29–31] . Analogous to the above examples , the agricultural process of applying bacteria , such as antibiotic-producing Pseudomonas and nitrogen-fixing Rhizobia , to crop seeds before sowing mimics pseudo-vertical-transmission , by ensuring that high densities of beneficial bacteria have better access to root exudates and are favoured during establishment on the plant [32 , 33] . Priority effects have also been demonstrated for mycorrhizae [34] , bees [35–38] , wasps [28] , leafcutter ants [25 , 39] , birds [40] , plants [41] , and humans [42] . A unique structure for symbiont transmission , called a ‘‘symbiont capsule , ” which serves as a private space and resource , has been described for the stinkbug Megacopta punctatissima [43–45] . These capsules are deposited next to the eggs and provide food and protection for the symbionts until the hatchlings open the capsules and ingest the symbionts [44 , 45] . A wide range of plant species secrete compounds , known as allelochemicals , which are toxic to a broad range of bacteria , fungi , and invertebrates in the rhizosphere , as well as toward other plants growing nearby [46–49] . For example , the compound 2 , 4-dihydroxy-7-methoxy-1 , 4-benzoxazin-3-one ( DIMBOA ) is an antimicrobial produced by maize seedlings [48] , which the plant-beneficial species Pseudomonas putida is able to degrade , thus avoiding its effects . P . putida also uses this compound as a chemoattractant and a signal for upregulating the production of the broad-spectrum antibiotic phenazine [48] . Together , these mechanisms allow P . putida to colonise maize roots in the presence of mostly DIMBOA-intolerant , competitor bacteria [48] . Similarly , the rhizobial species , Mesorhizobium tianshanense , which forms root nodules on liquorice plants , is able to outcompete other bacteria in the rhizosphere due to an efflux mechanism that confers resistance to the antimicrobial compound canavanine . Canavanine is abundant in liquorice root exudates and thus allows the host to filter out non-beneficial rhizobial species [50] . As another example , nitric oxide ( NO ) , a potent oxidising agent and antimicrobial , can play an important role in dictating symbiont specificity [51 , 52] . A classic example arises during the symbiosis between the bobtail squid , Euprymna scolopes , and bioluminescent bacteria in the species Vibrio fischeri . V . fischeri are the exclusive colonisers of the squid’s light organ , where they emit light to deceive predators , and are acquired horizontally from the environment within 48 hours after squid hatching [53] . High nitric-oxide synthase ( NOS ) activity and its product NO can be detected in the epithelial mucus of the light organ during the early stages of bacterial colonisation [54] , which V . fischeri are able to tolerate via the activity of two proteins , flavohemoglobin ( Hmp ) and a heme NO/oxygen-binding protein ( H-NOX ) [55–58] . Eliminating the genes for these proteins in V . fischeri leads to colonisation deficiency [56 , 58] , and diminishing the concentration of host NO results in a greater diversity of non-mutualistic bacterial species in the light organ epithelium [54] . Similar mechanisms of host selection are also reported for other animal species . For example , members of the Hydra family produce antibacterial arminins that help them to shape the establishment of the bacterial microbiota during their embryogenesis [59] . Hydra not only suppresses undesired strains [59] but also modifies the quorum-sensing signals by which bacteria communicate , hence manipulating the social behaviour of bacteria [60] . Enhanced metabolic activity from consuming a private resource can confer competitive superiority on a preferred microbial strain . Besides acquiring higher reproduction and growth rates , the beneficial bacteria can also achieve a higher rate of antibiotic production that results in the suppression of competitors [61] or achieve a higher production of other factors that promote colonization and symbiotic interaction with the host , such as adhesive molecules facilitating biofilm formation on the host surface [62 , 63] . The provision of specific metabolites is thought to play a key role in structuring the species-specific microbial communities associated with marine corals [64 , 65] . Coral juveniles , as well as their dinoflagellate symbionts , produce large quantities of the compound dimethylsulfoniopropionate ( DMSP ) [66] . In vitro and metagenomic studies have shown that several coral-associated bacterial groups can specifically metabolise the DMSP and use it as a sole carbon and sulphur source [64 , 65 , 67] . Such species are also amongst the first bacteria to colonise coral larvae , suggesting a nutritional advantage for them over bacteria that cannot degrade DMSP [64 , 68] . This includes a species of Pseudovibrio which can additionally use DMSP as a precursor for the production of antibiotics that inhibit coral pathogens [65] . Another example of a specific host-derived resource is human breast milk , which contains a large number of complex oligosaccharides that are preferentially consumed by a single species of co-adapted gut bacterium Bifidobacterium longum subsp . infantis [69] . In plants , experiments have shown that root exudates can be directly metabolised by the microorganisms that live endophytically within the plant roots [70–73] . Different species exude different groups of metabolites , and studies suggest that plant hosts may be able to tailor root exudate composition in order to recruit bacteria with particular metabolic traits [46 , 70 , 73] . For example , the concentration of the plant phytohormone salicylic acid ( SA ) has been shown to correlate with the abundance of several bacterial taxa , including the antibiotic-producing genus Streptomyces [73 , 74] , which can use SA as a sole carbon source [74 , 75] . As discussed earlier , leaf-cutter ant exocrine glands , which provide a nutrient source for Pseudonocardia bacterial growth , also fall into this category [26] . These mechanisms achieve one of two effects: ( I ) they either ensure the protected growth of the preferred strains and/or ( II ) they enhance the competitive abilities of preferred strains against non-preferred strains , for example by increasing the rate of antibiotic production or the rate of growth of the beneficial strain , for certain duration of time . Taken together , these examples show that hosts have access to multiple mechanisms that can provide a ‘private resource’ , in the form of space and/or food , to a subset of bacterial strains , and if those strains are beneficial to the hosts , the host is selected to apply one or more of these mechanisms to assemble host-beneficial microbiomes . However , once the private resource is withdrawn , the host becomes a public habitat on which a diversity of microbes can thrive , either feeding on generally available resources coming from the host ( for example , secretions , excretion , or dead epithelium ) or from the physical environment . The question therefore is whether and how a time-limited private resource can be translated into a persistent host-beneficial microbiome . To answer this question , we now abstract these mechanisms into an individual-based , spatially explicit model of host-associated defensive microbiomes ( Fig 1A and 1B ) ( reviews in 9 , 29 , 30 ) , which typically contain antibiotic-producing bacteria [76 , 77] . In our model , dispersal and direct competition for empty sites is limited to small numbers of neighbouring individuals , in accordance with experimental results [78] . At the same time , due to diffusion , indirect , antibiotic-mediated competition can occur amongst distant bacteria . We show that a host is indeed able to assemble a defensive microbiome , by providing a private resource that has the effect of exploiting the community bistability which emerges when bacterial species engage in interference competition [9] . We also show that the host only needs to provide the private resource until the beneficial microbe’s colony reaches a self-sustaining size , that bacterium-produced antibiotic defends the colony most effectively at an intermediate level of diffusion rate , and that the antibiotic-efflux resistance mechanism is the most efficient mechanism for achieving competitive superiority . We focus our modelling on the community dynamics of the bacteria , and therefore we only model the host indirectly . This is because bacterial community dynamics play out much more quickly ( hours to days ) than does the coevolutionary response of a host lineage to the fitness consequences of its achieved microbiomes . In other words , a host might evolve a new private-resource trait that changes the trajectory of microbiome assembly , which then affects host fitness and either selects for or against that new trait . Our focus is on the first half: how differences in the host-provisioning of private resources affect microbiome assembly , which is not well understood . We also simplify the modelling by binning multiple bacterial species into two archetypes , beneficial and parasitic , because we are interested in whether ( any number of ) beneficial species can coexist with or even competitively exclude ( any number of ) parasitic species . The same approach has long been used in community ecology , such as in modelling the coexistence of pioneer vs . shade-tolerant trees and superior competitors vs . superior dispersers [e . g . 79–81] . Typically , once two types can be shown to coexist , subsequent modelling shows that the same coexistence mechanism can be extended to allow the coexistence of multiple species [e . g . 82] , or additional mechanisms can be invoked . Our take-home message is that there is a wide range of conditions under which hosts can successfully promote the assembly of a self-sustaining defensive microbiome , which , in turn , supports the general idea that hosts can readily evolve to promote host-beneficial defensive microbiomes .
The beneficial strain produces and exports antibiotic at rate ρB , into the extracellular environment , resulting in a distribution of concentrations AExt ( i , t ) at position i at time t . The molecules are taken up by the cells at rates αB and αP ( αB≤αP ) by the B and the P strains , respectively , resulting in an AInt ( i , t ) interior concentration within the cell at position i at time t . The cells decompose the intracellular antibiotics at rates γB and γP ( γB≤γP ) , and they can also perform active outbound transport , i . e . controlled efflux , to release intracellular antibiotics at rates βB and βP ( βB≤βP ) . The antibiotics decay at rate φ in the environment . The model implements the three major antibiotic-resistance mechanisms: ( a ) reduced influx through the membrane ( αB ) , ( b ) a higher rate of intracellular decomposition and neutralisation ( γB ) , and ( c ) increased efflux of the molecules ( βB ) , and combinations of these mechanisms [76 , 83–86] . We first assume that the antibiotic molecules are point-like particles moving on a host-surface plane . Consequently , we can use reaction-diffusion dynamics to describe change in the extracellular antibiotic concentration AExt ( x , t ) at points x = ( x , y ) ( representing the coordinates on a surface ) and time t ∂AExt ( x , t ) ∂t=D ( ∂2AExt ( x , t ) ∂x2+∂2AExt ( x , t ) ∂y2 ) +F ( AExt ( x , t ) ) ( 1 ) where the first term on the right hand side is the diffusion term , and F ( AExt ( x , t ) ) is the reaction term , which depends on the extracellular antibiotic concentration ( AExt ( x , t ) ) and the positions and types of the cells . Using the above defined parameters and dynamical processes , we can write F ( AExt ( x , t ) ) =∑i=1N ( ρ* ( t ) +β*AInt ( i , t ) −α*AExt ( i , t ) ) δ ( x−i ) −φAExt ( i , t ) , ( 2 ) where the antibiotic sources and sinks are summed in the parentheses , i is the position of a cell among the N cells , which can either be B or P denoted by * in the bottom index where applicable , AInt ( i , t ) is the intracellular concentration of the antibiotic at position i , and δ is the Dirac delta [87] . Since in our case the birth and death processes and the spatial positions of particles are given by other complex interaction dynamics , writing down the complete dynamics of the system leads to an analytically intractable model . Therefore , we next implement the time-and-space-discretised dynamics of antibiotic concentration at site i on the rectangular grid and at time t+Δt in the extracellular environment as AExt ( i , t+Δt ) =AExt ( i , t ) +[DΔx2 ( ∑j=1vAExt ( j , t ) −vAExt ( i , t ) ) + ( ρ* ( t ) +β*AInt ( i , t ) −α*AExt ( i , t ) −φAExt ( i , t ) ) θ ( i ) ]Δt ( 3 ) where the first term corresponds to the diffusion of antibiotics according to the discretised diffusion algorithm between the four nearest neighbouring points ( v = 4 ) ( Neumann-neighbourhood: north , south , east , west ) ; Δx is the spatial resolution , and Δt is the time resolution . The diffusion rate of the antibiotics , D , is measured in the unit of x2/t , where x denotes the spatial resolution , here one cell of the grid , and t stands for time measured as an update step . θ ( i ) takes the value one if there is a cell at the site i , else being zero . The dynamics of intracellular concentration of the antibiotic at the site i can be written as AInt ( i , t+Δt ) =AInt ( i , t ) + ( α*AExt ( i , t ) −β*AInt ( i , t ) −γ*AInt ( i , t ) ) Δt . ( 4 ) Naturally AInt ( i , t+Δt ) = AInt ( i , t ) = AExt ( i , t ) = 0 if there is no cell at site i . For the birth and death processes , we define the reproduction or growth rate of the antibiotic-producing ( B ) and non-producing ( P ) strains respectively as rB ( i , t ) =rB , 0+rB , pr ( t ) −c , rP ( i , t ) =rP , 0−λ ( a , T , k , AInt ( i , t ) ) ( 5 ) where c is the decrease in reproduction rate because of the costly processes of antibiotic production and resistance . The reproduction rates rB , 0 , rP , 0 , and rB , pr ( t ) correspond to normal ( baseline ) and temporarily increased resource conditions , respectively . We assume rB , 0 = rP , 0; different assumptions would only rescale the value of c ( see S5 Fig in the Supplementary Information for different choices of rB , 0 ) . The effect of the antibiotic λ ( a , T , k , AInt ( i , t ) ) on the P strain’s reproduction rate depends on the critical threshold ( T ) , the maximum effect ( a ) , the steepness of the dosage effect ( k ) , and the actual intracellular concentration of the antibiotic in the sensitive cell at the site i ( AInt ( i , t ) ) . Following empirical observations [61] , we define a general sigmoid function for the effect of the antibiotic: λ ( a , T , k , AInt ( i , t ) ) =a/[1+exp ( −k ( AInt ( i , t ) −T ) ) ] ( 6 ) Population dynamics are represented by a death-birth process in which a randomly chosen focal individual at site i dies , and individuals from its Moore neighbourhood ( 8 nearest neighbours , w = 8 ) can reproduce and place a progeny into this focal empty site , with probability proportional to their reproduction rates p ( i ) =rξ ( i , t ) /∑j=1wrξ ( j , t ) , where ( ξϵ{P , B} ) ( 7 ) At the beginning of the simulation , the beneficial strain is represented in low numbers ( nB , 0 ) , and the parasitic strain is missing ( nP , 0 = 0 ) . We carried out two sets of invasion tests to demonstrate how host-provided private resources can result in self-sustaining , beneficial microbiomes , even if the private resource itself eventually diminishes . In the first test , we used time , while in the second , we used colony size as the signal to switch from private to public resources , or in other words , to stop the host’s selective support for the beneficial strain .
As discussed in the Introduction , the host has multiple mechanisms by which it can provide private resources . We find that protecting initial growth ( Fig 2A and 2B ) , increasing the reproduction rate ( Fig 2C and 2D ) , and/or enhancing the antibiotic effectiveness ( Fig 2E and 2F ) of the beneficial strain , can all result in a self-sustaining , beneficial-strain-dominated microbiome that is resistant to invasion even after the host resource is made public ( at time τ ) and the beneficial strain starts to experience a competitive disadvantage due to its costs of antibiotic production and of expressing its antibiotic-resistance traits . In all three scenarios , the longer the time τ that the resource is private ( Fig 2 , x-axis ) , the less of an advantage , in the form of protected growth ( s+ ) , increased population growth ( r+ ) , or increased antibiotic production ( ρ+ ) ( Fig 2 , y-axis ) , is required for the beneficial strain to be able to resist invasion after the resource becomes public . This is because invasion resistance increases with the size of the beneficial colony and with the concentration of antibiotic that the colony produces and transports into the environment . We also observe that if the physiological mechanism of resistance by the beneficial strain to its own antibiotic is efflux , this can additionally enhance invasion resistance , even if the supply time is short and the advantage conferred by the private resource is small ( Fig 2A , 2C , 2E vs . 2B , 2D and 2F ) . The reason is that re-exporting any ingested antibiotic increases the environmental concentration of antibiotic , which aids suppression of invading parasitic strains . Consistent with the results from Invasion test 1 , if the beneficial colony successfully reaches a critical size ( the Minimum Sustainable Colony size: MSC ) , it becomes resistant to invasion over a wide range of parameters after the private resource is made public ( Fig 3 ) . Again , having antibiotic efflux as the resistance mechanism promotes invasion resistance ( Figs 3 and 4 ) , whereas ( and intuitively ) a higher rate of extracellular decay of antibiotic counteracts this effect ( Figs 3 and 4 ) . When a large amount of antibiotic is in the environment , because efflux is high and decay is low ( Fig 3A and Fig 4A and 4C ) , the beneficial strain is able to dominate over a wide range of diffusion rates . However , when the extracellular-decay rate is high , only high diffusion rates allow the beneficial strain to dominate ( Fig 4D ) . This is because at low diffusion rates , the antibiotic produced in the centre of the colony is lost due to decomposition before it diffuses to the colony edge , where it would have attacked invaders . In contrast , at high diffusion rates , more of the antibiotic produced by cells deeper in the colony reaches the invasion front at the edge ( Figs 3 , 4 , and 5 ) . The complement to this result is that if the diffusion rate is low , then even a large colony size does not necessarily guarantee success unless the efflux rate is also high enough ( Fig 4A and 4B ) . Essentially , if antibiotic efflux is used as the resistance mechanism by the beneficial cells , this can substitute for outright diffusion of the antibiotic , allowing the antibiotic to reach the colony edge , where it can suppress invaders ( Fig 4 ) . Interestingly , under some conditions there is a non-monotonous effect of diffusion rate on invasion resistance , such that the Minimum Colony Size ( MSC ) can be much smaller for medium-level diffusion rates . For example , looking at Fig 3B , for low antibiotic diffusion rates ( values 0−1 on the x-axis ) , the MSC is close to 100%; that is , the colony can resist invasion only if more than 95% of the available habitat is already occupied by the producers; otherwise , parasites displace the whole population of antibiotic producers . Similarly , for high diffusion rates ( values D = 80−100 on the x-axis ) , although smaller , a considerable colony size still has to be reached . However , the MSC curve reaches a minimum between low and high diffusion rates , such that only a 1−10% MSC is enough to resist invasion ( Fig 3B ) . The important result is that for any intermediate efflux and decay parameters coupled with intermediate diffusion rates , colonies with practically any non-zero initial size can withstand parasite invasion ( Fig 3A , 3B and 3D ) . This nonlinearity occurs because , in general , diffusion carries antibiotic to the edge of the antibiotic-producing colony , where it can act against invading P strains , but diffusion also carries antibiotic away from the edge of the colony . An intermediate diffusion rate turns out to maximise the amount of antibiotic at the fighting front ( see S1–S6 Figs in the Supplementary Information for further results of different parameter combinations ) .
The composition of host-associated microbiomes has been shown to correlate with host health status and fitness [4 , 88–94] , and thus , there is likely to be strong selection on host species to evolve mechanisms that favour the assembly of certain kinds of microbiomes over others [11 , 12 , 27] . Here we have explored how a host can favour the assembly of a defensive microbiome that is persistently dominated by antibiotic-producing bacteria [7 , 23 , 77 , 95] . We argue that a host can take advantage of an ecological phenomenon known as ‘community bistability’: when two species compete via interference , such as when a bacterial species uses antibiotics to hinder a competitor , the winner depends partially on the initial population sizes of the two competitors [9] . If the antibiotic-producer initially establishes a larger population in the new habitat , it can collectively produce a sufficient amount of antibiotic to suppress its competitor and grow until the space of opportunity vanishes for the non-producer . In contrast , if the non-producer species starts with the larger or competitively superior population , then the small amount of antibiotic produced by a small colony of a producer is insufficient to suppress the non-producer , and the non-producer wins . It follows that by using an antibiotic-producer as the initial ( or ‘priming’ ) strain of the microbiome , a host can narrow down the variety of strains able to invade this already established environment [4 , 5 , 9 , 11] . The host is thus efficiently able to canalise the composition of the emerging microbiome . Such priming effects have been demonstrated in various experimental systems [25 , 37 , 39 , 96] . Our argument , in a nutshell , is that an effective way for hosts to guide microbiome assembly is by manipulating initial conditions , resulting in a cascade of bacterial community dynamics that ultimately favour some kinds of microbiomes over others , which will , in turn , affect host fitness . Another way of thinking about this is through the lens of game theory [9 , 13] . The host is able to ‘screen-in’ antibiotic-producing bacteria by exploiting a fundamental correlation: bacterial strains that make lots of antibiotics are both superior interference competitors against other bacteria and also likely to produce compounds that are useful for host defence . In this study , we have integrated local interactions and the explicit spatial dynamics of cellular and chemical components with the original phenomenological model that laid the foundations of the theory [9] . In this now more realistic model , even for large populations , the number of directly interacting cells is relatively modest , and thus , the spatial correlations of active agents determine dynamics meaningfully [5 , 78] . Furthermore , such an integrated , spatially-explicit model allows us to understand the effect of different antibiotic-resistance mechanisms [75 , 83–86 , 97] on the microbiome assembly , and to investigate how attributes of the host surface , which govern the diffusion dynamic of the antibiotic , can modify the outcome . We have also widened the applicability of Scheuring and Yu’s original model [9] by reviewing multiple mechanisms allowing a host to prime a defensive microbiome , even if the beneficial strain can only be recruited from the environment ( horizontal transmission ) ; the original model made the restrictive assumption that the beneficial strain is strictly vertically transmitted . We have corroborated the earlier results [9 , 13] that antibiotic producers and non-producers can form a bistable system and that the outcome of competition depends on their reproduction rates , how effectively the host is able to selectively promote the beneficial strain , and the initial ratio of the two strains [9] . Once the antibiotic producer is able to gain dominance , in such a system it can remain dominant for a lifetime , even if the host-provided private resource vanishes or becomes public . The current model also shows that localized interactions , which is an important realism that had been ignored in the simpler model [9] , do not impede this dominance because the antibiotic itself can diffuse to the colony edge to inhibit invaders . This effect is strengthened when the mode of resistance employed by the producers is antibiotic efflux . We also show with the current model that the host resource only needs to remain private for a finite critical time , basically until the beneficial colony reaches a Minimal Sustainable Colony Size ( MSC ) , at which point it becomes resistant to a given rate of invasion . The critical time and/or the MSC depends on the physiochemical properties of the system , most importantly the decomposition , decay , diffusion , and efflux rates of the antibiotic , and the advantage provided to the beneficial colony by the private resource , all deriving from the fact that colony size determines the amount of antibiotic produced . Our brief review of the literature suggests that multiple forms of ‘private resource’ exist , including food , space , and host-provided compounds that harm undesired strains . Nonetheless , privacy of resources is inherently difficult and costly to achieve , and it is therefore realistic to assume that any host-provided resources will eventually become public . This inevitable transition from private to public , which intuitively might be expected to allow the successful invasion and establishment of parasitic strains , does not in fact do so , because of bistability . After a beneficial colony establishes itself , a public resource is in practice only enjoyed by the winner , the beneficial colony . Finally , we show that an intermediate diffusion rate can maximise the amount of antibiotic accumulating at the colony edge . Our findings suggest that the attributes of the host surface , for example the diffusion rate , can either increase or reduce the effect range of the antibiotic [98] . As there is no conflict of interest between antibiotic-producer and host , their coevolution is expected to optimise the diffusion speed , and hence the effectiveness , of the antibiotic . Overall , evolutionary optimisation can act by minimising the host investment required to attain a beneficial microbiome , by reducing the duration of a private resource supply , and by evolving the optimal physiochemical properties of the habitat , the host surface . If so , then we might also expect that the co-evolution of host and preferred strains results in an efficient and well-conducted build-up of a beneficial microbiome , an orchestrated symbiosis that efficiently narrows down the enormous number of possible scenarios to canalise the emergence of a microbiome towards the most favourable one . | Host-associated microbiomes are complex communities , harbouring a great diversity of beneficial , neutral , or harmful species . Microbiome composition can have a significant effect on the health status and fitness of the host , and thus host species are selected to evolve mechanisms that favour the assembly of certain kinds of microbiomes over others . As we demonstrate , both by reviewing the literature and by using a detailed , spatially explicit , mathematical model , a host species can employ several cost-effective mechanisms to promote the establishment of beneficial bacteria , for example antibiotic producers , and to prevent the invasion of harmful microbes . These mechanisms include the host providing private resources in the form of a food source or inhabitable space that the preferred antibiotic-producing species are able to use to their advantage against species that are harmful to the host . After an antibiotic-producing species establishes itself with the help of the host , its colony is now self-sustaining , even if host resources now become publicly available , and can provide a reliable safeguard against parasites . | [
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"de... | 2019 | Efficient assembly and long-term stability of defensive microbiomes via private resources and community bistability |
Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone ( EZ ) with the aim of effectively resecting it . However , in a considerable number of patients , EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications . There is a clear need for developing alternative approaches to induce seizure relief , but minimal impact on normal brain functions . Here , we develop a personalized in-silico network approach , that suggests effective and safe surgical interventions for each patient . Based on the clinically identified EZ , we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation . We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes , and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain ( TVB ) , in order to evaluate effectiveness and safety of the target zones ( TZs ) . In particular , we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network . We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread , but only a small subset of them proves safe . The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies .
Epilepsy is a chronic neurological disorder that is defined by the occurrence of repetitive unexpected seizures . The epileptic seizures , characterized as abnormal synchronization of neural activities , originate in a specific brain region and propagate to other regions through inter-regional structural interactions , i . e . , individual brain connectome , and produce various ictal symptoms depending on the recruited brain regions . For the treatment of epilepsy , medication with antiepileptic drugs is preferentially applied [1 , 2] , and surgical intervention is often offered as an option for drug-resistant patients [3–5] , which account for more than 30% of patients [2 , 6] . There are two main types of surgical strategies: resection and disconnection . Resection , which removes the brain regions generating seizures , results in seizure-free outcomes in 30–70% of the postoperative patients depending on the localization accuracy of epileptogenic zone ( EZ ) and the pathology of each patient [7–10] . Disconnection , which severs nerve pathways that play an important role in seizure propagation , may have either a curative objective , i . e . , hemispherotomy , or may limit seizure propagation , i . e . , callosotomy [11 , 12] . Although surgical intervention is generally accepted as an effective method to control drug-resistant seizures , only about 10% of patients might be considered candidates for surgery [6] because EZs are often located in multiple brain regions simultaneously and involve eloquent areas , which are defined as brain regions where damage causes neurological complications such as language , memory and motor problems [13] . Several alternative methods including multiple subpial transection , which could prevent neuronal synchronization in the EZ without altering normal functions by severing horizontal intracortical fibers while preserving vertical fibers in the eloquent cortex , have been tried for patients who are unsuitable for conventional surgery , but with variable results [13–15] . Therefore , there is a clear need to provide more optimal surgical options for those patients . The alternative method must be 1 ) effective in seizure reduction , 2 ) able to provide flexible options depending on the inoperable EZ or technically inaccessible region for surgery , and 3 ) have minimal impact on normal brain functions . Studies on epilepsy have mainly focused on investigation of the brain network dynamics of individual patients . By analyzing functional data , such as intracranial electrocorticographic ( ECoG ) signals and stereotactic electroencephalographic ( SEEG ) signals , many studies have examined network properties for diverse brain states including interictal , preictal , ictal , and postictal epochs [16–21] . In particular , network analysis based on graph theory has been able to not only identify characteristics of the seizure onset zone that would be targeted in resection surgery [17 , 19] , but also observe changes in network topology over the onset and time-course of seizure [22–25] . Several studies have shown that one large regular network is formed at seizure onset compared to the network in the interictal period , which consists of several small sub-networks [22 , 23] . These results suggest that seizures may be prevented by disrupting the formation of large regular networks through the disconnection of well-chosen sub-networks . Furthermore , several other studies have demonstrated that the epileptic brain network has more segregated features than the healthy brain network [26–28] . Meanwhile , by analyzing structural data based on Magnetic Resonance Imaging ( MRI ) , many studies have reported structural abnormalities in the epileptic brain distinct from the normal brain , which include not only regional alterations ( decrease in subcortical volume and cortical gray matter thickness ) [29 , 30] , but also abnormalities in white matter tracts , i . e . , inter-regional connectivity ( reduction in fractional anisotropy ) [31 , 32] . From the network perspective , several studies have shown an increase of local network connectivity and a decrease of global network connectivity in the epileptic brain [27 , 33 , 34] , even though the situation is more complex depending on whether brain regions are involved in seizures generation and propagation [35 , 36] . Yasuda and colleagues have further reported that the healthy brain network present widespread distribution of hub regions ( frequently used brain regions in inter-regional signaling ) , while the epileptic brain network has hub regions concentrated in specific areas ( for example , in temporal lobe epilepsy , paralimbic/limbic and temporal association cortices ) [34] . The results of these studies suggest that the epileptic brain comprises a distinct modular structure and that seizure propagation can be controlled by blocking interactions between the modules , i . e . , by severing the connections . Translation of any computational modeling approach will require the personalization of the brain network models , tailored to a patient’s connectivity and lesion . Personalized brain network models , based on brain connectome and clinical information from each patient , have been able to simulate individual seizure propagation patterns [20 , 37] . Moreover , some investigations have simulated the effects of surgical intervention , and have been able to predict how the removal of certain brain regions will have an impact on the occurrence seizures [19 , 38–42] . These studies show the possibility of computational approaches being able to construct a paradigm that derives optimal surgical strategies for each patient by applying in-silico surgical techniques on the personalized brain network model . However , at present , most efforts in the field focus on improving the localization of EZ and develop strategies to effectively remove the identified zone . Only a few studies have reported the possibility of controlling seizure activities by eliminating the areas other than EZ [40 , 42] . Here we propose a computational method towards the identification of minimally invasive surgical interventions , particularly applicable for case in which the EZ is non-operable . Focusing on the fact that the epileptic brain network has distinct segregation characteristics , we employ modularity analysis with structural brain connectivity from each patient , in order to derive brain regions and fiber tracts as target zones ( TZs ) that should be removed for resection and disconnection surgery , respectively . Here , we assume the worst-case scenario in which the EZ is an inoperable zone , so that the proposed in silico surgical approach induces seizure relief by suppressing seizure propagation to other brain areas even though it cannot prevent seizure generation in EZs . Reducing the involvement of propagation networks is a major factor to reduce the impact of seizures , particularly the loss of consciousness [43] . The acquired TZs are evaluated by personalized brain network simulations in terms of the effectiveness to control seizure propagation and the safety to maintain normal brain functions , and then optimized according to the results . The notion of safety is critical to our approach and is here operationalized through network activation paradigms . We leverage our capacity to generate diverse realizations of the same personalized brain model , in particular a healthy and epileptic version . A systematic characterization of the signal transmission characteristics of the healthy brain , here realized via stimulation paradigms , provides us with target templates for the optimization of the safety constraints .
To derive personalized optimal surgical options for drug-resistant epilepsy patients , we propose a patient-specific in-silico surgical approach combining graph theoretical analysis with brain network simulations . Based on the patient-specific modular structure obtained from the structural brain connectivity and clinical estimation for EZ of each patient , brain regions and fiber tracts acting as hubs in the interaction between the modules , i . e . , connecting different modules , are identified as TZs for surgical intervention . The acquired TZs are evaluated through personalized brain network simulations regarding their effectiveness and safety . Fig 1 shows a concrete example for the TZ evaluation . Effectiveness to control seizure propagation is assessed by the degree of seizure propagation suppression ( Fig 1A–1D ) . Fig 1B presents simulated signals at several brain nodes when nodes 3 ( ctx-lh-caudalmiddlefrontal ) , 22 ( ctx-lh-posteriorcingulate ) and 27 ( ctx-lh-superiorfrontal ) are EZs , which show that the nodes are seizure-recruited following some delays depending on connectivity between nodes , after the seizure is generated from EZs . On the other hand , after removing a specific node in the seizure propagation pathway ( node 23; ctx-lh-precentral , or node 21; ctx-lh-postcentral ) , simulated brain signals show that the propagation beyond each node is prevented even if the seizure is still occurred from EZs ( Fig 1C and 1D ) . In this way , characteristics of seizure propagation are observed after eliminating target nodes or target edges for effectiveness evaluation of the TZ . All label names and indices corresponding to the subdivided regions of the brain ( brain nodes ) are provided in the S1 Table ( see the materials and methods section also ) . Our approach towards the evaluation of safety of the intervention rests on the maximization of the signal transmission properties of the brain network . The latter is assessed by stimulating relevant brain regions and quantifying the subsequent transient trajectory of brain network activation . More concretely , safety is evaluated by assessing similarity of the spatiotemporal brain activation patterns following electrical stimulation , before and after removal of the TZ . To investigate the variations in resting state ( RS ) networks , the brain regions where the stimulation is applied are determined based on previous results [44] , in which specific brain regions have been reported that can reproduce similar responsive networks to each of the eight well-known RS networks [44 , 45] ( Table 1 ) . Fig 1E–1G present an example of the response network when a stimulus is applied to a specific node ( node 3 ) . The stimulation locally activates the stimulated node first , followed by a propagation and sequential recruitment through the connectome , thereby generating a unique spatiotemporal response pattern specific to the stimulation site . The solid lines and dotted lines show the simulated signals obtained from several brain nodes before and after eliminating the certain node , respectively ( node 23 in Fig 1F , node 21 in Fig 1G ) . Compared with the pre-removal response pattern , the response signals are altered following the removal of node 23 , whereas the response signals appear unaffected following the removal of node 21 . The color bars represent the degree of these differences quantitatively , i . e . , as similarity coefficients . These results illustrate nicely the sensitivity of the spatiotemporal seizure organization to network alterations . In this way , the safety of TZ is evaluated by systematically stimulating specific nodes , which reproduce each RS network , and comparing the response patterns before and after eliminating target nodes or target edges . If the TZ is judged to be inadequate based on the network simulation results , another TZ is derived by applying the results to the modularity analysis again . Through this feedback approach , the optimized TZ that effectively prevents seizure propagation while minimally affecting normal brain functions can be obtained . Methodological details for the sequential steps of the proposed in-silico surgical approach are provided in the materials and methods section . Here , we present surgical intervention options outside the EZ , derived from the proposed in-silico surgical approach , for a particular patient ( Patient IL ) . The patient has two EZs , ctx-rh-lingual ( node 61 ) and ctx-rh-parahippocampal ( node 64 ) , and these two EZs are designated as inoperable zone . Using modularity analysis ( see the materials and methods section ) we construct a patient-specific modular structure considering inoperable zones ( Fig 2A ) . The brain network nodes are divided into seven modules with modularity coefficient of 0 . 3912 and the green module including EZs is further subdivided into four sub-modules . Based on this modular structure , 3 target nodes ( black triangles ) and 8 target edges ( gray dotted lines ) , connecting the EZ sub-module to other sub-modules or modules , are identified . Anatomical location of the initial TZs are shown in Fig 2B . In the network simulation for evaluating the effectiveness of the TZs , before the removal of TZs , most brain nodes are recruited after the EZs generate a seizure activity . However , when 3 target nodes are removed , the seizure activity is almost isolated in EZs with a suppression ratio ( SR , suppression ratio of seizure propagation ) of 95 . 65% . When 8 target edges are disconnected , seizure-recruited nodes are significantly reduced with the SR of 91 . 30% , even though the seizure activity is still observed in several neighboring nodes of EZs ( results are shown in the S1 Fig ) . These results demonstrate that the elimination of the derived TZs is able to prevent seizure propagation . Meanwhile , in the network simulation for evaluating the safety of the TZs , similarity coefficients between responsive activation patterns are calculated before and after removal of the TZs , by stimulating specific brain regions to test several RS networks ( Fig 3A ) . Low similarity coefficients indicate that the response pattern due to stimulation has been severely changed after removing the TZs . In this case , the results imply that the elimination of the obtained TZs could lead to a larger network disorganization and then a higher risk for negative cognitive impact , in particular for memory function . Here , the TZs are considered unsafe , if removal of the TZs deforms the response pattern to more than 25% of the original pattern ( i . e . , if the mean value of similarity coefficients in all brain regions is below 0 . 75 ) . More details are provided in the materials and methods section . Since the obtained TZs may have a negative impact on the memory network , the next step is to identify the critical node that leads to the most significant variation . Fig 3B presents the effect on the memory network when each node among the initially derived target nodes is removed . Eliminating the left-cerebellum-cortex ( node 35 ) yields the lowest mean similarity value compared to before removal ( 0 . 58 , when the stimulus is applied to node 10 ) , so that this node is defined as a critical node , and therefore designated as inoperable zone . By feeding back the updated inoperable zones to modularity analysis , a new modular structure is obtained . Fig 2C shows the modular structure when the critical node ( gray triangle , node 35 ) as well as two EZs ( nodes 61 and 64 ) are set to inoperable zones . The brain network nodes are divided into eight modules with modularity coefficient of 0 . 3995 , and the green module including EZs is subdivided into two sub-modules so that each inoperable zone and its neighboring nodes belong to the same sub-module . Based on this modular structure , new target nodes ( black triangles ) and target edges ( gray dotted lines ) are acquired . Anatomical location of the new TZs are shown in Fig 2D . Fig 4 shows network simulation results for effectiveness evaluation of newly derived TZs . The results present time series data , i . e . , local field potentials , in all brain nodes . Before the removal of TZs , the seizure activity originated from EZs propagates to other nodes after some delay , i . e . , most nodes are seizure-recruited ( Fig 4A ) . Having eliminated the new TZs , a significant reduction of seizure-recruited regions is identified compared to pre-removal simulation ( Fig 4B and 4C; the SR after removing 3 new target nodes: 89 . 86% , the SR after removing 5 new target edges: 85 . 51% ) , even though they have some more seizure-recruited regions than when removing initial TZs . Fig 4D shows the simulation results when removing the same number of random nodes ( excluding EZs ) as the derived target nodes . Comparing the degree of reduction in seizure-recruited nodes , it demonstrates that the elimination of TZs obtained from the proposed method can effectively suppress the seizure propagation ( in this example , the SR after removing 3 random nodes: 31 . 88% ) . Meanwhile , the simulation results show that persistent spikes occur even if seizure activity is suppressed in each brain node after the removal of TZ . These interictal spikes are caused by the noise environment that we apply for stochastic simulations . In this study , Gaussian noise is applied to all brain nodes ( Epileptors ) to account for background internal activity , so that each node generates random spike events as a baseline activity . The occurrence of these spikes is regulated according to the state of each node , such as preictal , ictal and postictal . Methodological details are provided in the materials and methods section , and more details on the behavior of the Epileptor model can be found in previous papers [37 , 46] . Fig 5 shows the difference between the safety evaluation results for initial TZs and new TZs . The histogram shows the mean value of the similarity coefficients between the response patterns due to stimulation in all brain regions before and after removal of TZs . Comparing the values between two groups , it indicates that eliminating the new TZs is able to maintain all RS networks at a similar level as before removal ( the mean value of similarity coefficients > 0 . 75 ) , whereas eliminating the initial TZs may disrupt memory network . In other words , this means that the newly derived TZs have less impact on the transmission properties of the brain network sustaining normal brain function . The results also show that disconnecting the fiber tracts corresponding to the target edges has less impact on normal brain function than resecting the brain regions ( corresponding to the target nodes ) . In this example , the new TZs obtained from a single feedback satisfy the safety criteria . However , if the newly derived TZs do not satisfy the criteria , the iterative feedback procedure ( find a critical node among the new TZs , set it to inoperable zone , and obtain a new modular structure ) continues until the TZs that meet the criteria are derived . In this section , we present the results for condition when the resolution parameter in the modularity analysis is fixed to 1 . 25 ( from Fig 2 to Fig 5 ) for simplicity . However , since the proposed method involves a parameter sweep of the resolution parameter ( 0 . 5 to 1 . 5 with intervals of 0 . 25 ) , multiple modular structures are obtained according to the parameter value ( the resolution parameter determines the size of each module , i . e . , the number of modules ) , resulting in multiple TZs options . In this patient , 5 variants for target node and 7 variants for target edge have initially been obtained . After applying the feedback , 7 variants for target node and 9 variants for target edge have finally been derived . The lists of TZ variants are presented in S2 Table . Results for 6 other patients are also shown in S3–S8 Tables and S3–S8 Figs . The results contain several TZ variants that are appropriate each patient’s circumstance considering the location of EZ and individual brain connectome . The final TZ variants should be effective surgical targets preventing seizure propagation with maintaining normal brain functions . Here , in order to demonstrate the robustness of the proposed method , we present additional simulation results that show how TZ varies according to the location of EZ . Fig 6A and 6B show target nodes and target edges , in a specific patient ( Patient CV ) , obtained by performing systematic simulations where one EZ is placed in all possible brain nodes ( The EZ is assumed to be an inoperable zone ) . The cumulative results of TZs identify the nodes and the edges that are frequently used as TZs . Frequently acquired nodes and edges play an important role in propagating seizure activity from the localized region to the entire brain , and can effectively control seizure propagation by being removed . In this patient , the most frequently derived node is ctx-rh-postcentral ( node 70 ) , and the most frequently derived edge is the connection between ctx-lh-supramarginal ( node 30 ) and ctx-lh-postcentral ( node 21 ) . Anatomical locations of cumulative results are presented in Fig 6C . Meanwhile , in deriving the TZs , the frequency of the target nodes initially acquired is positively correlated with the node strength ( the sum of weights of links connected with other nodes ) , i . e . , the nodes having high strength are frequently derived as TZs ( correlation coefficient: 0 . 7842 ) . However , the final target nodes obtained from the feedback procedure tend to be more concentrated at few nodes , and thus the frequency of the finally acquired target nodes is not noticeably relevant to the node strength ( correlation coefficient: 0 . 3059 ) . Simulation results for 6 other patients are shown in S9–S14 Figs . Interestingly , the critical nodes , which are used for the feedback strategy to consider the safety for normal brain functions , are not significantly different in all 7 patients . In particular , the superior-frontal cortex ( nodes 27 and 76 ) appears often as the critical node , which means that these nodes are effective to control seizure propagation but removing them may cause a problem for the normal brain function ( this region is also the node with the highest strength ) . The network simulation results identify that the elimination of those nodes severely distort the RS networks corresponding to visual , working memory and ventral stream as well as default mode . In fact , in previous studies , the superior-frontal cortex has been investigated as a node that is frequently used as the shortest path connecting two different brain nodes [47] , and also has been shown to play an important role in interhemispheric propagation of seizures [41] . Furthermore , several clinical studies have reported the resective surgery in the superior-frontal cortex , which indicate that it may cause working memory impairment [48] as well as transient motor deficit [49 , 50] . In this section , the systematic simulations have been demonstrated for different TZs according to the locations of EZ . The results can be used not only to identify major nodes and edges involved in seizure propagation , but also as a reference to elicit reasonable surgical targets if there are several clinical hypotheses for the EZ location .
We have demonstrated the use of personalized brain network models for the development of novel surgical intervention . In particular , we focused on deriving effective alternative methods for those cases where EZs are inoperable , so that we conducted the study assuming that all EZs of patients are inoperable even if some EZs are clinically removable . Our proposed in-silico surgical approach is based on the graph theoretical analysis using patient-specific brain connectome , specifically modularity analysis , and personalized brain network simulations . We propose a strategy to operationalize the notion of “safety” by minimizing the impact upon the brain’s signal transmission capacity . The modularity analysis is generally used as method to investigate synchronization characteristics between brain regions [23 , 33 , 51 , 52] . Consistent with previous observations [26 , 33 , 34] , we found that each patient's brain network has a distinct modular structure . From the patient-specific modular structure , nodes and edges connecting the EZ sub-module with other submodules or modules were extracted as surgical options , TZs , to suppress seizure propagation in a patient-specific manner . By adding a constraint to the existing modularity analysis , flexible TZs excluding inoperable zones could be derived , which may provide alternative surgical methods that can result in seizure relief to patients who are considered unsuitable for the conventional surgery since resection of EZ may cause severe neurological complications . Moreover , the parameter sweep in the modularity analysis obtained different modular structures , ultimately resulted in multiple TZ options . This multiplicity is crucial in that clinicians can select the surgical target within multiple options , taking into account the number of interventions and the suppression degree of seizures . Clinicians may also consider not only the specific regions that should be excluded for surgery based on their clinical experiences but also the technically challenging regions . To evaluate the effectiveness and safety of the identified TZ , brain network simulations were employed . Based on the patient-specific network model constructed by structural brain connectivity and clinical estimation for EZs of each patient , the effectiveness of the TZs were assessed by simulating seizure propagation characteristics before and after removal of the TZs . Reducing the involvement of propagation networks is a major factor to reduce the impact of seizures , particularly the loss of consciousness [43] . Loss of consciousness is one of the major signs and is clearly linked to the synchronization in propagation network , particularly fronto-parietal networks during temporal lobe epilepsy ( TLE ) seizures . It is recognized that a good outcome after epilepsy surgery ( according to the Engel's classification ) may include patients with residual subjective symptoms ( aura ) but without any more objective signs ( automatism , loss of consciousness ) , which is the definition of seizure free patients IB in Engel's classification . In the literature , other in-silico surgical approaches have been reported recently [39 , 41] . Hutchings and colleagues have identified seizure reduction by detecting and eliminating nodes having a fast transition time to seizure state through network simulations , which was constructed using the structural connectivity of each patient [41] . Sinha and colleagues have proposed a network model , based on functional brain connectivity of each patient , which has predicted the regions having higher likelihood of seizure occurrence [39] . They have shown that better surgical outcomes can be produced if the actual surgical site well matches the region obtained from the simulation [39] . In contrast to our approach , most previous studies have focused on decrease of seizure occurrence by removing the brain regions where seizures occur first , i . e . , seizure onset zones . These approaches of no use when the regions in question are inoperable and alternative approaches , as advocated here , need to be considered imperatively . Meanwhile , a few recent in-silico studies have reported the effects of resection of non-EZ areas on the epileptic networks [40 , 42] . The studies have demonstrated that eliminating a node other than the hyperexcitable node can be effective to reduce the seizure occurrence as well as removing the hyperexcitable node ( or , it may be more effective [40] ) , so that they have shown the possibility of a computational framework to suggest alternative surgical strategies . However , since the effect on seizure reduction is highly dependent on the location or contribution of each node in the network , a more systematic approach is needed to identify the target node ( i . e . , alternative surgical target ) . Furthermore , network effects should be investigated at a whole-brain scale . Here , we have derived TZs based on the clinical estimation and brain connectivity analysis of each patient , and examined the effect of TZ removal in the seizure propagation network through personalized brain network simulation based on individual brain connectome . Critical to surgical intervention outside of the EZ is the investigation of the safety of the procedure . We here operationalized safety by the concept of preservation of signal transmission properties of the brain network , assuming those to be directly linked to brain function . Brain function capacity is often , at least implicitly , quantified by functional connectivity of the resting state [53–59] . These approaches attempt to quantify , by construction , properties of attractor states at rest . Several computational studies have simulated resting state and task-related functional connectivity ( RS-FC ) through a large-scale brain network model , and shown the correlation with empirical human brain imaging data including functional MRI ( fMRI ) signals [53 , 58 , 59] . However , considering the variability of RS-FC observed in both empirical and simulation data [58] , these approaches may not be sufficient to compare the effects before and after removal of a specific brain region ( or a specific connection ) . Therefore , a straightforward method is required to evaluate the TZs by distinctly quantifying the changes in network characteristics at resting state in pre- and post-surgical condition . Perturbations to attractor states allow to sample additional properties of the brain network such as attractor stability , convergence and divergence of flows , and thus significantly enhance the characterization of its dynamic properties . Stimulation is a simple but reliable way to induce perturbation to each state , which generates a spatiotemporal response pattern according to the stimulation location and brain connectivity . Here , we employed the stimulation method to reproduce each RS network and to clearly quantify the changes in the network properties before and after eliminating the TZs . To best estimate the transient spatiotemporal trajectory due to stimulation applied to individual brain regions , we compared its spatial and temporal properties before and after eliminating the TZs . Analyses of this nature have been performed previously by Spiegler et al [44] and demonstrated that transient trajectories are highly constrained by the structural properties of the network and show a surprisingly low-dimensional behavior , after an initial local stimulation artifact . Here we have exploited these transient trajectory properties to quantify the difference of response network due to stimulation , and assumed that the changes in the response pattern after removal of TZ indicate a negative impact in terms of brain functionality ( i . e . , we have interpreted the TZ as unsafe if the difference in response patterns before and after removal of the TZ is large ) . However , some clinical studies have reported postoperative cognitive improvements in epilepsy patients [60 , 61] . In particular , Baxendale and colleagues have demonstrated improvements in memory function ( verbal learning and visual learning ) in about 10% to 20% of patients who underwent anterior temporal lobe resection [60] . These results indicate that , in contrast to the assumption we made in this paper , changes in the response pattern after elimination of TZs may have a positive impact on the functionality . This limitation should be sufficiently discussed and improved by the integrated and parallel approaches with clinical studies . In clinical routine , pre-surgical mapping of eloquent cortex is routinely conducted [62–64] including electrical stimulation through implanted electrodes [62] . These mappings make it possible to identify important cortical regions that should be excluded from the surgery because they are most disruptive . As noninvasive methods , fMRI and Magnetoencephalography ( MEG ) are frequently used for the mapping [63 , 64] , they localize eloquent cortex by identifying activated regions during certain tasks , such as motor , memory and language functions [64] . Despite these efforts , current epilepsy surgery still results in transient and permanent neurological complications including visual field defects , memory disturbances , dysphasia and hemiparesis [65–67] . In fact , except for the primary cortices responsible for specific functions , there is still a limit to accurately predicting what deficits may result from the removal of a specific brain region . Especially , with respect to RS networks , despite the fact that it is necessary to minimize postoperative changes in the functional networks , there is no index that can systematically evaluate the changes . Furthermore , the reported morbidity rates show a large variability across institutions [65 , 67] , suggesting that surgical outcomes , including postoperative deficits , are highly dependent on diagnostic procedures and decisions about the surgical planning . This observation is also known from a variety of other decision making situations including medicine and economics , best paraphrased by Daniel Kahneman , “To maximize predictive accuracy , final decisions should be left to algorithms , especially in low-validity environments” [68] . Taking into account these current limitations related to the prediction of postoperative deficits , we emphasize that the use of more systematic and integrative methods is condition sine qua non to quantitatively predict the impact of the removal of a specific region on normal brain functions . The computational method based on personalized brain network modeling suggests a novel approach to evaluate the safety of the surgery , and can be enhanced by combining with conventional clinical methods ( such as task fMRI and RS fMRI ) . Our proposed in-silico surgical approach derives personalized optimal TZs considering inoperable zones , by means of a feedback approach combining modularity analysis and brain network simulations . However , given that our current work addresses network modulation outside of the EZ , it has challenges linked to clinical validation , because it is by definition outside the clinical routine . Extensive clinical data sets that have undergone surgery for areas other than EZ , in particular disconnection surgeries such as partial hemispherotomy [69] , can be used to validate the proposed method . Individualized modeling for each data set and simulation results reflecting the actual surgical site can be directly compared with post-operative clinical outcomes ( empirical data ) . Animal experimental models that can reproduce relatively diverse protocols can further test the network modulation results of this study . Although validation limits remain , the here presented in-silico analyses open new grounds for the discovery of novel surgical interventions . The proposed method has also some limitations in terms of the brain network model . First , in this study , to verify the effectiveness of TZs , we used a constant excitability parameter value for all other brain regions excluding EZs in order to assume the worst-case scenario , i . e . , we used a relatively higher value corresponding to the PZ for all other regions . However , each brain region has different excitabilities in real world systems , and PZs could be limited to only a few regions , even though it depends on the brain connectivity and the number and location of EZs in each patient [20 , 37] . In particular , through the in-silico surgical approach , the cerebellum was sometimes derived as a TZ when the same excitability value as other brain regions was applied because it has strong connectivity with other brain regions ( i . e . , it plays important role in seizure propagation ) . However , in the real world system , the cerebellum has a low excitability so seizure recruitment rarely occurs . We have not considered this regional specificity in our virtualizations so far , but detailed atlases and integration with The Virtual Brain ( TVB ) platform will enable this line of further improvement . Secondly , we divided the patient’s brain network into 84 regions , and modeled each region as one node . Each brain region was connected through structural connectivity , so it was reasonable to observe the propagation characteristics of seizure generated from certain regions . However , since neurophysiological mechanisms including interactions between neurons within a specific region , i . e . , internal connections , was not reflected , high-resolution spatial synchronization phenomena could not be observed . This issue can be improved through further studies , which model each brain region into multiple nodes and define internal connections that represent interactions between the nodes within a region . Despite some limitations , our study has a great importance in that it demonstrates that computational approaches pave the way for personalized medicine , by deriving innovative surgical options suitable for each patient and predicting the surgical outcomes .
Neuroimaging data was obtained from 7 drug-resistant epilepsy patients . The patients had EZs with different locations and underwent comprehensive presurgical evaluations [37 , 38] . The clinical characteristics of each patient are provided in S9 Table [37] . The structural brain network of each patient was reconstructed from diffusion MRI scans and T1-weighted images ( Siemens Magnetom Verio 3 T MRscanner ) using SCRIPTS pipeline [20 , 37 , 71] . Each patient’s brain was divided into 84 regions , which included 68 cortical regions based on the Desikan-Killiany atlas [72] , and 16 subcortical regions ( all label names for the subdivided regions are listed in the S1 Table ) . Connection strengths between the brain regions were defined based on the number of streamlines ( fiber tracts ) , and tract lengths to determine signal transmission delays between the regions were also derived . | We propose a personalized in-silico surgical approach able to suggest effective and safe surgical options for each epilepsy patient . In particular , we focus on deriving effective alternative methods for those cases where EZs are inoperable because of issues related with neurological complications . Based on modularity analysis using structural brain connectivity from each patient , TZs that would be considered as surgical sites are obtained . The acquired TZs are evaluated by personalized brain network simulations in terms of effectiveness and safety . Through the feedback approach combining modularity analysis and brain network simulations , the optimized TZ options that minimize seizure propagation while not affecting normal brain functions are obtained . Our study has a great importance in that it demonstrates the possibility of computational neuroscience field being able to construct a paradigm for personalized medicine by deriving innovative surgical options suitable for each patient and predicting the surgical outcomes . | [
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"signalin... | 2019 | Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP) |
DNA sequence polymorphism in a regulatory protein can have a widespread transcriptional effect . Here we present a computational approach for analyzing modules of genes with a common regulation that are affected by specific DNA polymorphisms . We identify such regulatory-linkage modules by integrating genotypic and expression data for individuals in a segregating population with complementary expression data of strains mutated in a variety of regulatory proteins . Our procedure searches simultaneously for groups of co-expressed genes , for their common underlying linkage interval , and for their shared regulatory proteins . We applied the method to a cross between laboratory and wild strains of S . cerevisiae , demonstrating its ability to correctly suggest modules and to outperform extant approaches . Our results suggest that middle sporulation genes are under the control of polymorphism in the sporulation-specific tertiary complex Sum1p/Rfm1p/Hst1p . In another example , our analysis reveals novel inter-relations between Swi3 and two mitochondrial inner membrane proteins underlying variation in a module of aerobic cellular respiration genes . Overall , our findings demonstrate that this approach provides a useful framework for the systematic mapping of quantitative trait loci and their role in gene expression variation .
DNA sequence polymorphisms that alter the activity of regulatory proteins can have considerable effect on gene expression [1]–[3] . With the advent of microarray and other genotyping technologies , it is now possible to examine the genome-wide effects of naturally occurring DNA sequence polymorphism on gene expression variation in segregating populations . For example , genotyping and expression data have been measured for 112 segregants obtained from a cross between the laboratory ( BY ) and wild ( RM ) strains of S . cerevisiae [1] and for 111 BXD mouse strain segregants [3] . Linkage analysis is commonly employed to identify DNA sequence polymorphism underlying gene expression phenotypes [1] , [3]–[14]: the gene expression levels are treated as quantitative traits and the underlying DNA polymorphisms are called expression quantitative trait loci ( eQTLs ) . Although standard linkage analysis successfully identifies eQTLs when applied to relatively small datasets , its utility in high-throughput eQTL analysis is limited due to the increased amount of background noise . To tackle this problem , a variety of methods take advantage of the modularity of biological systems and identify sequence polymorphisms that underlie an entire group of genes rather than single gene expression traits [4]–[6] , [8] , [10] , [12] . Alternatively , a number of integrative approaches combine several data sources , including promoter binding data and sequence information , to improve the accuracy of eQTL identification [3] , [14] . Several advanced methods capture not only sequence polymorphisms , but also the regulatory proteins underlying the expression changes . In those methods , the regulatory proteins are inferred concurrently with the linkage analysis , based on the approximation of regulatory protein activities by their mRNA expression level ( e . g . , [4] , [12] ) . In this study we devise a new method for characterizing the transcriptional response to DNA sequence variation . Called Regulatory-Linkage ( ReL ) analysis , it captures groups of genes together with their underlying DNA polymorphisms and their common regulatory mechanisms . The method ( Figure 1A ) takes as input genotyping and expression data for individuals in the segregating population , as well as a compendium of high-throughput transcription regulatory signatures . These regulatory signatures are gene expression profiles ( selected from the literature ) of strains mutated in particular regulatory proteins , such as transcription factors and chromatin modifiers . Our method produces a set of ‘ReL modules’ , each consisting of a triplet: a small set of regulatory proteins , a group of target genes , and a genetic linkage interval . The target genes are jointly linked to the interval and share a common transcriptional control by the regulatory proteins . We say that the module's target genes are co-regulated by the module's regulatory proteins and are co-linked to the modules' linkage interval ( Figure 1B ) . The novelty of the current approach is twofold . All three components of the ReL modules – the groups of target genes , the underlying polymorphism and the regulatory proteins – are predicted simultaneously . Extant methods predict only two of the components simultaneously and add the third one in a separate pre- or post-processing step . Moreover , we integrate high-throughput gene expression data consisting of perturbations in a large variety of transcription factors . This integrated approach has several important benefits: First , the additional regulatory information makes it possible to capture weaker linkage signals . Second , the analysis focuses on groups of target genes that have a common regulatory protein and therefore avoids groups of genes that happen by chance to be co-linked to the same genomic interval . Third , the approach infers regulatory relations based on perturbations in a variety of regulatory proteins , thereby avoiding the approximation of protein activities by mRNA expression levels . Previous studies relied on this rough approximation to infer regulatory proteins concurrently with DNA polymorphisms ( e . g . , [4] , [12] ) . Finally , the predicted regulatory proteins may suggest possible mechanisms through which genetic polymorphisms affect their target genes , providing initial interpretations of the ReL modules as part of the analysis .
Our analysis takes , as input , genotypic and expression data for a set of 112 individuals in a yeast wild-type segregating population . We organize these data as a linkage matrix , which presents the linkage ( an eQTL likelihood score ) between the expression level of each gene and each genetic marker ( Figure 1A; see Methods ) . In addition , our procedure utilizes a compendium of ‘regulatory signatures’ that includes gene expression profiles from 283 different strains mutated in a variety of regulatory proteins [15]–[16] . In the following analysis , linkage relations are evaluated based on the linkage matrix , whereas regulatory relations are assessed by preferential over- or under-expression of target gene groups across regulatory signatures . We aim to identify triplets of ( i ) target genes , ( ii ) linkage interval , and ( iii ) regulatory signatures , where the target genes are jointly linked to the linkage interval and co-expressed in the regulatory signature . The naïve approach of finding high-scoring triplets by evaluating all possible combinations is computationally infeasible even for relatively small datasets . To tackle this problem , our method proceeds heuristically in two stages . In the first stage , we organize the input as a higher order ‘ReL matrix’ across all genetic markers and regulatory signatures ( Figure 1A ) . Each entry in the matrix indicates whether genes that are strongly linked to a particular marker are also over- or under-expressed in a particular regulatory signature . This statistical measure , referred to as ReL score , is calculated as follows: For each genetic marker , we partitioned the genes into two sets: genes with high linkage to the genetic marker and the rest of the genes . Given the regulatory signature , the ReL score measures the difference in the gene expression distribution between these two sets ( see Methods ) . We now use the observation that when a group of genes is co-regulated by several regulatory proteins and is jointly linked to the same linkage interval , the corresponding ReL sub-matrix will attain high scores . In accordance , the second analysis stage ( Figure 1A ) applies a biclustering algorithm on the ReL matrix to search for sub-matrices whose average scores are higher than randomly expected . In this work , we assume a single linkage interval underlying each sub-matrix . Accordingly , the ISA biclustering algorithm [17] was adapted to choose a single range of genetic markers ( Methods ) . The biclustering output is a set of sub-matrices , each scored by its average ReL scores , and specifies a set of regulatory signatures and a single linkage interval . For each high-scoring sub-matrix , referred to as ReL module , we attached additional attributes: ( i ) A set of regulatory proteins – the proteins that were mutated in the strains from which the module's regulatory signature was obtained . ( ii ) A group of target genes - genes that are both co-regulated by the module's regulatory proteins and co-linked to the module's linkage interval ( see Methods ) . Since we focus only on trans-acting regulation , genes residing within or near the modules' linkage interval were excluded from the group of target genes . ( iii ) We hypothesize that the linkage interval contains a single gene that underlies the module's gene expression variation . We call this gene the causal regulator of the module . Among the genes within the linkage interval , we predict a plausible putative causal regulator ( see Methods; Figure 1B ) . In this analysis , we focus on the thirteen highest-scoring ReL modules ( modules with ReL score >3 ) . A comprehensive description of these modules is given in Table S1 and Table S2 . Five additional modules were highly enriched in target genes residing in telomeric or subtelomeric regions of multiple chromosomes , and therefore were excluded from the analysis ( Table S2; gene expression variation in telomeres has been discussed extensively elsewhere ( e . g . , [4] ) ) . Each of the identified ReL modules consists of at least 10 target genes . The modules comprise a total of 311 genetic markers , 82 different regulatory proteins , and 281 different target genes . Randomization analysis shows that the identified modules are highly unlikely to be generated at random ( module size P-value<0 . 05 , see Text S1 for details ) . The identified ReL modules have no overlapping linkage intervals and only a few shared regulatory proteins: Eleven regulatory signatures are shared across two modules and no regulatory signature is shared across three or more modules . This is likely to be a consequence of our biclustering approach and the small number of modules . The little overlap allows us to organize the ReL matrix into a global map of ReL modules ( Figure 2 ) . The global map highlights the existence of ‘high intensity’ sub-matrices ( modules ) . The map clearly shows that the high ReL scores within each module decrease drastically at the boundaries of its linkage interval and for regulatory signatures that are not part of the module . Table 1 summarizes the ReL modules and their function . Modules are listed along with their key ( best-scoring ) regulatory protein , putative causal regulator , and the biological processes most enriched in the target genes ( based on enrichment test; see Table S3 ) . For example , the nucleobase biosynthesis module ( module #6 ) predicts that uracil biosynthetic enzymes are linked to the causal regulator URA3 and regulated by the transcription factor Ppr1 . Indeed , Ppr1 is a known transcription regulator of uracil biosynthesis genes , and the RM parental strain carries a deletion of URA3 , a gene encoding one of the uracil biosynthetic enzymes ( see details below ) . All thirteen modules are significantly associated with a biological process ( Table 1; eleven significant enrichments based on the GO database and two additional enrichments based on SGD , see Table S3 ) . These significant enrichments give further support to the inferred ReL modules . For example , they justify the division of linkage interval II:352–697kb into two neighboring modules , #1 and #2 ( linkage intervals II:352–376kb and II:489–697kb , respectively ) , since each module is characterized by a different biological process ( ‘ribosome biogenesis’ and ‘cytokinesis’ , respectively; Table S3 ) . Module #1 consists of 32 target genes , including ten ribosome biogenesis genes and only one cytokinesis gene . In contrast , module #2 consists of thirteen target genes with seven cytokinesis genes and no ribosome biogenesis genes ( Table S1 ) . Among the genes residing within the linkage interval , the putative causal regulators ( Table 1 ) were identified based on three criteria: ( i ) genes sharing the same biological process as the target genes , ( ii ) genes that have a physical interaction with at least one of the module's regulatory proteins , or ( iii ) proteins having a preferential binding to the promoter of the target genes ( see Methods and Text S2 for a comprehensive description of causal regulator identification ) . For example , we have two indications that the causal regulator URA3 underlies gene expression variation in module #6 . First , it takes part in the same biological process as the target genes ( nucleobase biosynthesis ) , and second , it physically interacts with the module's regulatory protein Ppr1 . Out of the thirteen putative causal regulators , seven were previously confirmed ( LEU2 , URA3 , AMN1 , MAT , GPA1 , HAP1 , IRA2; [1] , [6] , [18]–[19] ) , thereby serving as positive controls . Two other putative causal regulators ( ZAP1 and CAT5 ) were proposed previously but have not been tested [4] , [6] . Two previously confirmed eQTLs ( MKT1 and FLO8 [1] , [5] ) are not included in our ReL modules . Four putative causal regulators , RFM1 , CRD1 , TRM7 and TAN1 ( modules #1 , #5 , #7 , and #13 ) , have not been previously identified . The ReL analysis predicts regulatory relations between the modules' regulatory proteins and target genes . To demonstrate the quality of these predictions , we present their agreement with known , well-established transcriptional relations . Out of six known relations , ReL detects five relations whereas compared methods detect zero and four relations ( see Text S3 for details ) . Interestingly , the nucleobase biosynthesis system was detected only by the ReL analysis . The nucleobase biosynthesis system ( module #6; Table 1 ) shows the unique ability of ReL analysis to recover not only the causal regulators , but also the regulatory proteins . The module's causal regulator is URA3 , the target genes consist of URA1 and URA4 , and the highest scoring regulatory protein is Ppr1 . The module successfully captures the current biological knowledge about the uracil biosynthesis system . The RM parental strain carries a deletion of the URA3 gene , which is known to be linked to several members of the uracil biosynthesis pathway [1] . De-novo uracil biosynthesis is catalyzed by seven biosynthetic enzymes ( Ura2 , 3 , 4 , 5 , 6 , 7 , 10 ) . Four biosynthetic enzymes ( Ura1 , 3 , 4 , 10 ) are subject to transcription regulation via the transcriptional activator Ppr1 , whose activity is negatively regulated by uracil production rate [20] . The predicted effect of URA3 mutation on URA1 , 4 is highly likely to be mediated by Ppr1 activity: in the absence of Ura3 ( RM variant ) , uracil production is reduced , causing Ppr1 activation ( through the negative feedback ) and , consequently , a transcriptional up-regulation of the uracil biosynthetic genes . Notably , although most extant methods detect the nucleobase biosynthesis module , our approach is unique in inferring Ppr1 as the regulatory protein of the module ( Text S3 ) . This difference is not surprising , as most extant methods estimate Ppr1 activity by its mRNA level , whereas the actual activity is governed by uracil production rate . Taken together , the nucleobase biosynthesis module highlights the advantage of ReL analysis in predicting regulatory proteins based on causal information , without estimating protein activities with mRNA levels . The sporulation module ( module #13 ) shows our method's ability to reveal small modules . This module consists of only seventeen genes , eight of which encode meiosis- and sporulation-specific proteins ( Figure 3A ) , linked to a locus on chromosome XV . Using previously reported mRNA expression patterns of all yeast genes through the sporulation time course , we found that these target genes are induced during mid-sporulation ( Figure 3B ) . In agreement , the module's regulatory proteins are two DNA-binding proteins , Hst1 and Sum1 , both required for transcriptional repression of middle sporulation-specific genes during vegetative growth and mitosis ( [21] , Figure 3A ) . Taken together , these results associate the module with transcription regulation of middle sporulation . Hst1 and Sum1 are two subunits [1] of the Sum1p/Rfm1p/Hst1p tertiary repression complex controlling middle sporulation genes . RFM1 is a specificity factor that directs the Hst1p histone deacetylase to some of the promoters regulated by Sum1p [22] . Notably , Rfm1 lies in the modules' linkage interval; in fact , it is located within the peak of the interval ( Figure 3C ) . It has an average eQTL likelihood score of 2 . 6 to its targets , and explains 27% of their gene expression variation . Segregants that inherited the linked locus from the wild RM showed higher expression of the sporulation module's targets than did segregants carrying the locus from the BY strain ( Figure 3D ) . The BY parent carries two polymorphisms at the RFM1 locus: P247S and N227D . Sequence alignment of six yeast species [23]–[24] showed that the proline residue at position 247 is conserved whereas only the BY strain carries the P247S polymorphism; aspartic acid at position 227 is not evolutionarily conserved ( data not shown ) . This observation suggests that the Ser247 impairs Rfm1 function , perhaps affecting the activity of the entire Sum1/Rfm1/Hst1 complex , leading to residual de-repression of mid-sporulation genes during vegetative growth . The linkage of RFM1 to expression variation has not been previously shown , probably since the signal could not be detected robustly for a small number of target genes . Our methodology overcomes this problem by exploiting the joint repressive effect of Hst1 and Sum1 during vegetative growth , enabling prediction of the genetic cause of variation in mid-sporulation genes . The two respiration modules ( #5 and #12 ) show the ability of our method to identify two distinct linkage intervals sharing the same target genes . The target genes of both modules are enriched with oxidative phosphorylation ( P10−16 in #5 , P10−41 in #12 ) , and generation of precursor metabolites and energy ( P10−13 in #5 , P10−29 in #12 ) , both of which are related to the process of aerobic cellular respiration , generating energy in the form of ATP ( Table S3 ) . The predicted causal regulators are CRD1 and CAT5 ( modules #5 and #12 , respectively ) , both required for normal respiration functionality and both residing within the peaks of the linkage interval on chromosomes IV and XV , respectively ( Figure 4A; only CAT5 was previously proposed as a causal regulator [6] ) . Cat5 and Crd1 have an average eQTL likelihood score of 3 . 5 and 2 . 6 to their targets , respectively . Cat5 is required for biosynthesis of ubiquinone , an electron-carrying coenzyme in the electron transport chain . Cardiolipin is a phospholipid of the mitochondrial inner membrane , synthesized by the Crd1 cardiolipin synthase . Absence of cardiolipin in crd1 mutants results in decreased mitochondrial membrane potential and reduced respiration activity [25] . The target genes of the two modules show lower expression in segregants carrying the linked locus from the RM strain compared to the BY strain ( Figure 4B ) . Our results point to Swi3 , but not to the common regulators of respiratory gene expression , as the key mediator of the CAT5-CRD1 effect . Swi3 is the sole predicted regulator of both respiratory modules ( Table S1 and Table 1 ) . Figure 4C demonstrates that indeed , the two respiratory modules are significantly over-expressed in the swi3 strain ( t-test P10−8 and P10−22 in modules #5 and #12 , respectively ) . Interestingly , the effect of swi3 deletion is stronger than the deletion effect of known respiratory transcriptional regulators , including Hap2/3/4/5 , Mot3 , Rox1 , Aft1/2 , and Cth1/2 ( Figure S1 ) . Swi3 is a subunit of the SWI/SNF chromatin remodeling complex , which is required for transcription of a diverse set of genes ( e . g . , mating-type switching and Gcn4 targets ) , but its specific role in respiratory gene expression has not been documented . We next investigated the interrelations between the genetic variation in CAT5 and CRD1 . To that end , we analyzed all genes that have high linkage ( eQTL likelihood >2 . 5 ) to either CAT5 or CRD1 . Interestingly , the linked genes have a strong overlap: out of the 62 genes linked to CAT5 and 29 genes linked to CRD1 , twelve genes are linked to both regulators ( hyper-geometric test P10−17 ) and contain mainly respiratory-related genes ( 11 of 12 , Figure 5A and Table S4 ) . Many of the linked genes are subunits of four respiration-related reactions: the electron transport chain , the citric acid cycle , ATP synthase , and mitochondrial carriers ( in total , 15 of 29 in module #5 and 35 of 62 in module #12 ) . Interestingly , the linked genes encode proteins that are non-randomly distributed across the various respiratory complexes: cytochrome c oxidase ( Complex IV of electron transport chain ) is exclusively encoded by genes linked to CAT5; the TCA cycle is composed of proteins encoded by the CRD1 linked group; and the genes encoding the ATP synthase complex and succinate dehydrogenase ( Complex II of electron transport chain ) are linked to both CAT5 and CRD1 ( Figure 5B ) . To test for possible genetic interactions , we compared the expression of the twelve overlapping linked genes in segregants carrying four possible combinations of the CRD1 and CAT5 alleles . Interestingly , we observed an additive effect of the CAT5-CRD1 genotypes ( Figure 5C; compare also with Figure 4B ) . Whereas CAT5 and CRD1 alone explain 22% and 17% of gene expression variation , respectively , the combination of the two eQTLs CAT5-CRD1 explains 32% of the gene expression variation . Therefore , our results indicate that a genetic interaction between the eQTL pair CAT5 and CRD1 underlies the inheritance of genes required for normal respiration .
Our approach provides a high-resolution tool for identifying functional DNA polymorphisms that affect gene expression . Importantly , it also provides insights into the mechanisms by which genotypes underlie expression changes . In our method , the regulatory signatures are gene expression profiles that were measured in rich medium under standard conditions on yeast cells carrying a single perturbation . The same methodology can be expanded to handle additional regulatory signature resources . For example , gene expression data measured under a variety of conditions may be included , disclosing modules that are inactive under standard conditions but active under particular extracellular stimuli . Furthermore , protein-DNA binding data , and data from double mutants might provide additional powerful information on ReL modules . The ReL modules should be interpreted with caution . Genetic linkage does not necessarily imply causality . Two of the three criteria used for identifying the causal regulator are aimed to select among plausible hypotheses but do not demonstrate causality ( see Text S2 for details ) . Additionally , the linkage interval might contain more than one causal polymorphism , whereas ReL analysis assumes a single causal regulator . In the case of two causal polymorphisms located at the same genomic region , ReL analysis might unify them into the same module or fail to detect one of them . Another point to consider is that the ReL modules do not provide an unbiased view of genome-wide genetic linkage . Since the modules are detected based on co-regulation in at least one regulatory signature , the resulting modules depend on the particular signatures included in the compendium . Further , some regulatory relations might be specific to a single regulatory signature , a short linkage interval , or a small number of target genes . ReL analysis may not have enough statistical power to generalize those focused relations into a module . Finally , our modules currently contain only a single linkage interval . Hence , ReL analysis might fail to detect the prevalent case where the target genes are influenced by a combination of multiple interacting loci . It might be possible to extend our framework to detect such interactions automatically . For all these reasons , our method may fail to identify certain correct modules despite a detectable causal polymorphism . ReL analysis is likely to succeed in organisms other than yeast , including mouse and human . Several genotypic and gene expression datasets are available for these populations [26]–[27] , and thus the most prominent obstacle is the lack of a large compendium of mammalian regulatory signatures . Such a resource , however , is likely to be compiled in the future , and the ReL methodology provides a good example of its usefulness . Text S4 provides a quantitative estimation of the number of regulatory signatures required for significant ReL analysis , highlighting the importance of a large compendium . As new technologies for cost-effective count of transcripts in perturbed cells become available ( e . g . , nCounter [28] , shRNA-perturbation ) , it will be soon easier to obtain a large collection of mammalian regulatory signatures and apply our methodology to them . When applied to the yeast system , our methodology reveals two intriguing ReL modules . First , we find that DNA polymorphism in RFM1 underlies gene expression variation of middle sporulation genes . Second , we show that both CRD1 and CAT5 underlie gene expression variation in aerobic cellular respiration genes . Further analysis reveals a novel genetic interaction ( epistasis ) between these two loci . It would be of great interest to explore whether the regulatory mechanisms uncovered here are conserved in other fungal genomes . The discovery here of previously uncharacterized modules and interactions in the well-studied segregating yeast population underscores the importance of large-scale integrated methods in genetic analysis .
We calculated the linkage ( an eQTL likelihood score ) of genotypic and expression data measured for 112 individuals in a yeast segregating population , as described previously [1] . The linkage matrix represents genetic markers versus genes , where each entry corresponds to the eQTL likelihood score between a given genetic marker and the expression of a given gene . The analysis was applied to all 2956 markers that were genotyped , and all 6230 genes whose gene expression was measured across the segregating population . We formed a compendium of 283 high-throughput expression profiles obtained from strains mutated in various regulatory proteins [15]–[16] . The compendium includes only strains mutated in a single gene , and each mutant strain is represented by exactly one expression profile . The expression profiles are referred to as regulatory signatures . Given a genetic marker and a regulatory signature , we evaluate whether genes that are tightly linked to the genetic marker are also over- or under-expressed in the regulatory signature . To that end , we partition the genes into two subsets: genes with high linkage to the genetic marker ( denoted high-linkage genes ) , and the rest of the genes . The difference in the distribution of the regulatory signature values between the two subsets is evaluated using a t-test . The ReL score is the −log10 P-value of this t-test ( all reported ReL scores are Bonferroni corrected ) . In our analysis , 11 , 166 of the 836 , 548 ReL scores ( 1 . 3% ) were significant at P<0 . 001 ( see Text S1 ) . Given that the high-linkage ( the rest ) genes tend to have high ( respectively low ) regulatory signature values , the group of hit genes includes all those high-linkage genes whose values are above ( respectively below ) the average regulatory signature value . The hit genes are later used to calculate the target genes of the ReL modules . The eQTL likelihood threshold , which distinguishes the high-linkage genes from the rest of the genes , was identified as follows: First , genes that are over-expressed and genes that are under-expressed in the regulatory signature are identified . For every possible eQTL likelihood threshold , we test for the over-representation of high-linkage genes in one of these expression groups using a hyper-geometric score ( we consider all observed eQTL likelihood values as thresholds ) . The best score determines the eQTL likelihood threshold . The combination of hyper geometric score and the t-test is important for a robust evaluation . Unlike a t-test , the hyper-geometric test takes into account the amount of high-linkage genes , making sure that the eQTL likelihood threshold is not too high; on the other hand , unlike the hyper-geometric test , the t-test estimates the significance of difference between two distributions . Text S5 demonsrates the robustness of ReL analysis to small changes in the eQTL likelihood threshold . The ReL matrix summarizes the ReL scores across all genetic markers and regulatory signatures . We set out to construct a group of co-regulated genes whose common transcription regulation involves both regulatory proteins and a causal regulator . In the ReL matrix , such an event appears as a sub-matrix with significant over-representation of high ReL scores . To identify those sub-matrices , the ISA biclustering algorithm [17] was adapted to work on the ReL matrix . ISA looks for any subset of columns and any subset of rows whose sub-matrix has high scores; the sub-matrix is subject to iterative improvements by adding or removing any column or row . Here we seek sub-matrices with a single range of consecutive genetic markers rather than any subset of markers . To that end , we modified the original ISA so that only markers at the boundaries of the current genetic marker range can be added or removed . On each ISA step , the genetic marker range is optimized efficiently using a dynamic programming algorithm . We start from all possible single entries as seed sub-matrices , and optimize each such seed independently of all others ( see Text S6 for details ) . The resulting sub-matrix is called a ReL module . The ReL score of a module is the average ReL score of its entries . A ReL module specifies a single range of genetic markers ( referred as a linkage interval ) and a set of regulatory signatures . For each ReL module , we further compiled the following information: ( i ) Each module is associated with a set of regulatory proteins corresponding to the deletion mutants in the module's regulatory signatures . The ReL score of a regulatory protein is its average ReL score over the linkage interval . ( ii ) As defined above , each entry of the ReL matrix is associated with a set of hit genes . The module's target genes are all hit genes included in at least 60% of the sub-matrix entries . Here we aim to investigate trans-acting regulation , and therefore , to avoid biases related to cis-acting regulation , genes residing within the linkage interval or less than 30 genes away from it were excluded from the set of target genes . In all thirteen modules under analysis , the original fraction of cis-linked genes was relatively small ( Table S2 ) . Next , the function of the set of target genes is characterized by a hyper-geometric enrichment test using the GO biological process annotation ( computed using the EXPANDER software [29]; all reported P-values are corrected for multiple testing ) . Given one or more significantly enriched biological processes for the same set of target genes , the best scoring process is termed the primary biological process of the module . ( iii ) A causal regulator is a gene carrying a polymorphism in its promoter or coding region , which has a trans-acting effect on expression variation of other genes . For each ReL module , we aim to find one or a few putative causal regulators – genes contained within the linkage interval that are highly likely to be the causal regulators of the target genes . Following Tu et al . [7] , we predict a putative causal regulator based on the following rules: The causal regulator either plays a role in the primary biological process of the module , or the yeast protein-protein and protein-DNA interaction network contains at least one direct link between the causal regulator and the module's regulatory proteins . Alternatively , the module shows statistical significant enrichment for targets of the causal regulator ( see Text S2 for details ) . Taken together , a full description of a module includes a set of regulatory proteins , a ( small ) set of putative causal regulators , and a set of target genes characterized by a primary biological process . A program implementing our framework is available on the website: http://acgt . cs . tau . ac . il/ReL/ . | High-throughput genotypic and expression data for individuals in a segregating population can provide important information regarding causal regulatory events . However , it has proven difficult to predict these regulatory relations , largely because of statistical power limitations . The use of additional available resources may increase the accuracy of predictions and suggest possible mechanisms through which the target genes are regulated . In this study , we combine genotypic and expression data across the segregating population with complementary regulatory information to identify modules of genes that are jointly affected by changes in activity of regulatory proteins , as well as by genotypic changes . We develop a novel approach called ReL analysis , which automatically learns such modules . A unique feature of our approach is that all three components of the module—the genes , the underlying polymorphism , and the regulatory proteins—are predicted simultaneously . The integrated analysis makes it possible to capture weaker linkage signals and suggests possible mechanisms underlying expression changes . We demonstrate the power of the method on data from yeast segregants , by identifying the roles of new as well as known polymorphisms . | [
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"genetics",
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] | 2010 | Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast |
Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost . Routing of messages along shortest paths requires global information about the topology , while spreading by diffusion , which operates according to local topological features , is informationally “cheap” but inefficient . We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network . The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk ( diffusion ) communication processes at the limiting extremes . We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bias on the network’s communication cost . We identify routing strategies that approach a ( highly efficient ) shortest-path communication process with a relatively small global information bias on the system’s dynamics . Moreover , we show that the cost of routing messages from and to hub nodes varies as a function of the global information bias driving the system’s dynamics . Finally , we implement the model to identify individual subject differences from a communication dynamics point of view . The present framework departs from the classical shortest paths vs . diffusion dichotomy , unifying both models under a single family of dynamical processes that differ by the extent to which global information about the network topology influences the routing patterns of neural signals traversing the network .
We model messages or signals transferred from a source brain region to a target brain region as random walkers traversing a brain network , where network nodes and edges represent small cortical parcels that are connected by bundles of axons . We consider the dynamics of such random walkers ( signals/messages ) on the network , where walkers must reach an a priori specified target node t . Formally , let X be a random variable indicating the current node of the walker , Y the random variable indicating the node to which the walker will move in the next time step , and T the random variable indicating the target node where the walk will terminate ( we assume that all nodes can be reached from all nodes in finite time ) . For all t , we denote the transition probabilities at X = i as: pijt = Pr ( Y = j|X = i , T = t ) where ∑j pijt = 1 , and pijt = 0 when there is no connection between nodes i and j . Finally , the walk ends when i = t , in which case pijt = 1 for j = t and 0 for all other j . Formally , the network dynamics for each separate target t form a Markov chain with state t as an absorbing state ( see Methods ) . The set of transition probabilities for all t express the routing strategy that governs the dynamics of walkers ( signals ) navigating the network . We specify transition probabilities at every node using a family of dynamical processes that combine local and global information about the network’s topology . To this end , we define the dynamics of the system by tuning a global information bias using the following stochastic model: Pλ ( Y=j|X=i , T=t ) =exp ( − ( λ ( dij+gjt ) +dij ) ) 1Zit ( 1 ) where Zit=∑jexp ( − ( λ ( dij+gjt ) +dij ) ) is a normalization factor . Transition probabilities are governed by two sources of information: The parameter λ controls the extent to which transition probabilities are shaped by global information . Most importantly , λ gradually changes the dynamics on the network from an unbiased random walk towards a shortest-path routing strategy ( see Fig 1 ) : It is worth noting that the model acts on the routing strategies by changing the transition probabilities at each node , but we assume that the topology and weight structure of the network remain unchanged ( see Fig 1 ) . This procedure was formally introduced by Lambiotte et al [27] , who showed that a biased random walk on a network A can be interpreted as an unbiased random walk on an appropriately defined flow graph A’ , where the weights of the connections of A’ dictate the patterns of flow of a diffusion process at equilibrium . We are interested in characterizing the communication cost of the dynamics generated by our model as we gradually increase λ , therefore increasing the global information bias on the dynamics . Here , we focus on two aspects of the cost associated with a communication process . First , we consider a transmission cost , which is the cost associated with messages being transmitted from one node to another . Second , we consider an informational cost , which is the cost associated with using global information to reshape the system’s dynamics and thus route messages efficiently . We consider a walker navigating the network and acting according to the routing strategies Pλ ( Y| X , T ) . Let cλtrans ( i , t ) = ∑j Pλ ( Y = j| X = i , T = t ) dij be the immediate transmission cost at node i for a walker going to node t with routing strategy Pλ ( Y| X = i , T = t ) . The immediate transmission cost quantifies the cost associated with X = i partaking in the communication process by relaying the message to one of its neighbors , and in this setting it is equivalent to the expected distance that a walker at node i has to travel to move to a neighbor of i . Let ntλ ( i , k ) be the mean number of times node k is visited by a walker starting at a source node X0 = i and acting according to a routing strategy Pλ ( Y| X = i , T = t ) . We define the transmission cost of a walk starting at source node X0 = i and terminating at the target node t as the sum of the immediate transmission costs accumulated at each visited node along a walk , that is Cλtrans ( i , t ) = ∑k ntλ ( i , k ) cλtrans ( k , t ) . Thus , a walk’s transmission cost is equivalent to the mean walk length between nodes i and t , under the routing strategy defined by λ . Noting that the transmission cost is not a symmetric measure , ( i . e . Cλtrans ( i , t ) may not be the same as Cλtrans ( t , i ) , except for when λ → ∞ ) , we can define the average transmission cost of a node acting as a source as C→λtrans ( i ) =1N∑tCλtrans ( i , t ) , and the average transmission cost of a node acting as a target as C←λtrans ( t ) =1N∑iCλtrans ( i , t ) . These measures quantify the source and target closeness centrality of each node under a routing strategy: C→λtrans ( i ) quantifies the average walk length from a node i to any other target node in the network , whereas C←λtrans ( t ) quantifies the average walk length from any source node to the target node t . To quantify the informational cost associated with routing messages to a target node t under the routing strategy Pλ ( Y| X = i , T = t ) , we define cλinfo ( i , t ) = KL ( Pλ ( Y| X = i , T = t ) ||Pref ( Y| X = i , T = t ) ) as the informational cost at node X = i , measuring the Kullback-Leibler divergence between the routing strategy Pλ ( Y|i , t ) and the null model or reference routing strategy Pref ( Y|i , t ) . The Kullback-Leibler divergence measures the additional bits of information required to manipulate the outgoing transition probabilities at a given node , and adopt a routing strategy that deviates from the reference routing strategy , Pref ( Y|i , t ) . Hence , the informational cost quantifies the effect of the global information bias by measuring the extent to which the biased dynamics Pλ ( Y|i , t ) deviate from Pref ( Y|i , t ) at node X = i [34] . Then , the informational cost of routing a message from a starting at node X = i to a target node t is the weighted average informational cost across all nodes in the network , weighted by the frequency with which each node is visited along the walk: Cλinfo ( i , t ) = ∑k ( nλt ( i , k ) ∑rnλt ( i , r ) cλinfo ( k , t ) ) . Finally , we can define the average informational cost of a node acting as a source as C→λinfo ( i ) =1N∑tCλinfo ( i , t ) , and the average informational cost of a node acting as a target as C←λinfo ( t ) =1N∑iCinfoλ ( i , t ) . It is important to note that , in order to model the system’s dynamics and construct a matrix of transition probabilities Pλ ( Y| X , T ) , the shortest path-length between all node pairs ( i . e . the global information term dij + gjt ) is required , for any λ > 0 , as an input parameter to the model . Once Pλ ( Y| X , T ) are defined by the model , the knowledge about the global topology becomes “fuzzy” or probabilistic andthe dynamics become autonomous; the walker trajectories on the network will evolve according to the dynamics dictated by Pλ ( Y| X , T ) . There is no need for the neural elements to store the global information in a table , as any information about the global topology is “implicitly encoded” as a bias on the random walk , and the degree of such bias is precisely what we quantify with the informational cost measure . In the following sections we will study the communication costs of routing strategies generated by our stochastic model applied to the structural brain connectivity matrices of two cohorts of healthy subjects . In the main text , we focus on 173 unrelated subjects from the Human Connectome Project ( HCP ) dataset [35 , 36] . The Supplementary S1–S5 Figs show results from the replication dataset ( LAU ) , composed of 40 healthy subjects ( see Methods ) . We first analyze cost measures at the global , nodal , and pairwise level , measured and averaged across all subjects ( within each cohort ) . Lastly , we examine individual subject differences with respect to the proposed cost measures . We emphasize that our aim is not to identify a routing strategy for brain communication , but instead , to expose a spectrum of communication dynamics that unifies the classical shortest-paths routing vs . diffusion dichotomy [12 , 37] . By construction , the transmission and informational cost have a competing relationship ( or trade-off ) such that as we increase λ in the stochastic model , the mean walk lengths ( Cλtrans ) of messages acting according to Pλ become shorter while the bias effect due to global information ( Cλinfo ) increases . This trade-off is shown in Fig 2A where averages of Cλtrans and Cλinfo across all {i , t} pairs are plotted as a function of λ . It can be seen that Cλtrans , measuring the average walk length , approaches a shortest path-length regime at around λ = 1 ( ln ( λ ) = 0 in Fig 2 ) , suggesting that in this regime messages can be efficiently routed at a low informational cost . Next , we consider an ensemble of random networks and compare average transmission and informational costs incurred in empirical brain networks and in randomized ensembles of networks . All randomized networks preserve node degree , node strength ( evaluated with respect to the proximity edge-weights ) , and the network’s weight distribution ( see Methods ) . We generate routing strategies Pλ for all randomized networks and normalize the cost measures Cλtrans and Cλinfo of each subject’s empirical brain network with respect to the average cost measures computed across the corresponding randomized counterparts . Fig 2B shows normalized cost measures ‖Cλtrans‖=Cλtrans ( emp ) /Cλtrans ( rand ) ( red line ) and ‖Cλinfo‖=Cλinfo ( emp ) /Cλinfo ( rand ) ( blue line ) as a function of λ . In accordance with prior work ( 37–39 ) , we find that average walk lengths are shorter for random networks ( i . e . ‖Cλtrans‖ > 1 ) at the extremes of the spectrum , representing the unbiased random walk ( Pref ) and shortest path regimes . Interestingly , our analysis reveals an interval of λ values ( shaded region in Fig 2B ) for which empirical networks exhibit shorter walk-lengths compared to the randomized counterparts ( i . e . ‖Cλtrans‖ < 1 ) . Moreover , the informational cost behaves similarly , although the regions ‖Cλinfo‖ < 1 and ‖Cλtrans‖ < 1 barely overlap . Overall , these results show that the randomized counterparts of empirical brain networks are more efficient only at the extremes of the communication spectrum . Fig 2C and 2D show pairwise Cλtrans and Cλinfo ( median across subjects ) computed for routing strategies generated with λ1 = e-4 . 49 , λ2 = e-1 . 64 , λ3 = e0 . 37 and λ4 = e1 . 79 ( see dashed vertical lines in Fig 2A and 2B ) . These values of λ correspond to two points located near the extremes of the communication spectrum , and two points located at the minimum and maximum of the curve ‖Cλtrans‖ , where the empirical networks are most and least efficient compared to their randomized counterparts . As evidenced by the column-like patterns in the matrices corresponding to λ1 and λ2 , the dynamics of messages navigating the network are strongly determined by the local connectivity of the target node when the global information bias is small . As the bias increases and routing strategies depart from the reference strategy Pref , the dynamics of messages are less dependent on the target node only . Finally , as walk-lengths converge towards shortest-path , the transmission cost becomes symmetric , i . e . , Cλtrans ( i , t ) = Cλtrans ( t , i ) . We now analyze cost measures at the nodal level . Fig 3A and 3B show scatter plots of the average source and target transmission costs ( C→λtrans and C←λtrans , respectively ) , and the average source and target informational costs ( C→λinfo and C←λinfo , respectively ) associated to all nodes ( median across all subjects ) for the same values of λ highlighted in Fig 2 . Nodes are colored according to their membership in functional intrinsic connectivity networks ( ICNs; see Methods ) , highlighting a tendency of some ICNs to contain an overabundance of costly sources and/or targets , while other ICNs’ cost varies as a function of λ . Interestingly , nodes belonging to the unimodal networks , namely the visual ( VIS , colored red ) and somatomotor ( SM , colored green ) networks , exhibit less variability across cost measures . Nodes belonging to the somatomotor network tend to exhibit a high C→λtrans and low C←λtrans for λ < e0 . 37 , while they also exhibit a consistent low C→λinfo; nodes belonging to the visual network exhibit high C→λinfo and C←λinfo for λ>e-4 . 49 , while C→λtrans and C←λtrans vary as a function of λ . We also note that the dorsal attention regions ( DA , colored purple ) consistently exhibit low C→λtrans and C←λtrans for λ>e-4 . 49 . In order to assess to what extent high or low nodal costs are driven by the network’s overall topology , as opposed to nodal degree or strength distribution , we standardize nodal costs with respect to the corresponding nodal cost distributions measured on the randomized network ensembles . Significantly high or low standardized nodal cost measures are indicative of global connectivity patterns that are encountered only in empirical brain networks . Supplementary S6 and S7 Figs show thresholded z-scores ( α = 0 . 01 ) for all nodal cost measures as a function of lambda . As expected , near the extremes of the spectrum ( λ = 0 and λ > 1 ) , most nodes exhibit significantly higher costs , compared to the randomized networks , however , significantly low cost regions are found in the middle of the spectrum . Prominent low C→λinfo regions include the right and left hemisphere frontal , precentral , paracentral and postcentral regions; low C←λinfo regions include the right and left posterior cingulate , the supramarginal gyrus , the superior parietal cortex , the precuneus , and the inferior parietal cortex . Prominent low C→λtrans regions are mainly located in the frontal cortex ( frontal pole , medial orbital frontal and rostral middle frontal regions ) , right and left superior parietal regions , the right and left precuneus , and the left cuneus . Interestingly , no significantly low C←λtrans regions were identified , suggesting that randomized topology favors all regions as routing targets , but not as routing sources . Our analyses also reveal a varying relationship ( as a function of λ ) between the nodal cost measures and node strength ( see Fig 3C ) . At the extremes of the spectrum , transmission cost is strongly driven by node strength . When λ = 0 , the correlation between node strength and C→λtrans and C←λtrans is r = 0 . 55 and r = -0 . 61 , respectively ( p < 0 . 001 ) , indicating that high strength nodes ( hubs ) are costly sources but low cost targets with respect to transmission cost . In other words , when the global information bias is low ( or zero ) , messages can be routed at a low transmission cost from any brain region to a hub; conversely , routing a message from a hub to any brain region incurs a high transmission cost . At the other end of the spectrum ( i . e . for large values of λ ) , hub nodes are low cost sources and targets with respect to transmission cost ( r = -0 . 53 , p<0 . 001; note that the orange and red lines in Fig 3C converge ) . However , in the middle of the spectrum , the average correlation between node strength and C→λtrans is close to zero , whereas the correlation between node strength and C←λtrans remains significant ( r ≈ -0 . 5 , p<0 . 001 ) throughout the entire spectrum . This varying relationship between node strength and cost measures as a function of λ highlights a distinction between the dynamical measures proposed here , and static centrality measures that rely only on the network’s structure . Static measures such as betweenness centrality , page rank [38] and communicability [39] , are strongly driven by nodal degree or strength ( See Supplementary S1 Text showing a comparison between Cλtrans and a set of static centrality measures ) , but are blind to the patterns of flow imposed by the network structure and the dynamics of the system . The relationship between source and target costs also varies as a function of λ ( see Fig 3D ) . For low values of λ , both C→λtrans and C←λtrans , and C→λinfo and C←λinfo are negatively correlated . In other words , nodes that are costly sources are efficient targets , and nodes that are costly targets are efficient sources . However , the correlations undergo a sign flip as λ increases and C→λtrans and C←λtrans , and C→λinfo and C←λinfo become positively correlated . Note that the correlation between C→λtrans and C←λtrans converges to 1 as these two measures are identical at the shortest-path extreme ( the symmetry between C→λtrans and C←λtrans at the shortest path extreme will hold for any undirected network ) . A node’s propensity to be a costly transmission/informational source or target is projected onto the cortical surface in Fig 4 , where we show the difference between a node’s source and target costs for λ1 = e-4 . 49 , λ2 = e-1 . 64 , λ3 = e0 . 37 and λ4 = e1 . 79 ( same values highlighted in Fig 2 and Fig 3 ) . Cortical regions that are costly sources ( compared to the cost of being a target ) are colored red whereas regions that are costly targets ( compared to the cost of being a source ) are colored blue . This analysis reveals that dorsal portions of the precentral and postcentral gyri are increasingly costlier sources in terms of transmission cost , whereas frontal regions of the temporal lobes and inferior frontal areas are increasingly costlier targets , as λ increases . In terms of informational cost , we see the opposite relationship in the same anatomical regions , but the informational cost differences decrease as λ increases . In this section we will explore a different scenario where , in the interest of economizing on informational cost , we allow only a subset of privileged nodes to be affected by the global information bias . We consider increasingly larger size sets of r privileged nodes that are able to reshape their routing strategies according to the influence of global information . Privileged nodes are selected according to different node centrality rankings . Given a centrality-based ranking of nodes , we generate routing strategies for the r-highest ranked ( privileged ) nodes according to the stochastic model , where λ is an attribute that only applies to the set of privileged nodes; all non- privileged nodes’ routing strategies remain unbiased and are equal to Pref ( X ) . The left and middle panel of Fig 5 show network average values of Cλtrans and Cλinfo ( median across all subjects ) as a function of λ for varying fractions of privileged nodes that are selected according to various centrality-based rankings . The black dotted lines show Cλtrans and Cλinfo , respectively , for the case in which all nodes’ routing strategies are biased . This approach reveals three interesting properties about the routing capacity of the brain . First , the composition of the set of privileged nodes matters , as evidenced by the differences in Cλtrans and Cλinfo that are obtained as the set size and composition is varied . Second , for a fixed number of privileged nodes , the more the system economizes on informational cost , the more it expends on transmission cost . For example , routing strategies where we select privileged nodes according to betweenness centrality ranking yield smaller Cλtrans and larger Cλinfo throughout the entire spectrum , compared to other centrality-based privileged node selections . Conversely , routing strategies where we select privileged nodes according to a random walk centrality ranking are the most costly in terms of Cλtrans , but least costly in terms of Cλinfo . Third , a small number of strategically selected privileged nodes can achieve a Cλtrans that approximates the Cλtrans achieved when all nodes are subject to the global information bias . To show this , we compute the stretch of a walk [25] defined as the absolute difference between optimally shortest path lengths obtained when all nodes’ dynamics are biased by global information , and shortest walk length obtained when only privileged node’s dynamics are biased by global information . Node stretch distributions ( medians across all subjects ) are shown in the right-side panel of Fig 5 . We note that when the top 25% betweenness centrality nodes are selected as privileged nodes , the average stretch is only 4 . 2 , in contrast to a stretch of 12 . 7 obtained when the top 25% random walk closeness centrality nodes are selected . Overall , these results indicate that efficient routing patterns can emerge even when less than half of the nodes are capable of routing information . Our approach allows us to study the variability of communication cost measures across subjects . We first examine whether subjects who exhibit higher values of Cλtrans at λ = 0 ( that is , longer walk lengths for the unbiased random walk ) will maintain a high Cλtrans throughout the entire spectrum . Fig 6A shows correlations between all subject’s Cλtrans across all values of λ . These correlations show that subjects who exhibit higher values of Cλtrans at λ < e-3 . 1 are also subjects with the highest Cλtrans at λ >1 , but the relationship is inverted in the middle of the spectrum . Finally , we investigate if there are differences in how individual subject’s brain networks take advantage of the global information bias . We address this question by measuring the area under each subject’s Cλtrans curve and Cλinfo curve . Moreover , since we are interested in capturing the rate of decay and growth of subject’s Cλtrans and Cλinfo curves , we first normalize each subject’s Cλtrans curve with respect to Cλtrans at λ = 0 ( that is , the average length of unbiased random walks ) , and we normalize each subject’s Cλinfo curve with respect to Cλinfo at λ = e3 . 2 ( that is , the max value of Cλinfo ) . The normalized Cλtrans and Cλinfo curves of 8 subjects are shown in Fig 6B , illustrating curves that decay/grow faster with λ , which we can capture by measuring the area under the curve . Fig 6C shows a scatter plot of the areas under the normalized Cλtrans and Cλinfo curves of all subjects , exhibiting a strong negative correlation between the normalized areas under Cλtrans and Cλinfo ( r = -0 . 74 , p<0 . 001 ) . This strong relationship indicates that there is a trade-off between a brain network’s ability to take advantage of global information to route messages in a fast manner , and the amount of informational cost required to achieve optimally fast routing . How this trade-off is negotiated varies across individual subjects .
The efficiency of communication in real world networks is not only determined by the speed with which messages are relayed , but the informational cost associated with selecting efficient routes is equally important . Here we introduce a stochastic model that generates routing strategies on a network by controlling the effect of global information over the actions of random walkers . We characterize the trade-offs between the cost of reshaping the system’s dynamics ( Cλinfo ) and the cost of relaying messages through the network ( Cλtrans ) , and characterize these costs at a global , nodal and subject-wise level . Our results show that biased random walk dynamics can rapidly approach a shortest-path communication regime when afforded gradual small increases in the bias on global information . The concept of communication dynamics has become increasingly important in the context of brain networks [40 , 41] . Here , we address some of the assumptions behind two widely used brain communication models , namely routing and diffusion models . On the one side , communication that takes place through shortest paths assumes that neural elements are able to identify the optimal path and route a signal/message through such path; however , the mechanisms by which signals are routed and the informational cost associated with routing them are rarely discussed . On the other side , communication that takes place through ( unbiased ) random walks assumes that signals are able to “bounce between nodes” for long periods of time . Yet , such a scheme raises issues about signal integrity and strength as well as metabolic cost . Our framework unifies these two extreme communication strategies under a family of communication models that can be characterized by the extent to which global information about the network topology biases the dynamics that shape the patterns of flow within the network . Under the framework presented here , communication cost is not measured as a structural property of the network [17 , 24 , 37] . While wiring cost affects brain communication by means of being an important driver of brain geometry and network topology [1 , 17 , 15] , it should be noted that wiring cost is a static property of the network ( within relatively short time-scales ) that is invariant under any communication process taking place on the network . In contrast , our framework approaches communication cost by considering two different cost components that are measured from the modeled dynamics of neural signals traversing the network under a specific routing strategy . First , we consider the transmission cost which we interpret as a proxy for the metabolic cost of transmitting neural signals from one neural node to another . It has been estimated that about 50% of the brain’s energy is used to drive signals across axons and synapses [1] , suggesting that energy consumption is a strong incentive to minimize the length of communication pathways in neural systems . Second , we consider the cost of reshaping the patterns of information flow ( informational cost ) that allow a signal to be efficiently routed towards a specific brain region . We conceptualize this cost as associated with modulatory processes that take place at the mesoscale or microscale , where signal traffic may be regulated as two neuronal population’s firing rates change in order to synchronize and thus communicate [42] , or as a process that emerges on top of the collective oscillatory dynamics of neural elements [43] . As our work is focused on macro-scale brain networks , it is important to note that we cannot claim that the signals we are modeling represent individual action potentials traveling along neuronal axons . Instead , we conceptualize neural signals as emerging from the coordinated activity of large populations of neuronal circuits and sub-systems . Under this higher order perspective , the signaling dynamics that we model represents the flow of information through the network’s connections . Our results contrast with well-established notions about the efficiency of random topologies [44 , 45] , as we demonstrate that the randomized counterparts of empirical brain networks are only more efficient at the extremes of the communication spectrum . Interestingly , we find that within the regime where empirical networks are most efficient with respect to the randomized models , the frontal cortex has an overabundance of efficient source nodes , both in terms of information and transmission cost; conversely , the posterior and parietal regions of the cortex exhibit an overabundance of efficient target nodes in terms of information cost . We note that this behavior is only found in a limited regime that does not include the extremes of the communication spectrum . The implications of these findings are twofold . On the one hand , they demonstrate that cost-efficiency measures are relative to the communication process under consideration , and on the other hand , they raise questions regarding the use of appropriate null models as benchmarks to normalize graph-theoretic measures [12 , 46] , as we have shown here that the randomized topology is not always more efficient than empirical networks . Fundamentally , the cost measures that we consider here intrinsically capture the informational cost associated with traversing high-degree and high-strength nodes , that is , those comprising the brain’s rich club . Indeed , it has been proposed that rich-club nodes facilitate integration of information within the network at the expense of a high wiring cost [24]; nonetheless , hubs are only advantageous for communication if signals can be routed through them , which implies high informational cost [47] . Here we show that at the low-information end of the spectrum hub nodes are low cost targets but are high cost sources . It is only when we increase the global information bias that hubs become low cost sources and targets , but at the expense of an overall higher informational cost . Interestingly , a strong relationship between node degree , and the directionality with which signals are preferentially transferred through a network has been found in analytical , computational and empirical studies [48 , 49] , where it has been noted that high degree nodes’ oscillatory activity lags in phase whereas low degree nodes’ activity leads . These findings match the routing patterns that we find here but only at the low information-end of the spectrum , where hub nodes are efficient directional targets , while low degree nodes are efficient sources [49] . Our findings regarding the selection of privileged nodes that have access to global information show that some nodes are poised to take advantage of global information more efficiently than others; in brain networks , efficient routing patterns can be achieved by allowing as few as 25% of the highest betweenness or strength centrality nodes to reshape their routing strategies according to a bias on global information . These results offer a new perspective on the role of highly central nodes in facilitating the co-existence of functional integration and segregation between and within neural sub-systems: densely connected clusters of nodes ( network communities ) tend to “trap” random walkers [50] which promotes segregation , however a few well-connected privileged nodes that are specialized to direct the exchange of information between clusters can promote efficient integration of information . Hence , the privileged nodes framework presented here may provide some insight about the underlying communication processes allowing the exchange of information between modular sub-systems [51 , 52] . Finally , our study of individual differences not only expose an interesting trade-off between transmission and informational cost across subjects , but show that the measures are sensitive to individual differences . This is a promising avenue for future studies focusing on communication processes differences across clinical populations and human lifespan [53] . Several properties inherent in this framework have important implications for the future study of communication processes in brain networks . First , the routing patterns presented here are derived from a dynamical point of view , and not from a purely topological analysis of the system , allowing us to make use of well-established theoretical results about linear processes and biased random walks [21 , 26–31] . Second , discounting the extreme case of shortest path walks , the routing patterns generated by the model take place through multiple paths , promoting robustness to structural failures , and a higher tolerance to abundant signal traffic . Third , while we do not formally define a measure of communication efficiency in this study , it is worth noting that a natural derivation from the transmission cost measure results from its reciprocal ( or inverse ) , thus extending and generalizing the global ( or routing ) efficiency [44] and diffusion efficiency [12 , 37] measures for shortest path and diffusion-based communication , respectively . Fourth , routing strategies at each node are dynamic , opening up the door to potential directions of further investigation focusing on the impact that functional demands and the availability of metabolic resources may have on the repertoire of routing patterns in brain networks . Finally , building on the concept of dynamic routing patterns , the notion of dynamic measures of centrality emerge naturally as a means to quantify the varying importance of nodes and edges under different underlying dynamics [27 , 31] . Here we have proposed the nodal cost measures C→λtrans , C←λtrans , C→λinfo and C←λinfo as dynamic source and target closeness centrality measures , but we note that additional centrality measures can be evaluated , such as the number of times that nodes are visited during a biased random walk ( this centrality measure would converge to the betweenness centrality and random walk centrality at the extremes of the spectrum ) . It is worth noting that the model we present here is only one way to formularize the spectrum between shortest paths and ( unbiased ) random-walk communication ( see for example [54 , 55] ) ; different formulations of the spectrum may generate different families of communication models , which presume different assumptions about the cost of communication processes . For instance , our spectrum excludes communicability [39] , a communication processes that takes into consideration all possible walks between a pair of brain regions . Like diffusion , communicability admits sub-optimal and parallel signal traffic , but unlike diffusion , communicability is blind to the patterns of flow imposed by the local properties of the network , and therefore , it presupposes some degree of knowledge about the global topology of the network in order to ensure that all walks of length k are equally likely to be used by signal traffic . Therefore , the informational cost measure that we propose here would not be appropriate to capture the informational cost of communicability . Another subtle but important consideration is the question of how is global information made available to the system . The model we propose takes as an input parameter the global information about the network topology in the form of a pairwise shortest path distance . Therefore , the model does require full knowledge about the network topology . However , this does not imply that elements of the system have access to such information as this is a model of the dynamics , not a model for the underlying mechanisms that may generate the dynamics . It is still an open question how the system gains information about the global topology of the network , and what mechanisms dictate what connections are used to transmit neuronal signals . Biological systems are the product of evolution , adaptation , development and learning; one possibility is that , through these processes which continuously act to improve the system’s performance , neural systems have gained information about their topology through feedback , resulting in an incremental update of the system’s dynamics . Some limitations are worth mentioning . First , for this study , our application of the stochastic model is limited by restricting λ to be a global attribute for all nodes , or for a set of privileged nodes; nonetheless , it is feasible ( although computationally expensive ) and perhaps more realistic to define λ as a continuously varying nodal property , λ ( i ) . Second , the stochastic model considers a scenario where communication between all nodes and a given target is equally salient . In systems such as the brain , where different sub-systems are associated with specific cognitive tasks , it is unlikely that all node pairs require the ability to efficiently exchange information with all other nodes . In this sense , the cost measures computed here may serve as an upper bound for the actual communication cost , however , it is important to keep in mind that our model does not consider issues of congestion that can arise as the traffic capacity of the network is exceeded [54 , 56] . Third , linear dynamics may not be appropriate for systems that exhibit highly complex non-linear dynamics . Indeed , the brain is highly complex , topologically and dynamically . Yet , its complexity allows us to study it at different scales [57] . While it is clear that both structure and dynamics must be considered simultaneously to achieve a more comprehensive description of the system , it is still unclear how communication dynamics manifest at the various scales at which we are able to capture brain structure and dynamics . As pointed out in comparative analysis performed by Messe et al . [58] , complex models of brain activity can effectively be reduced to simpler ( linear ) processes that are easier to dissect and understand . Hence , there is no evidence to discard linear dynamics as good approximation of the routing patterns taking place on large-scale brain networks . An interesting avenue to pursue is the exploration of higher-order models of flow , where transition probabilities are conditioned by past visited nodes . Finally , a goal for future work is the design of novel experimental strategies that can connect our current understanding of brain network topology and communication dynamics , illuminating the empirical problem of how brain networks integrate and process information in a manner that is adaptive , dynamic , flexible , and cost efficient . Taken together , our work establishes a theoretical framework to study the efficiency of a broad range of communication processes on complex networks . While we have focused on a particular class of biased random walks where biases depend on the topological distance to target nodes , we note that biases may also depend on other aspects of the global topology or the embedding of a network in physical space [14 , 29] . Overall , this framework can be used to study any real world network that employs communication or navigation processes in its operation . It may be used , for instance , to infer pathways through which information is preferentially transferred , or , when such pathways are known , to infer the search and navigation strategies that allow accessing these pathways . In the context of brain networks , this theoretical framework may prove useful to identify efficient communication strategies that balance different aspects of the cost associated with neural communication .
Informed written consent in accordance with the Institutional guidelines ( protocol approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine , University of Lausanne , Switzerland ) was obtained for all subjects included in the LAU dataset . The HCP imaging data in this study are from the data sample labeled 100 Unrelated Subjects in ConnectomeDB ( https://db . humanconnectome . org ) , the database managed by the Washington University-University of Minnesota ( WU-Minn ) consortium of the Human Connectome Project ( HCP; http://www . humanconnectome . org ) . Participants were recruited by the WU-Minn HCP consortium and provided written informed consent prior to experiments [35] . All experimental procedures were approved by the Institutional Review Board ( IRB ) at Washington University ( IRB number 201204036; “Mapping the Human Connectome: Structure , Function , and Heritability” ) and no further IRB approval is required for our data analysis . The edge weights of human brain structural connectivity networks are normally defined in terms of proximity measures such as the number of streamlines or fiber densities . These proximity edge-weights are often interpreted as a measure of information flow or traffic capacity that can travel through a connection ( a notion that is analogous to the concept of bandwidth in telecommunication networks ) . Hence , the proximity between two brain regions is determined by the sequence of edges that maximize the traffic or flow capacity . In order to define topological distances on human brain structural connectivity networks , a proximity-to-distance mapping must be applied over the set of edge-weights , such that large edge-weights ( large edge-proximities ) are mapped onto small edge-distances , and small edge-weights are mapped onto large edge-distances . The proximity-to-distance mapping can be defined in various ways . Following previous work [6 , 47] , in this study we use the mapping dij = log ( 1/wij ) , where wij are edge-proximities ( i . e . fiber densities ) and dij are the resulting edge-distances . This mapping has been shown to be less biased towards using only a small set of strong connections for shortest paths [6] , and moreover , it yields edge-distances with a log-normal distribution , which is consistent with evidence showing log-normal distributions of synaptic strengths between cortical cells [65] and cortico-cortical projections [66] . Finally , in order to implement this mapping , we first normalize all edge-weights , to ensure that wij are bounded in the interval [0 , 1] . As shown previously [32] , there is a unique linear function that can normalize any weighted graph onto the unit interval without affecting network properties: wij¯= ( 1−2ϵ ) wij+ ( 2ϵ−1 ) ∙MIN ( wij ) MAX ( wij ) −MIN ( wij ) +ϵ ( 2 ) Here we use ϵ = MIN ( wij ) , in order to obtain normalized edge-weights in the interval ( 0 , 1 ) which allows us to apply the proximity-to-distance map dij = log ( 1/wij¯ ) . Let M = {S , Pλ } be a Markov chain composed by a set of N states S = {1 , 2 , … , N} that correspond element by element to the set of nodes of a graph G with N nodes and E edges; Pλ is the matrix of transition probabilities characterizing the probability of transitioning from one state to another . Then , Pλ ( i , j ) ≠ 0 if and only if an edge exists between nodes i and j in graph G . Let X be a random variable indicating the current state of the chain , or equivalently , the current node where the walker is located; Y is the random variable indicating the node to which the walker will move in the next time step , and T is the random variable indicating the target node where the walk will terminate ( we assume that M is an irreducible chain ) . For a given value of λ , and an specified target T = t , let Pλ be the NxN matrix of transition probabilities where elements of Pλ are defined as Pλ ( Y=j|X=i , T=t ) =exp ( − ( λ ( dij+gjt ) +dij ) ) 1Zit ( 3 ) where Zit=∑jexp ( − ( λ ( dij+gjt ) +dij ) ) is a normalization factor , dij is the distance from i to j and gjt is the geodesic distance from j to the target node t . We make M an absorbing chain and t an absorbing state by setting all transition probabilities Pλ ( Y = j|X = t , T = t ) = 0 for j ≠ t and Pλ ( Y = j|X = t , T = t ) = 1 for j = t , and define Qtλ as the ( N-1 ) x ( N-1 ) matrix of transition probabilities from non-absorbing to non-absorbing states . Then , ntλ = ( I- Qtλ ) -1 is the fundamental matrix for the absorbing chain [67] , and the elements ntλ ( i , j ) denote the amount of time that the chain spends in the j-th non-absorbing state when the chain is initialized in the i-th non-absorbing state . In other words , if we take Pλ to represent the transition probabilities for a ( biased ) random walker on graph G , and going from a source node i to a target node t , then ntλ ( i , j ) represents the number of times that the random walker starting at node i visits node j before it reaches node t . Let π1 and π2 be any two paths going from node i to node t through edges {i , j} , and {i , k} , respectively . The ratio between the transition probabilities Ptij and Ptik is: PijtPikt=exp ( −λ ( dij+gjt ) ) exp ( −dij ) exp ( −λ ( dik+gkt ) ) exp ( −dik ) ( 4 ) Assume that the length of π1 and π2 is equal , so dij+gjt = dik+gkt . Then we can write: PijtPikt=exp ( −dij ) exp ( −dik ) ( 5 ) Now , let S indicate the set of edges leaving from node i along which there is a shortest path from node i to node t . Since all edges in S lie on shortest paths , for any pair of edges {i , j} , {i , k}∈S , it must be that dij+gjt = dik+gkt . Then , when λ→∞ , we can write Pijt={exp ( −dij ) ∑{i , j′}∈Sexp ( −dij′ ) if{i , j}∈S0otherwise ( 6 ) If the network is unweighted , then all dij = const . In that case , all edges in S will have a uniform transition probability from node i . Note that in the λ→∞ case , only transitions along shortest paths will be allowed . This means that the random walk path lengths will be equal to shortest path lengths . For each subject , we created a population of 500 randomized brain networks , with preserved degree and strength sequence , and preserved weight distribution , following the procedure described in [68] , which is a modified version of the randomizations proposed in [69 , 70] . Specifically , the empirical networks were first binarized and then randomized by swapping pairs of connections as proposed by Maslov and Sneppen in [71] , thus preserving the binary degree of each node . In order to approximate the strength sequence of the empirical structural connectivity matrices , we shuffle the empirical weights and randomly assign them to the edges of the randomized network . Then , we used a simulated annealing algorithm that minimizes the cost function C = ∑i|si—ri| , where si is the strength of node i in the empirical network and ri is the strength in the randomized network . The cost function is minimized by randomly permuting weight assignments across edges and probabilistically accepting the permutations that reduced the energy as the temperature parameter of the algorithm is decreased . The annealing schedule consisted of 123 iterations and a starting temperature of t0 = 100 , which was scaled by 0 . 125 after each iteration . The result of this procedure was an average final energy of C = 0 . 2797±0 . 04 , which indicates that the average strength discrepancy per node was between 0 . 0011–0 . 0014 . We mapped the Desikan Killiany anatomical parcels used to construct individual subject structural connectivity networks , onto the seven intrinsic connectivity networks ( ICN ) defined by Yeo et al . ( 2011 ) [72] . This parcellation was derived by using a clustering algorithm to partition the cerebral cortex of 1000 healthy subjects into networks of functionally coupled regions . The clustering procedure resulted in the definition of seven clusters comprising systems previously described in the literature including the visual ( VIS ) and somatomotor ( SM ) regions , dorsal ( DA ) and ventral ( VA ) attention networks , frontoparietal control ( FP ) , limbic ( LIM ) and default mode network ( DMN ) . The mapping between the Desikan-Killiany anatomical parcels and the seven ICNs from the ICN parcellation was obtained by extracting the vertices of the brain surface corresponding to each anatomical region in the Desikan-Killiany atlas , and then evaluating the mode of the vertices’ assignment in the ICN parcellation . | Brain network communication is typically approached from the perspective of the length of inferred paths and the cost of building and maintaining network connections . However , these analyses often disregard the dynamical processes taking place on the network and the additional costs that these processes incur . Here , we introduce a framework to study communication-cost trade-offs on a broad range of communication processes modeled as biased random walks . We control the system’s dynamics that dictates the flow of messages traversing a network by biasing node’s routing strategies with different degrees of “knowledge” about the topology of the network . On the human connectome , this framework uncovers a spectrum of dynamic communication processes , some of which can achieve efficient routing strategies at low informational cost . | [
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"methods",... | 2019 | A spectrum of routing strategies for brain networks |
The ATP-gated P2X4 receptor is a cation channel , which is important in various pathophysiological events . The architecture of the P2X4 receptor in the activated state and how to change its structure in response to ATP binding are not fully understood . Here , we analyze the architecture and ATP-induced structural changes in P2X4 receptors using fast-scanning atomic force microscopy ( AFM ) . AFM images of the membrane-dissociated and membrane-inserted forms of P2X4 receptors and a functional analysis revealed that P2X4 receptors have an upward orientation on mica but lean to one side . Time-lapse imaging of the ATP-induced structural changes in P2X4 receptors revealed two different forms of activated structures under 0 Ca2+ conditions , namely a trimer structure and a pore dilation-like tripartite structure . A dye uptake measurement demonstrated that ATP-activated P2X4 receptors display pore dilation in the absence of Ca2+ . With Ca2+ , the P2X4 receptors exhibited only a disengaged trimer and no dye uptake was observed . Thus our data provide a new insight into ATP-induced structural changes in P2X4 receptors that correlate with pore dynamics .
P2X receptors ( P2XRs ) are cell-surface ATP-gated cation channels , and seven subtypes ( P2X1–7 ) are known [1] . One functional P2XR channel is composed of three subunits . Each P2XR subunit is predicted to have a large extracellular domain ( ECD ) , two transmembrane-spanning domains ( TMD ) , and N and C termini intracellular domains ( ICD ) [1] . It has been suggested that the second half of the ECD ( residues 170–330 ) has sequence and secondary structure similarities to the catalytic site of class II aminoacyl-tRNA synthetase [2] . A six-stranded antiparallel β-pleated sheet structure is believed to exist in the ECD of P2XRs . 3-D homology modeling in P2X4Rs suggests that this region coordinates ATP binding and the allosteric coupling of the conformational changes in the ATP binding domain with corresponding changes at the transmembrane channel gate through a linker region ( the α-helix between the β6 strand and TM2 region ) [3] . In addition to the allosteric coupling of the ATP-binding sites at ECDs and the channel gate at TMD , P2XRs have different permeability states that were originally discovered by Cockcroft and Gomperts [4] . With P2X4Rs , extracellular Ca2+ levels greatly affect the permeability dynamics [5] . In the presence of Ca2+ , P2X4R only opens a small cation-permeable channel pore but in the absence of extracellular Ca2+ it forms a larger pore that allows larger molecules including N-methyl-D-glucamine ( NMDG ) + , propidium iodide , and ethidium bromide ( EtBr ) to pass . Although there is a functional relationship between ECD and TMD , the ATP-induced structural changes in ECD are poorly understood . Recent extensive studies by Khakh's group have clearly demonstrated the allosteric coupling of ICDs and the ion channel permeability of P2XRs [6 , 7] . These results strongly support the hypothesis of the allosteric coupling of channel pores in TMD and other domains including ECDs . In recent structural studies of P2XRs two approaches have been used: electron microscopy ( EM ) and atomic force microscopy ( AFM ) . In EM , single particle averaging analysis and the Ni-NTA gold labeling of human P2X4Rs have clearly demonstrated the distance between the C-terminal tails , the molecular volume , and the 3-D structure [8] . In AFM research , an antibody tagging study has revealed the trimer structure of P2XRs [9 , 10] . AFM has the important advantage of allowing proteins to be observed under liquid conditions , and this makes it possible to activate P2XRs by ATP during AFM studies . In an AFM study combined with ATP treatment , P2XRs exhibited a pore-like structure [11] . In addition to drug treatment , AFM can be used for imaging both lipid bilayers [12] and proteins inserted in lipid membranes [13] . Extensive AFM studies by Engel and Müller's groups have obtained high-resolution topographs of many proteins including aquaporin [14] , connexin [15] , F-ATP synthase [16] , and tubulin [17] . A recent study by Cisneros clearly demonstrated the topography of orientation regulated and covalently assembled homotrimer OpmF proteins [18] . In their report , the authors employed the single particle correlation averaging method to obtain 3-fold symmetrized images of OmpF trimer that are identical to the topographs of 2-D crystals of OmpF . Because many P2XR channels are also homotrimers , this approach can be used for the high-resolution imaging of P2XRs . Although the use of AFM provides significant advantages the imaging speed is usually very slow ( several tens of seconds ) . Many ion channel reactions occur in less than a second , so fast scanning is essential for observing the P2XR reaction with AFM . To address this issue , we employed a recently developed fast-scanning AFM [19] that allows us to observe biological molecules including nucleic acids [20] , lipids [12] , and proteins [21 , 22] at high temporal resolution . Fast-scanning AFM in combination with single particle averaging is considered a powerful tool for analyzing single P2X4R channels with high spatial and temporal resolution .
The expression of rat P2X4R protein in human 1321N1 astrocytoma cells was estimated by western blotting . P2X4R was detected only in P2X4R gene-overexpressed cells ( Figure 1A ) . In silver-stained native PAGE , only one band corresponding to a trimer ( about 150 kDa ) ( Figure 1B ) was observed . The same protein analyzed by SDS-PAGE and silver-staining exhibited a band corresponding to a monomer ( about 50 kDa ) . For the AFM analysis of P2X4Rs , we used freshly cleaved mica as a substrate because it has an atomically flat surface and is usually used for protein observation with AFM . All the AFM images were presented as gray-scale height images . In many cases , the P2X4R particles were only loosely attached to the uncoated mica and so they moved during the AFM observation . To obtain a stronger attachment for electrostatic interactions , we coated the mica with positively charged poly-D-lysine ( PDL ) ( 1 mg/ml , 30 min at room temperature [RT] ) and set the pH of the imaging buffer ( AFM imaging buffer A ) at 8 . 0 because the isoelectric point of P2X4R is pH 7 . 41 . All the P2X4Rs on the PDL-coated mica were observed in AFM imaging buffer A . Under this condition , the P2X4Rs were attached stably to the substrate ( Figure 2A ) . The P2X4R control particles were relatively homogenous and nearly all circular , ellipsoid , or triangular with obtuse angles ( Figure 2B , upper panels ) . PDL-polymers were also observed ( Figure 2B[ii] , arrows ) . In this study , we defined the dimensions of the P2X4Rs as their diameter and height on the basis of our criteria ( see also Materials and Methods and Figure S1 ) . The nonstimulated P2X4Rs had a diameter of 12 . 6 ± 0 . 2 nm ( mean ± standard error of the mean [SEM] ) ( n = 200 ) and a height of 2 . 3 ± 0 . 1 nm . To observe activated P2X4Rs , we added ATP before the AFM observation . ATP did not induce any significant changes at 100 μM ( unpublished data ) , but the P2X4Rs changed greatly at 1 mM ( Figure 2B , lower panels ) . Under this condition , at least several minutes of ATP treatment was required before the P2X4Rs underwent structural changes . After the structural changes caused by 1 mM ATP , most of the P2X4Rs appeared to be trimers ( 84 . 9 ± 5 . 0% , n = 393 ) ( Figure 2C ) . The ATP-treated P2X4Rs had a diameter of 14 . 2 ± 0 . 2 nm ( n = 205 ) and a height of 3 . 0 ± 0 . 1 nm . The diameter of one lobe in a P2X4R trimer was 5 . 9 ± 0 . 2 nm ( n = 40 ) . To obtain clear topographs of P2X4Rs , we averaged single P2X4R images by using the same approach employed by Cisneros et al . [18] and on the basis of our criteria ( Figure S1 ) . The nonsymmetrized averaging of ATP-treated P2X4Rs revealed a tripartite morphology ( Figure 2D[i] , right ) that was enhanced by 3-fold rotational symmetrization ( Figure 2D[ii] , right ) . Nonstimulated P2X4Rs were circular or triangular with obtuse angles after averaging ( Figure 2D , left panels ) . For averaging , we used the particles shown in Figure 2B ( iii ) ( n = 60 ) . Then , we checked whether these trimers were one unit of P2X4R trimers or simply three adjacent particles . If each lobe was an individual P2X4R trimer that was incidentally assembled into a trimer , the distance between lobes would not be significantly different from the distance between trimers . The distance between the lobes in a P2X4R trimer and the distance between two adjacent trimers were 8 . 7 ± 0 . 1 nm ( n = 100 , between lobes ) and 35 . 5 ± 2 . 7 nm ( n = 115 , between trimers ) , respectively ( Figure 2E ) . Sometimes , P2X4R particles on PDL-coated mica shifted position within the same scan area . In this situation , single lobes in a P2X4R trimer ( 15 min after 1 mM ATP treatment ) did not move individually but moved along with the other two lobes ( Figure 2F[i] ) . Enlarged images of single P2X4R trimer in a rectangle at 5 s are shown on the left in Figure 2F ( ii ) . The nonsymmetrized and symmetrized averaging of ten particles in the same scan area at 0 s is shown in the center and on the right , respectively , in Figure 2F ( ii ) . To observe the ATP-induced continuous structural changes in P2X4Rs , we performed imaging using fast-scanning AFM with a scan rate of two frames per second . P2X4Rs were observed in AFM imaging buffer B . Under our conditions , faster scan rates than this degraded the signals and increased noise so that we were unable to obtain sufficient resolution . It is known that a mica surface is negatively charged [23] , and so we used uncoated mica rinsed with a high concentration of KCl ( 1 M , 30 min at RT ) to reduce electrostatic interactions between the mica surface and the ATP or P2X4Rs . Under this condition , many P2X4Rs shifted position during AFM imaging . To obtain a clear topology of P2X4R , ten P2X4R particles were averaged at the same time point . The resulting 3-fold symmetrized images of P2X4Rs clearly exhibited the structures at each time point . Before the uncaging ( −2 . 5 to ∼0 . 0 s ) of caged ATP ( 200 μM ) , P2X4R exhibited a circular structure ( Figure 3 , see also Video S1 ) . At 0 . 5 s after uncaging , the P2X4R structure changed greatly and a clear trimeric structure was observed . After this change , the distances between individual lobes gradually increased ( ≈5 s ) . The conformational change in the nonsymmetrized P2X4R is also shown in Figure S2 . The same reaction was reproduced in three independent experiments . Another result of the ATP-induced structural changes in P2X4R is shown in Figure S3 . Some P2X4Rs were stable at one location during AFM imaging . Several examples of ATP-induced structural changes in a single P2X4R are shown in Figure S4 . At a single particle level , although the P2X4R topologies were relatively blurry , individual subunits became visible after uncaging and appeared to move away from each other . When the ATP was washed off , the pore dilation-like structure returned to a circular structure ( unpublished data ) . To estimate the orientation of observed structures , P2X4Rs were reconstituted in a lipid bilayer . Figure 4A ( i ) is a diagram showing the predicted structure of a P2X4R subunit . A six-stranded anti-parallel β-plated sheet structure is reported to exist in the second half of the ECD in P2X4R subunits [2 , 3] . The entire structure of trimeric P2X4R is predicted on the basis of this homology modeling data , as shown in Figure 4A ( ii ) . In AFM , this β-plated sheet structure should be observed as one large domain . Figure 4B shows our working hypothesis , which is that when P2X4Rs are reconstituted in a lipid bilayer and if they are inserted in an upward orientation , they should respond to ATP thus resulting in structural changes and increased Ca2+ permeability . When P2X4Rs were inserted in the lipid bilayer that formed on mica , the AFM images of P2X4Rs in membranes were similar to the P2X4Rs that were dissociated from the membrane . The P2X4Rs had circular structures in the control and trimeric structures after binding with ATP ( 200 μM , 1 min ) ( Figure 4C and 4D ) . P2X4Rs reconstituted in a lipid bilayer did not require as high a concentration of ATP as those on PDL-coated mica . Under this condition , the structures of most of the P2X4Rs ( 83 . 3 ± 5 . 4% , n = 70 ) changed into a tripartite form . The P2X4Rs in the membranes were 11 . 4 ± 0 . 3 nm in diameter and 5 . 8 ± 0 . 1 nm high ( including the height of the membrane ) in the control ( n = 50 ) and 13 . 3 ± 0 . 3 nm in diameter and 6 . 1 ± 0 . 1 nm high after ATP addition ( n = 100 ) . The calculated height of the membrane was 4 nm . The AFM imaging of membrane-inserted P2X4Rs was performed in imaging buffer B . For calcium imaging , the P2X4Rs were reconstituted in a lipid bilayer that was suspended over a 500 μm hole . The green fluorescence intensity of fluo-3 ( 50 μM , in hole ) was significantly increased after ATP ( 100 μM ) addition ( Figure 4E ) . This green florescence was only detected in the hole ( Fig . 4E[i] ) . The intensity of the green fluorescence increased rapidly for a few seconds after ATP addition and then increased gradually ( Fig . 4E[ii] , see also Video S2 ) . The averaged trace was obtained from five individual experiments . An EtBr-dye uptake measurement was performed at the same time as the Ca2+ imaging . Here , no increase was observed in red fluorescence after ATP addition ( Fig . 4E[iii] ) . The Ca2+ imaging was performed in Ca2+ imaging buffer . In the time-lapse imaging of ATP-induced structural changes in P2X4R , we observed a characteristic pore dilation-like structure ( Figure 3 , ≈5 . 0 s ) . This pore dilation-like structure was also observed in membrane-reconstituted P2X4Rs ( Figure 4D ) . Before the appearance of this structure , the P2X4Rs on the uncoated mica exhibited nondilated trimer structures ( Figure 5A , center ) . We observed two P2X4R structures similar to these two different forms on PDL-coated mica ( Figure 5B ) . 15 min after ATP ( 1 mM ) addition , the P2X4Rs exhibited a nondilated trimer structure ( Figure 5B , center ) but they exhibited a pore dilation-like structure 30 min after ATP addition ( Figure 5B , right ) . Then we estimated the dye uptake function of P2X4Rs using the same Ca2+ imaging system . EtBr-uptake imaging buffer containing no Ca2+ was used for this study . Here , ATP ( 100 μM ) addition increased the red fluorescence intensity in the hole ( Figure 5C , upper panels , see also Video S3 ) . Under our conditions , the red fluorescence intensity started increasing within seconds of the ATP addition and then increased gradually ( ≈300 s ) ( Figure 5C , lower panel ) . When we measured dye uptake with 2 mM Ca2+ in an external solution , we observed no increase in red fluorescence intensity ( Figure 4E[iii] ) . To confirm whether the effect of Ca2+ on dye uptake is related to the pore dilation-like structural changes , we compared the structures of P2X4Rs in the presence and absence of Ca2+ . In this study , we used the same mica as we used for the time-lapse imaging , and we used AFM imaging buffer B or C for each condition . With 0 Ca2+ , an averaged P2X4R image was obtained from 18 particles ( Figure S5A[i] ) . The particles were selected from frames at least 5 s after uncaging . In this case , a pore dilation-like image was again obtained ( Figure 5D[i] ) . In the presence of 2 mM Ca2+ , no pore dilation-like averaged image was obtained but a nondilated trimer was observed ( Figure 5D[ii] ) . An averaged P2X4R image was obtained from 18 particles ( Figure S5A[ii] ) at least 5 s after uncaging . The averaged images are obtained after 3-fold symmetrized averaging . Under both conditions , the majority of the P2X4Rs responded to ATP ( 0 Ca2+: 67 . 0 ± 2 . 8 % , n = 257; 2 mM Ca2+: 62 . 8 ± 2 . 7 % , n = 324 ) . Nonsymmetrized averaging images of P2X4R under each condition are shown in Figure S5B . Models of the ATP-induced structural changes of P2X4R based on our results are shown in Figure 6 . In the control , three ECDs of P2X4Rs were close to each other and AFM revealed no individual subunits . Under this condition , neither Ca2+ nor EtBr can permeate the TMD pore . In the absence of Ca2+ , the ECDs are disengaged and a tripartite topology was observed immediately after ATP binding ( Figure 6A , center ) . Prolonged ATP treatment induces further disengagement of the three ECDs ( Figure 6A , right ) . These two structures appear to correspond to the Ca2+ permeable and EtBr permeable states ( Figure 6A , below ) . Under a 2-mM Ca2+ condition , P2X4R has a nondilated trimer structure regardless of the ATP exposure time ( Figure 6B ) . In this situation , the TMD pores allow Ca2+ to permeate but not EtBr however it is unclear whether or not P2X4R is desensitized during ATP exposure .
Our main findings in this study are that ( i ) it is possible to achieve time-lapse imaging of the dynamic structural changes of P2X4Rs evoked by ATP; ( ii ) the three subunits are close to each other and it is impossible to observe individual subunits in the control but they disengage and move away from each other after ATP binding; and ( iii ) the two types of structural changes observed in AFM appear to correspond to two functional states , namely the Ca2+ permeable state and the dye permeable state . Recent structural studies with direct imaging methods including EM and AFM or with other methods including fluorescence resonance energy transfer ( FRET ) -based analysis have provided strong motivation for structural studies of P2XRs . These reports clearly demonstrated trimeric stoichiometry using antibody-tagging [9 , 10] or Ni-NTA gold labeling on the His-tag of the C termini in P2XRs [8] , and the shape , architecture , and size of P2X4Rs in a nonstimulated state and the distance between the C termini of P2XRs [8] . We needed to determine the way in which P2XRs change their entire structure in response to ATP binding . To address this issue we analyzed homotrimeric P2X4Rs . To this end , we overexpressed P2X4R gene in human 1321N1 astrocytoma cells . Because this cell does not express P2XRs [24] , purified P2X4Rs from the membrane fraction of this cell are considered to form homotrimers . In fact , the purified P2X4R presented as a single band corresponding to a trimer ( about 150 kDa ) in native-PAGE but as a monomer ( about 50 kDa ) in SDS-PAGE , and purified P2X4R was functional as estimated in terms of Ca2+ permeability . We then observed P2X4Rs on mica but they did not attach to it stably . P2X4Rs on PDL-coated mica exhibited stable attachment but a high concentration of ATP was required to induce structural changes . The ATP has negative charges that may induce the strong attraction of ATP to the positively charged PDL . In fact , P2X4Rs reconstituted in a lipid bilayer or on mica without PDL coating responded to lower ATP concentrations . In addition to the high ATP concentration , a long period of ATP exposure was required when P2X4Rs were adsorbed on PDL-coated mica . This may be due to the strong attachment of P2X4Rs to mica . In a recent report , the N-terminal tagging of fluorescent proteins on P2X2Rs dramatically increased the ATP EC50 value , but this did not occur with small tetracystein ( 4C ) tags labeled with fluorescein arsenical hairpin [7] , implying that the limited spatial flexibility in the N-terminal domain of P2XRs may reduce the response to ATP . Koshimizu et al . have reported that the cytoplasmic intersubunit interaction prior to ATP binding in P2X2R contributes to the subsequent channel activity and conformational changes [25] . The strong attachment of P2X4R to mica may also affect the intersubunit interaction via the ICDs , which perhaps causes the reduced response of P2X4R to ATP . Under our conditions , the strong attachment of P2X4R may change the structural flexibility and/or the intersubunit interaction that reduces the responsivity to ATP . The reduced attachment of P2X4Rs to mica without PDL dramatically increased the velocity of the ATP response , and thus supported our hypothesis . Despite the low ATP reactivity of P2X4Rs on PDL , we observed clear structural differences between the control and the ATP-treated condition . We believe that the three lobes observed after ATP addition were three individual subunits of one P2X4R trimer . First , the distance between the lobes was significantly smaller than that between trimers . If each lobe was an individual P2X4R trimer that was incidentally assembled into a trimer , the distance between lobes would not be significantly different from the distance between trimers . Second , during the AFM observation , some P2X4R trimers occasionally shifted position , and these trimers moved as trimers ( i . e . , the three lobes did not dissociate ) . Third , in time-lapse analysis , the circular structure changed into a trimer after ATP treatment both in the averaged particle images and in single particles . This result also suggests that the trimeric stoichiometry exists even in P2X4Rs before ATP binding . From this observation , we considered circular particles without individual subunits before ATP binding to be trimeric P2X4Rs because those subunits were closer together than the spatial resolution of our AFM system . If this is the case , the diameter of the P2X4R in the control should be double that of one lobe . In fact , the diameter of the P2X4R in the control ( about 12 . 6 nm ) was approximately double that of one lobe ( 5 . 9 × 2 nm ) . These three lobes were also observed when P2X4Rs were inserted into a lipid bilayer , suggesting that these lobes are the predicted six-stranded antiparallel β-pleated sheet structures in the ECDs of P2X4Rs . EM analysis of P2X4Rs revealed propeller-like domains in the ECDs [8] that were similar to the six-stranded antiparallel β-pleated sheet-like structure that we observed in the ECDs . In their report , the authors clearly demonstrated that the EM-based distance between the C termini of the P2X4Rs was 6 . 1 nm and the FRET-based distance between the C termini was 5 . 6 nm . The three propeller-like domains at the opposite end of the P2X4R to the gold-labeled C termini means the distances between these domains would be similar . As described above , when three lobes are assembled close together in the control , the distance between the centers of two lobes is twice the lobe radius ( 2 . 95 × 2 nm ) , which agrees well with the distance between P2X4R C termini estimated by FRET and EM [8] . As mentioned above , the AFM images of P2X4Rs in a lipid bilayer and on mica were comparable; this result strongly suggests the upward direction of the P2X4Rs on mica . However , the height of P2X4R on mica was less than the height of a lipid bilayer composed of phospholipids ( about 4 nm ) [12] . In nonsymmetrized averaging , one of the three lobes in the P2X4R trimer on mica was lower than the other two . The height of the P2X4Rs on mica was only slightly greater than that from the surface of a lipid bilayer to the top of the inserted P2X4Rs , indicating the possibility that the P2X4Rs do not stand vertically and TMD and/or ICD are bent during the AFM observation . From these observations , we concluded that P2X4Rs lean to one side on mica and TMD or ICD may be bent and concealed behind the ECDs . Similarly , the simple adsorption of P2X2Rs [11] on mica also results in these molecules having a top view-like structure in AFM images , thus supporting our conclusions . In the time-lapse imaging , we observed two different structural changes: ( i ) from one circular structure to a trimeric structure ( 0 . 0 → 0 . 5 s after uncaging ) and ( ii ) the subsequent moving away of each lobe ( 0 . 5 → 5 . 0 s ) . The second structural change reminds us of an important function of the P2X family , namely pore dilation . In an early study , Khakh et al . clearly demonstrated that P2X4R exhibits NMDG+ permeable pore dilation in the absence of extracellular Ca2+ [5] . In their work , the P2X4Rs exhibited sustained activity for several minutes , indicating that our pore dilation-like structure is not a desensitized P2X4R state . Our work represents direct evidence of the functional and structural relationship of pore dilation in P2X4R . Under a 0 Ca2+ condition , we observed both pore dilation-like structural changes in ECDs and EtBr uptake . This pore dilation-like change was reproducible under various conditions including on mica , on PDL-coated mica and in a lipid bilayer , strongly suggesting that this structural change is a fundamental reaction of P2X4R . At 2 mM Ca2+ , we observed no EtBr uptake but there was a Ca2+ flow via P2X4R that also corresponded to the previous report [5] . Under this condition , the pore dilation-like structure of P2X4R was not observed but P2X4R trimers similar to the structure seen immediately after ATP binding were evident . The averaged trace of the green fluorescence intensity exhibited a near-plateau state after an initial increase . This result may indicate that the number of desensitized P2X4Rs increase during a long ATP exposure . From these observations , we considered that the structural changes in the ECDs of P2X4Rs are related to permeability dynamics . Recent reports on P2X7Rs suggested the possibility that their EtBr uptake is mediated by accessory Pannexin-1 ( Panx1 ) channels [26] . In their report , the authors demonstrated that human 1321N1 cells express Panx1 , so it is possible that there is functional coupling between overexpressed P2X4Rs and Panx1 in this cell . We concluded that EtBr can pass through P2X4R independent of Panx1 ( at least in our study ) for the following reasons . First , we used purified P2X4Rs and only a single band was observed in the native-PAGE/silver staining . As Panx1 ( about 50 kDa ) forms a hexameric channel [27] , Panx1 contamination would be detected as another band ( about 300 kDa ) . Second , Panx1 and connexins are known to have structural similarities [28] and connexins are observed as hexameric structures [15] in AFM . We observed no hexameric structures in our AFM study . Third , the issue of Panx1 coupling with P2X7R remains to be clearly settled because another group has demonstrated that P2X7R exhibits pore dilation independent of Panx1 [29] . P2X2R also exhibits the pore dilation independent of Panx1 [7] . These results indicate that Panx1 may not be a fundamental component of the pore dilation state of the P2XR family . Fourth , in contrast to connexin hemichannels , Panx1 is active at physiological extracellular Ca2+ concentrations [28] . In our simultaneous Ca2+/dye uptake measurement , EtBr uptake was not observed at 2 mM Ca2+ . However , our data and these reports do not rule out the possibility of functional coupling between P2X4Rs and Panx1 in cells . Thus , our present study provides direct evidence of structural changes in the ECDs of P2X4Rs that are involved in permeability dynamics . We have achieved the first direct , time-lapse imaging , to our knowledge , of ATP-induced structural changes of P2X4R using a new technique , namely fast-scanning AFM . Our approach provides new insights into the structure of P2XRs , and an extension of this approach to other P2X subtypes will help us to understand the structural and functional relationships of the P2XR family .
Reagents were obtained from the following sources . DMEM , EDTA , and FBS were purchased from Gibco . Aprotinin , bestatin hydrochloride , bromophenol blue , geneticin , glycine , leupeptin , NaCl , EGTA , penicillin , pepstatin A , PDL , protein A sepharose , SDS , streptomycin , sucrose , Tris-HCl , Triton X-100 , HEPES , 3-[ ( 3-Cholamidopropyl ) dimethylammonio]-1-propanesulfonate , 4–2 ( aminoethyl ) benzenesulfonyl fluoride hydrochloride ( AEBSF ) , and L-α-phosphatidylcholine ( PC ) were obtained from Sigma-Aldrich . Geneticin was supplied by Invitrogen . The silver staining kit and MeOH were purchased from Wako Pure Chemicals . E-64 protease inhibitor was obtained from Calbiochem . Anti-P2X4 receptor antibody was supplied by Alomone Labs . Brain-derived phosphatidylserine ( PS ) was obtained from Avanti . Native mark ( Invitrogen ) microdialysis rods were purchased from Hampton Research . The spectrapor dialysis membrane was obtained from Spectrum Lab . n-octyl-β-D-glucopyranoside ( βOG ) was obtained from DOJINDO . Human astrocytoma 1321N1 cells were maintained in DMEM , containing 5% ( v/v ) FBS , 100 μg/ml penicillin , and 100 μg/ml streptomycin ( Sigma ) . For 1321N1 cells expressing P2X4R , 400 μg/ml G-418 ( geneticin ) was added . cDNA encoding rat P2X4R was subcloned into the pcDNA3 . 1 vector . Transfection was carried out with Superfect ( QIAGEN ) according to the manufacturer's protocol . 1321N1 cells successfully expressing P2X4R were confirmed by the ATP-induced increase in [Ca2+]i , and were isolated and proliferated . P2X4R-expressing 1321N1 cells were cultured to confluence and then harvested by scraping . The cells were homogenized with a Teflon homogenizer in HEPES buffer containing 20 mM HEPES , ( pH 7 . 4 ) , 320 mM sucrose , 5 mM EDTA , 5 mM EGTA , and protease inhibitors ( 100 μM AEBSF , 80 nM aprotinin , 5 μM bestatin , 1 . 5 μM E-64 protease inhibitor , 2 μM leupeptin , and 1 μM pepstatin ) . Supernatants obtained by centrifuging the homogenate at 3 , 000g for 15 min at 4 °C were further spun at 38 , 400g for 15 min to obtain membrane pellets . The pellets were resuspended in buffer containing 20 mM HEPES , ( pH 7 . 4 ) , 1% CHAPS , 100 mM NaCl , 5 mM EDTA , 5 mM EGTA , and protease inhibitors . The sample was treated with anti-P2X4R antibody ( 10 μg ) and incubated for 24 h at 4 °C with gentle agitation . Then protein A sepharose ( 1 mg ) was added to the sample and incubated for 1 h at 4 °C . The sample was then centrifuged at 3 , 000 g for 5 min and the pellets were washed with buffer ( 20 mM HEPES , [pH 7 . 4] , 100 mM NaCl , 5 mM EDTA , 5 mM EGTA , and protease inhibitors ) three times and treated with 50 μl 0 . 1 M glycine-HCl ( pH 2 . 7 ) to dissociate the P2X4R protein from the antibody . The supernatant was transferred to a new tube and added to 1/10 volume of 1M Tris-HCl ( pH 8 . 5 ) . Purified protein was resolved in a native sample buffer ( 62 . 5 mM Tris-HCl , [pH 6 . 8] , 15% glycerol , 1% deoxycholate , and 0 . 01% bromophenol blue ) and was loaded onto 4%–13% acrylamide gradient gel . Native Mark was used as a marker for detecting the molecular weight of purified P2X4R . After native-PAGE , silver staining was undertaken following the manufacturer's protocol ( Silver stain kit II , Wako ) . After electrophoresis , the gel was transferred into a container and fixed with a first fixation buffer ( 10% MeOH , 10% acetic acid and 40% H2O ) for 10 min followed by a 10-min second fixation in a second fixation buffer ( 10% fixation buffer A and 90% H2O ) . Then the gel was incubated in an intensification buffer ( 5% intensification buffer , 47 . 5% MeOH , and 47 . 5% H2O ) for 10 min and washed with H2O for 5 min . The gel was stained in a stain buffer ( 5% stain solution A , 5% stain solution B , and 90% H2O ) for 15 min . After washing with H2O ( 3 min × three times ) , the gel was incubated in a developing buffer ( 5% developing solution and 95% H2O ) until the protein bands became visible . Cells and purified P2X4R protein were lysed with lysis buffer ( containing 10 mM Tris , [pH 7 . 5] , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1 % Triton X-100 , 0 . 1% SDS , 1 mM sodium orthovanadate , 1% doxycholate , and 10 μg/ml each of aprotinin , bestatin , pepstatin A , leupeptin ) . For SDS-PAGE , the lysates were mixed with an equal amount of Laemli sample buffer ( 62 . 5 mM Tris/HCl , 20% glycerol , 2 . 5% SDS , 0 . 01% bromophenol blue , and 10% 2-merchapt EtOH ) and then boiled at 95 °C for 5 min . Proteins were separated in 4%–13% acrylamide gradient gel and then visualized by silver staining . For western blotting analysis , electrophoresed proteins were transferred to the PVDF membrane and P2X4R protein was detected with anti-P2X4R antibody . The AFM experiments were performed using an NVB500 high-speed AFM ( Olympus Corporation ) . BL-AC7EGS-A2 cantilevers with a spring constant of 0 . 1 N/m ( Olympus Corporation ) were used in the tapping mode with an oscillation frequency of 800–1 , 000 kHz . PDL ( 0 . 1 mg/ml in H2O ) was treated on mica for 30 min at RT . The sample was washed with imaging buffer A ( 25 mM Tris-HCl , [pH 8 . 0] , 137 mM NaCl , 2 . 7 mM KCl ) , and then deposited on the mica and incubated for 30 min at RT . For the time-lapse imaging of the P2X4Rs , mica rinsed with a high-salt buffer ( 10 mM Tris-HCl , [pH 8 . 0] , 1M KCl , 30 min at RT ) without PDL coating and imaging buffer B ( 25 mM Tris-HCl , [pH 7 . 4] , 137 mM NaCl , 2 . 7 mM KCl ) were used . To observe P2X4Rs in the presence of Ca2+ , imaging buffer C ( 25 mM Tris-HCl , [pH 7 . 4] , 137 mM NaCl , 2 . 7 mM KCl , 2 mM CaCl2 ) was used . To activate the P2X4Rs , caged ATP ( 200 μM ) was uncaged by UV illumination ( BH2-RFL-T3 , Olympus ) . ATP and caged ATP were dissolved in imaging buffers A and B , respectively . Images containing 192 × 144 pixels were obtained at a scan rate of 0 . 2 or 0 . 5 fps for static images and 2 . 0 fps for time-lapse imaging . All AFM images were processed using Image J software ( http://rsb . info . nih . gov/ij/ ) . The P2X4R diameters were measured by using “segmented line selections . ” The height and diameter were measured by using “Analyze-Plot profile” found on the menu bar . The 3-D images of P2X4R shown in Figure S1 were converted from 2-D AFM images with the Image J plug-in “interactive 3D surface plot” ( http://rsbweb . nih . gov/ij/plugins/surface-plot-3d . html ) . The plug-in programs were downloaded from the Image J software homepage ( http://rsb . info . nih . gov/ij/plugins/index . html ) . The P2X4R images were averaged with EMAN software [30] ( http://blake . bcm . tmc . edu/eman/ ) . Because the majority of the P2X4Rs exhibited a similar direction , we simply selected the P2X4R particles at random for averaging . All the P2X4R images used for EMAN processing were converted to TIFF files . The TIFF images were opened by boxer program and particles for averaging were selected . The selected images were processed with an averaging command in proc2d program . The resulting averaged image was saved in PNG file format . For 3-fold symmetrized averaging the P2X4Rs were rotated through three angles ( 0 , 120 , and 240° ) with illustrator CS software and the file was converted to a TIFF file . The resulting three P2X4Rs were further averaged using EMAN software . We first established criteria for determining the P2X4R particle center . Three types of P2X4Rs were observed in the control , namely those with triangular , circular , and ellipsoidal structures ( Figure S1A ) . The center of the triangular P2X4R was defined as the center of a triangular circumcircle . The center of the circular P2X4R was defined as the center of an approximated circle . The center of the ellipsoidal P2X4R was defined as the intersection of the long and minor axes . The center of the trimeric P2X4R was defined as the center of a circle connecting the highest points of all subunits . Next , we established P2X4R size criteria . In the present study , we defined the P2X4R dimensions as diameter and height . The diameter of the triangular and circular P2X4Rs was defined as the diameter of the circles used for determining the particle center . The diameter of the ellipsoidal P2X4R was defined as the average value of the long and short axes . The diameter of the trimeric P2X4R was defined as the diameter of a circle that circumscribed the three lobes . The particle height in the control was simply defined as the distance between the top of the particle and the mica surface . The height of the trimeric P2X4R was defined as the average height of three lobes . The height of P2X4R in a lipid bilayer was defined as the total distance from the top of the particle to the membrane surface plus the height of the lipid bilayer ( 4 nm ) . Then , we established criteria for particle selection . The P2X4R diameters obtained from 600 particles including activated and nonactivated P2X4Rs exhibited a clear single distribution and the top 5% and bottom 5% of the particles were eliminated from the analysis . The remaining 90% of the particles indicated by the arrows in Figure S1C ( i ) were used for analysis . In addition to this , P2X4Rs that exhibited a large noise ( Figure S1C[ii] ) were also eliminated from the analysis . During AFM observation , the P2X4Rs did not always provide clear images . Although the P2X4Rs had a clear topology in some frames , it was not clear in others . When the ATP stimulated P2X4Rs were selected for averaging , P2X4R particles without subunit-like structures were eliminated from the averaging process . We performed the averaging in accordance with an early study [18] . First , we selected individual P2X4R particles on the basis of our criteria and then averaged them using EMAN software ( nonsymmetrized averaging ) . Under our conditions , most of the P2X4Rs exhibited similar directions , so we did not perform any additional processing before averaging . The resulting images were further rotated ( 0 , 120 , and 240° ) and averaged again ( 3-fold symmetrized averaging ) . When the activated P2X4Rs were averaged , the P2X4Rs without the subunit-like structures observed in the control were eliminated . Lipid mixtures ( 100 μl ) for reconstitution were prepared from L-α-phosphatidylcholine/brain-derived phosphatidylserine ( PC/PS = 1:1 , 160 μM ) with 160 mM n-octyl-β-D-glucopyranoside . Mixed micelles were added to 100 μl of 100 ng/ml P2X4R protein . Detergent was removed by dialysis using microdialysis rods and a Spectrapor dialysis membrane ( molecular cut-off of 50 , 000 ) in a dialysis buffer ( 30 mM HEPES , 5 mM EDTA , 1 mM EGTA , 0 . 02% of NaN3 ) . The P2X4Rs were dialyzed for 5 d and the buffer was changed every day . Purified DNA was prepared from primary cultured rat cortex astrocytes using ISOGEN ( Nippongene ) . Primary rat cortex astrocytes were cultured as described in detail in our previous work [31] . DNA isolation was performed in accordance with the manufacturer's protocol . Confluent cultured astrocytes in a 100-mm cell culture dish were washed three times with PBS and lysed with 1 ml of ISOGEN . After homogenization by pipetting , the cell lysate was transferred to a 1 . 5-ml tube . Then 0 . 2 ml of chloroform was added to the tube and the resulting mixture was incubated for 3 min at RT after vigorous shaking ( 15 s ) . The tube was centrifuged ( 12 , 000g ) for 15 min at 4 °C and the inter/organic phases were transferred to a new tube . Next , ethanol ( 0 . 3 ml ) was added to the tube and incubated for 3 min at RT . The tube was centrifuged ( 2 , 000g ) for 5 min at 4 °C . The supernatant was discarded and 1 . 0 ml of 0 . 1 M sodium citrate ( in 10% ethanol ) was added to the tube . After 30 min incubation at RT , the tube was centrifuged ( 2 , 000g ) for 5 min at 4 °C . The precipitate was mixed in 2 ml of 75% ethanol and incubated for 30 min at RT . The tube was then centrifuged ( 2 , 000g ) for 5 min at 4 °C . The precipitate was dried and dissolved in H2O . Calcium and dye uptake imaging of P2X4Rs was performed using a 500-μm hole cut in a plastic plate consisting of the bottom plate of a 60-mm cell culture dish . A Terumo syringe ( 25 gauge , 500 μm in diameter ) was briefly heated with a gas burner and then pushed through the plastic plate . The resulting plastic burr around the hole was removed with a razor . Then 0 . 2 μl of imaging buffer containing 50 μM fluo-3 and 100 ng/μl DNA was placed in the hole . The top and bottom of the hole were covered by 1 μl of n-decane containing 2 mM PC/PS ( 1:1 ) and incubated for 5 min at RT . P2X4R-containing proteoliposome ( 0 . 5 μl ) was supplied to the top surface of the hole and incubated for 10 min at RT . The bottom surface of the hole was covered with 2 μl of 10 mM Tris-HCl buffer ( pH 7 . 4 ) . Proteoliposome-containing buffer was carefully washed with 2 μl of 10 mM Tris-HCl buffer ( pH 7 . 4 ) and then with 10 mM Tris-HCl buffer containing 20 μM EtBr with/without 2 mM CaCl2 ( calcium imaging buffer or EtBr uptake imaging buffer ) . To stimulate the P2X4Rs , 1 μl of ATP ( 300 μM , the final concentration of ATP in the buffer was 100 μM ) was added to the top of the hole . Calcium and dye uptake imaging was performed using a Zeiss LSM510 and ZEN2007 imaging system under a 5× objective . Throughout the functional analysis , fluo-3 was excited with the 488-nm line of an argon ion laser and the emitted light was collected using a 500–530-nm band-pass filter . EtBr was excited at 488 nm and the emission fluorescence was collected using 560–615-nm band-pass filters [32] . Average results are expressed as the mean ± SEM . Data were analyzed with the Student's t-test to determine the differences between groups . Significance was accepted when p < 0 . 05 . | ATP is not only a source of intracellular energy but can act as an intercellular signal by binding membrane receptors . Purinergic receptors , which bind with nucleotides including ATP are known as P2 receptors and are divided into two types: ion channel-type P2X receptors and metabotropic-type P2Y receptors . P2X receptors are thought to undergo conformational changes in response to ATP binding , leading to the opening of transmembrane channels , through which cations enter the cells . A growing body of evidence shows that P2X receptors control various physiological and pathophysiological cellular responses . However , the receptor structure and the conformational changes it experiences upon stimulation remained to be clarified . Here , we employed an atomic force microscope ( AFM ) to observe P2X receptor behavior at the single channel level . We chose to analyze the P2X4 receptor , because it is known to increase the transmembrane pore size ( i . e . , pore dilation ) in the absence of extracellular calcium . Activated P2X4 receptor exhibited a trimeric topology with a pore-like structure in the center . When calcium was present the receptor exhibited a trimer without a pore structure at its center . These structural changes corresponded well with the changes of ion permeability of P2X4 receptor . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"physiology",
"pharmacology",
"neuroscience"
] | 2009 | Direct Observation of ATP-Induced Conformational Changes in Single P2X4 Receptors |
Typhoid fever , caused by the intracellular pathogen Salmonella ( S . ) enterica serovar Typhi , remains a major cause of morbidity and mortality worldwide . Granzymes are serine proteases promoting cytotoxic lymphocytes mediated eradication of intracellular pathogens via the induction of cell death and which can also play a role in inflammation . We aimed to characterize the expression of extracellular and intracellular granzymes in patients with typhoid fever and whether the extracellular levels of granzyme correlated with IFN-γ release . We analyzed soluble protein levels of extracellular granzyme A and B in healthy volunteers and patients with confirmed S . Typhi infection on admission and day of discharge , and investigated whether this correlated with interferon ( IFN ) -γ release , a cytokine significantly expressed in typhoid fever . The intracellular expression of granzyme A , B and K in subsets of lymphocytic cells was determined using flow cytometry . Patients demonstrated a marked increase of extracellular granzyme A and B in acute phase plasma and a correlation of both granzymes with IFN-γ release . In patients , lower plasma levels of granzyme B , but not granzyme A , were found at day of discharge compared to admission , indicating an association of granzyme B with stage of disease . Peripheral blood mononuclear cells of typhoid fever patients had a higher percentage of lymphocytic cells expressing intracellular granzyme A and granzyme B , but not granzyme K , compared to controls . The marked increase observed in extra- and intracellular levels of granzyme expression in patients with typhoid fever , and the correlation with stage of disease , suggests a role for granzymes in the host response to this disease .
Typhoid fever , caused by the intracellular and human-specific Gram-negative bacterium Salmonella ( S . ) enterica serovar Typhi , remains an important cause of illness and death in many parts of the world . The global burden of this systemic infection is estimated to be around 26 . 9 million cases of per year resulting in over 200 , 000 deaths annually [1–3] . However , the remarkable mechanisms for cellular persistence of S . Typhi remain ill defined . Cellular mediated immune responses against S . Typhi infection rely largely on two types of lymphocytic cells: CD4+ and CD8+ T cells [4] . The presence of both CD4+ helper T cells and S . Typhi-specific CD8+ T cells has been observed in humans challenged with oral S . Typhi or immunized with the attenuated oral Ty21a typhoid vaccine [4 , 5] . Cytotoxic CD8+ T cells and natural killer ( NK ) cells are important effector cells of cell-mediated immunity and are involved in adaptive and innate immune responses . It is well established that T lymphocytes and NK cells are important sources of interferon ( IFN ) -γ , a critical cytokine for systemic control of Salmonella infection [6] . Interestingly , a human vaccine study showed that the killing of S . Typhi-infected cells by specific CD8+ T cells is executed through a Fas-independent , but granule-dependent mechanism , which suggests a role for granzymes in the containment of S . Typhi [7] . Granzymes are a family of serine proteases found in the cytoplasmic granules of cytotoxic lymphocytes . To date , five human granzymes ( A , B , H , K and M ) have been described of which granzyme A and B have been studied most extensively [8] . The classic role of granzymes is to promote cytotoxic lymphocytes-mediated eradication of infected , neoplastic , or foreign cells via the induction of cell death . However , it is now accepted that granzymes can be expressed in other cell types of immune and non-immune origin , and increasing evidence support that they can also play a role in inflammation [9] . Extracellular granzymes have been shown to exhibit different functions i . e . propagate inflammation and cytokine processing but not cell death [10] . Circulating granzymes can be measured in the plasma of patients and are considered to reflect the involvement of cytotoxic CD8+ T cells and NK cells in diverse disease states [11] . Elevated levels of granzyme B are a characteristic feature of various chronic inflammatory diseases and are thought to reflect a state of hyper-inflammation [10 , 12] . Furthermore , it has been shown that granzymes are elevated in patients with malaria , endotoxemia , sepsis and tuberculosis [8 , 12–14] . However , the expression of intra- and extracellular granzymes in patients with S . Typhi infection has , to the best of our knowledge , never been studied . In the present study , we aimed to characterize the expression of extracellular and intracellular granzymes in patients with typhoid fever . For this , we analyzed the extracellular levels of granzyme A and B , as well as the intracellular expression of granzymes A , B and K in lymphocyte subsets , in patients with culture-proven typhoid fever compared to controls . We also investigated whether the extracellular levels of granzyme correlated with IFN-γ release .
A total of 143 eligible febrile adult patients were prospectively recruited over a 6-month period in 2012 after admission to Chittagong Medical College Hospital , a 1 , 000-bed government hospital located in Chittagong , eastern Bangladesh . From all these febrile patients , blood samples ( 24 mL ) were obtained within 48 hours after admission to the hospital and at discharge , and were collected into EDTA or heparin tubes ( BD vacutainer ) , or BactAlert blood culture bottles ( bioMérieux ) . A total of 28 of the 143 febrile patients tested positive for S . Typhi either with blood-culture and/or S . Typhi PCR in blood , urine or feces as described [15] . From a subset of 8 patients with only blood-culture confirmed typhoid fever additional consent was required and blood was drawn ( 30 mL ) within 72 hours and collected in CPT Cell Preparation Tubes with sodium heparin for cell separation ( BD Vacutainer ) . Thirty-eight healthy Bangladeshi volunteers from among the hospital staff who were known to have no illness and were not currently receiving any medication were recruited and served as control population . The study protocol was approved by the National Research Ethics Committee ( NREC ) of Bangladesh ( BMRC/NREC/2010-2013/1543 ) and the Oxford Tropical Research Ethics committee ( OXTREC reference 25–11 ) . Informed written or thumbprint consent was taken from the subject or caretaker for all cases and controls . Plasma samples were stored immediately at −20°C after obtention . Soluble granzyme A and B were measured by sandwich ELISA ( eBioscience; LD 2 pg/ml ) in plasma , in accordance with the manufacturer’s recommendations . Human tumour necrosis factor ( TNF ) -α , interleukin ( IL ) -1β , IL-6 , IL-8 , IL-10 , IL-12p70 , and IFN-γ were measured by cytometric-bead-array multiplex assay ( BD Biosciences; LD 0 . 5 pg/mL ) . Aspartate transaminase ( AST ) , alanine transaminase ( ALT ) and renal function were measured in plasma with spectrophotometry ( Roche Diagnostics ) as for previous study [16] . Peripheral blood mononuclear cells ( PBMCs ) were isolated from blood collected in CPT Cell Preparation Tubes and handled according to manufacturer's instructions ( BD Vacutainer ) . After isolation , PBMCs were counted using count chamber ( Loptik labor ) and cells were suspended in RPMI 1640 ( Gibco ) with 20% Fetal Bovine Serum ( FBS; Lonza ) . Another equal volume of RPMI with 20% FBS and dimethyl sulfoxide ( Gibco ) was gently added to the cryovial drop by drop . To ensure stepwise temperature decrease , cryovials containing PBMCs were put in an alcohol-free container using a temperature exchange system ( CoolCell , Biocision ) to achieve temperature lowering at ~1°C/min rate and placed in a −80°C refrigerator before shipment . Prior to analysis stored cells were carefully thawed , washed and stained with monoclonal antibodies against CD3 ( AF700 ) , CD4 ( PerCP-Cy5 . 5 ) , CD56 ( APC ) ( all from BD Pharmingen ) and CD8 ( PE-Cy7; Biolegend ) , at 4°C for 25 min in the dark . For the intracellular staining , cells were fixed for 20 min in Cytofix/Cytoperm ( BD Bioscience ) at 4°C in the dark before washing . Subsequently , the cells were suspended in a buffer containing the antibodies against granzyme A ( PE; BD Pharmingen ) , granzyme B ( PE-CF594; BD Horizon ) and granzyme K ( FITC; Santa Cruz Biotechnology ) before analyzing with a FACSCanto ( BD Bioscience ) . FlowJo software ( Tree Star Inc . ) was used for analysis . Lymphocytes were gated in the forward scatter versus side scatter dot plot . Cells were selected as CD3+ or CD56+ , or as CD3+CD4+ ( CD4+ T cells ) , CD3+CD8+ ( CD8+ T cells ) , CD3+CD56+ ( CD56+ T cells ) and CD3−CD56+ ( NK cells ) , and expression of granzymes was analyzed in these populations as described previously [14] . The results are expressed as percentage of cells of the specific lymphocyte population expressing the corresponding granzyme and as the median fluorescence intensity ( MFI ) . Alternatively , to analyze the lymphocyte source of each granzyme , cells were selected as positive for each granzyme and the percentage of the above-mentioned lymphocyte subpopulations were analyzed within the granzyme-positive lymphocytes . Values are expressed as median and interquartile ranges ( IQR ) unless indicated otherwise . Differences between groups were analyzed by Mann-Whitney U test . For correlations Spearman Rho is reported . These analyses were performed using GraphPad Prism version 6 . 0 for Mac ( GraphPad Software ) and SPSS version 15 . 0 ( Chicago , Ill , USA ) . A P<0 . 05 was considered to represent a statistically significant difference .
We included 28 patients with confirmed typhoid fever: 11 ( 39% ) of these confirmed cases were diagnosed by isolation of S . Typhi from blood , and 17 ( 61% ) by positive S . Typhi PCR in blood ( 15 ) , urine ( 2 ) and/or feces ( 1 ) [15] . A summary of baseline clinical features and laboratory results of patients and controls are shown in Table 1 . While the total white blood cell count ( WBC ) did not differ between patients and controls , there was a significant shift towards a higher neutrophil and lower lymphocyte count in typhoid fever patients , which is a characteristic clinical feature for this disease [17] . Consistent with the literature on typhoid fever [2] , levels of IL-6 , IL-8 , IL12p70 and IFN-γ were significantly increased in patients compared to controls . However , IFN-γ was the only marker that was significantly increased when comparing blood-culture negative/PCR positive patients with blood-culture positive patients ( median 2 . 0 IQR [0 . 5–156] versus 598 [292–773] , P<0 . 001 ) , suggesting a role for IFN-γ during active bacterial replication of S . Typhi in the blood stream . IL-10 tended to be elevated in patients but the difference with controls did not reach statistical significance , partly due to a large inter-individual variation . IL-1β was undetectable and TNF-α was low in both patients and controls . To first establish the presence of circulating granzymes during clinical typhoid fever we measured granzyme A and B in the plasma of 28 patients with S . Typhi infection and 38 healthy controls . Both granzyme A and B were elevated in the plasma of patients with typhoid fever compared to healthy controls ( median 16 IQR [8 . 8–33] versus 5 . 7 [3 . 7–7 . 1] pg/ml , P<0 . 001 , and median 23 . 3 IQR [5 . 5–51 . 3] versus 3 . 2 , [2 . 4–7 . 8] pg/ml , P<0 . 001 respectively; Fig 1A–1B ) . In patients , a moderate correlation was seen between granzyme A and B levels ( Spearman Rho r = 0 . 60; P<0 . 001 , Fig 1C ) . Granzyme A levels ( P<0 . 01 ) , but not granzyme B levels ( P = 0 . 16 ) were significantly elevated in culture-positive patients compared to culture negative/PCR-positive patients , which suggests that bacteria circulating in the bloodstream are inducers of extracellular granzyme A release . We next determined the correlation between extracellular granzymes and IFN-γ levels , since IFN-γ showed to be an important acute phase cytokine in this cohort . Positive correlations were observed between IFN-γ and granzyme A ( Spearman´s Rho r = 0 . 80 , P<0 . 001; Fig 1D ) , and granzyme B ( Spearman Rho r = 0 . 52 , P<0 . 001; Fig 1E ) . In order to determine if granzyme levels correlated with stage of disease we obtained plasma samples of patients at discharge and compared them to admission samples . Plasma granzyme B ( median 7 . 1 , IQR [3–11] pg/ml , P<0 . 05 ) , but not granzyme A levels ( median 13 . 4 , IQR [4 . 5–16 . 4] pg/ml , P = 0 . 26 ) were decreased at follow-up when patients were clinically improved ( Fig 2A–2B ) . Plasma levels of granzyme B ( P = 0 . 18 ) , but not granzyme A ( P<0 . 01 ) levels returned to normal during convalescence comparing samples of healthy controls to patient discharge samples . To identify the lymphocytes subsets and the cells expressing intracellular granzymes A , B and K , flow cytometry was performed in cells from 36 control individuals and from 8 culture-positive typhoid fever patients . As in the total groups of controls and patients , in these subgroups both percentage of cells ( median 34 . 5 IQR [31 . 5–39 . 3] and 17 . 5 [15 . 0–21 . 0] respectively for controls and patients; P<0 . 0001 ) and cell numbers ( median 28 . 3 IQR [23 . 4–30 . 4] and 11 . 6 [8 . 1–15 . 5] cells x 108/L respectively; P<0 . 0001 ) were lower in typhoid fever patients . A significant decrease in cell numbers of CD8+T , CD4+T , CD56+T and NK cells was found in patients compared to controls ( Table 2 ) . However , only CD4+ T cells presented a lower percentage in patients . Interestingly , the percentage of NK cells remained unchanged , and CD8+T as well as CD56+T cells , a subset of innate lymphocytes that possess the characteristics of both NK and T cells [18] , showed higher percentages in the typhoid fever group ( Table 2 ) . In line with the measured extracellular granzyme levels , lymphocytes of typhoid fever patients had a higher percentage of cells expressing intracellular granzyme A and granzyme B than controls , although the increase in cell numbers was not significant ( Table 2 ) . We also measured the cells expressing granzyme K , which is thought to stimulate monocytic cells to secrete pro-inflammatory mediators like granzyme A [11] , and this was increased in typhoid fever patients albeit not statistically significantly ( Table 2 ) . On day of discharge , percentage of total lymphocytes expressing granzymes remained comparable to day of admission and significantly different from controls ( Table 2 ) . In typhoid fever patients , the number of lymphocytes producing both granzyme A and B simultaneously was almost doubled compared to controls ( 25% vs 49% , P<0 . 001 ) . We then analyzed which lymphocyte subsets were expressing each granzyme ( that is , the cellular source ) . For this , we identified by flow cytometry the cells positive for each granzyme within the lymphocytes gate , and determined the percentage of cells belonging to each lymphocyte subset in each group of granzyme+ cells . As expected [14] , we found that CD3+ cells were the main producers of the three granzymes , with a significant increase in the percentage of granzyme A and B expressed by CD3+ cells in patients compared to controls ( median values of total cells positive for each granzyme that were CD3+ , controls vs patients: 66% vs 87% for granzyme A , 53% vs 78% for granzyme B , and 91% vs 80% for granzyme K ) . Next , we determined the percentage of cells that expressed granzymes per lymphocyte subset ( Fig 3A–3D ) . For this , we identified by flow cytometry each lymphocyte subset within the lymphocytes gate , and determined the percentage of granzyme+ cells in each subset . We found that the percentage of CD8+ T cells , CD4+ T cells , CD56+ T cells , and NK cells positive for granzyme A was significantly higher in patients compared to controls . The percentage of CD8+ T cells and CD4+ T cells positive for granzyme B was also significantly higher in patients than controls . This was not the case for CD56+ T cells or NK cells . Percentages of subset of lymphocytes expressing granzyme K did not differ between both groups of individuals . The median fluorescence intensity ( MFI ) of the three different granzymes ( i . e . , the amount of each granzyme expressed by granzyme+ cells ) was also determined in each subset of lymphocytes ( Fig 3E–3H ) . The MFI of cells expressing granzyme A was significantly increased in CD8+ T , CD4+ T , CD56+ T and NK cells , whilst the MFI of the CD56+ T cells expressing granzyme B was lower in patients compared to controls . The MFI of cells expressing granzyme K was also significantly lower in CD8+ T cells and NK cells in patients compared to controls .
Our study aimed to investigate the extracellular levels of granzymes A and B , as well as the intracellular expression of granzymes A , B and K in different lymphocyte subsets in patients with confirmed S . Typhi infection at the time of admission and discharge , and compare them to healthy controls . To the best of our knowledge , this is the first study to describe the expression of granzymes in patients with typhoid fever . Additionally , we intended to examine the correlation between granzymes expression and IFN-γ levels . The present study showed an increase in extracellular levels of granzyme A and B in patients with typhoid fever compared to controls , and lower levels of granzyme B in patients at day of discharge compared to admission . The extracellular expression of both granzymes correlated in patients with the concentration of IFN-γ , a critical cytokine for systemic control of Salmonella infection . On the other hand , the proportion of different subsets of lymphocytes expressing intracellularly granzyme A and granzyme B were increased in patients at acute stage . Typhoid fever is a systemic infection in which lymphocytes play an essential role in the protective immune response [4 , 19] . In contrast to other Gram-negative bacterial infections , typhoid fever patients usually have normal to low leucocyte numbers , and low lymphocyte counts [17] . In corroboration , patients reported in this study had a normal leucocyte count with markedly lower lymphocyte numbers , accompanied with a decrease in the percentage of CD4+ T cells but an increase of CD8+ T and CD56+T cells . IFN-γ is critical for the systemic control of S . enterica infections . Although CD4+ T cells were thought to be the key producers of IFN-γ during Salmonella infections [19 , 20] , it is well established that T cells and NK cells are important sources of this cytokine [4] . Human challenge studies using attenuated S . Typhi live oral vaccines have shown that sensitized lymphocytes proliferate and produce IFN-γ in response to a number of S . Typhi-antigens [20] . CD4+ T cells are apparently the predominant IFN-γ-secreting cells associated with this response , although CD8+ T cells are also present and secrete IFN-γ [19 , 21] . Unsurprisingly , we found elevated levels of IFN- γ in patients compared to controls . Granzymes are serine proteases promoting cytotoxic lymphocytes-mediated eradication of intracellular pathogens via the induction of cell death [10] . Granzymes are delivered intracellularly into target cells where they can activate pathways of apoptosis . Furthermore , granzymes can be released into the extracellular environment by both immune and non-immune cells , and may propagate cytokine processing and inflammation , so having other functions than intracellular cytotoxicity [10] . Elevated levels of soluble granzymes have been reported in many different infectious diseases , reaching peak levels in patients with severe sepsis [11 , 13] . In addition , we have demonstrated earlier that bacterial stimuli can induce the extracellular release of granzymes [11] . A study using PBMCs from individuals immunized with the Ty21a typhoid vaccine , has shown that CD8+ T cell-induced cytotoxicity was mediated by the granule exocytosis pathway [7] , involving the release of perforin and granzymes into the intercellular space , thereby mediating cell death [8 , 10] . Another report , however , provided results demonstrating that IFN-γ–independent cytotoxic mechanisms , mediated by granzymes or perforin [6] , were not sufficient for NK cell—mediated limitation of the bacterial replication of S . Typhimurium . In the present study , we demonstrate that plasma concentrations of extracellular granzyme A and granzyme B were both elevated in typhoid fever at day of admission . Disease severity correlated with extracellular granzymes , as plasma granzyme correlated with IFN-γ levels and lower levels were seen on day of discharge compared to admission , which is in line with an earlier study using the typhoid vaccine [19] . This may suggest a role for granzymes in the stimulation of IFN-γ release , or that the inflammatory process simultaneously activates the release of both proteins . Of note , extracellular granzyme A , but not B , was significantly elevated in culture-positive patients suggesting that bacteria circulating in the bloodstream are potent inducers of extracellular granzyme A release . When we explored the intracellular expression of granzyme A , B and K in lymphocytes by flow cytometry , we found that , in spite of the decreased numbers of lymphocytes , both the numbers ( although non-significant ) and the percentages of granzyme A and B , were increased in culture-proven typhoid fever patients compared to controls . As expected [14] , we observed that the CD3+ lymphocytes were the main producers of the intracellular granzymes , both in controls and patients . In this study , we also found that all subsets of lymphocytes were expressing granzyme A in significantly higher percentages and with greater MFI ( that is , the granzyme+ cells were expressing increased amounts of granzyme ) in patients than in controls , suggesting a role for granzyme A during typhoid fever . For granzyme B , the percentage of CD8+T and CD4+T cells , but not CD56+T and NK cells , expressing the granzyme was significantly higher in patients than in controls . It is worth noting here that the percentage of NK and CD56+T cells expressing granzyme B was already very high in control individuals ( around 90% ) . However , in contrast to granzyme A , the MFI of cells expressing granzyme B did not significantly increase , and were even decreased in CD56+T cells . Regarding granzyme K , which is thought to stimulate monocytic cells to secrete pro-inflammatory mediators like granzyme A , our results do not indicate a role for this granzyme in these patients . Further studies are needed to confirm these findings and see whether granzymes are indeed playing a role in the host defense against Salmonella Typhi . In summary , patients demonstrated a marked increase of extracellular levels of granzyme A and B in acute phase plasma of patients with typhoid fever , and showed evidence for an association of these granzymes with higher levels of IFN- γ and with disease severity . Lymphocytes of typhoid fever patients showed higher levels of intracellular granzyme A and B , but not K , compared to healthy controls . | Typhoid fever is an ( sub ) acute febrile illness that remains an important global burden with more than 27 million cases worldwide each year and an estimated 217 , 000 deaths . During infection by Salmonella ( S . ) Typhi , the etiologic agent for typhoid fever , a cascade of antimicrobial functions is triggered and causes release of signaling and cytotoxic proteins for the rapid control of the infection . Granzymes are proteins promoting cytotoxic lymphocytes mediated eradication of intracellular pathogens via the induction of cell death and which can also play a role in inflammation . In the present study we analyzed extracellular levels of different granzymes in healthy volunteers and patients with confirmed S . Typhi infection at the time of admission and discharge , as well as their correlation with levels of interferon ( IFN ) -γ , a cytokine significantly expressed in typhoid fever . Patients demonstrated a marked increase of extracellular released granzyme A and B in acute phase plasma , which correlated with IFN-γ levels , while granzyme B levels were associated with disease stage . Intracellular expression of both granzymes was also increased in patients compared to controls . In conclusion , granzymes are markedly elevated in human typhoid and correlate with stage of disease , suggesting their involvement in the host response to the disease . | [
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... | 2017 | Expression of intra- and extracellular granzymes in patients with typhoid fever |
In eukaryotic cells , local chromatin structure and chromatin organization in the nucleus both influence transcriptional regulation . At the local level , the Fun30 chromatin remodeler Fft3 is essential for maintaining proper chromatin structure at centromeres and subtelomeres in fission yeast . Using genome-wide mapping and live cell imaging , we show that this role is linked to controlling nuclear organization of its targets . In fft3∆ cells , subtelomeres lose their association with the LEM domain protein Man1 at the nuclear periphery and move to the interior of the nucleus . Furthermore , genes in these domains are upregulated and active chromatin marks increase . Fft3 is also enriched at retrotransposon-derived long terminal repeat ( LTR ) elements and at tRNA genes . In cells lacking Fft3 , these sites lose their peripheral positioning and show reduced nucleosome occupancy . We propose that Fft3 has a global role in mediating association between specific chromatin domains and the nuclear envelope .
Nuclear architecture , i . e . non-random positioning of chromosomal loci and nuclear components in three dimensions , is important in organizing genome processes such as transcription , DNA replication and DNA repair [1–3] . One crucial aspect of this organization is the interaction between chromatin and the nuclear periphery . In many eukaryotic species , chromosomal domains near the nuclear envelope show low expression levels , repressive chromatin marks and low gene density [4–7] . These domains interact with the nuclear lamina [4 , 5] or inner nuclear membrane ( INM ) proteins , such as the LEM-domain proteins [6 , 7] . How chromosomal loci are targeted to the nuclear envelope is still poorly understood , although step-wise methylation of lysine 9 on histone H3 ( H3K9me ) [8 , 9] and interaction with different nuclear membrane proteins [10] are known to contribute . In the fission yeast Schizosaccharomyces pombe , the INM protein Man1 interacts with about a third of the genome , mainly at lowly transcribed genes [7] . Especially striking , we observed Man1 binding to large domains adjacent to the telomeres of chromosomes I and II , which are characterized by a unique type of chromatin [11] . These subtelomeric domains show low levels of histone methylation , in both the repressive mark H3K9me2 and the active mark H3K4me2 , as well as lower histone acetylation and H2A . Z levels compared to euchromatin [11 , 12] . Many genes in these regions are lowly expressed in rapidly growing cells , but are induced during nutritional stress or meiosis [13] . The borders of the subtelomeric domains are bound by the chromatin remodeling factor Fft3 [14] . Maintenance of the special chromatin state inside these domains in rapidly growing cells depends on Fft3 , since a deletion of the remodeler results in a strong upregulation of subtelomeric genes and spreading of euchromatin marks into the domains . Fft3 belongs to a highly conserved subfamily of SNF2 remodeling enzymes . Fft3 homologs are present in all eukaryotes examined , including Fun30 in S . cerevisiae , ETL1 in mouse and SMARCAD1 in humans [15 , 16] . Fun30 subfamily enzymes act in regulating chromatin , maintaining silent chromatin domains and preserving genome stability [14 , 16–22] . Here , we explore the interplay between genome organization and transcriptional regulation . We show that Fft3 maintains chromatin structure and peripheral positioning of subtelomeres by binding to and remodeling nucleosomes at their borders . In addition to the subtelomeres , Fft3 interacts with TFIIIC and Pol III at tRNA genes and affects nucleosome positioning and interaction with the nuclear periphery . We propose that Fft3 maintains nucleosome stability and peripheral positioning of these elements and thereby preserves proper genome organization .
We previously showed that subtelomeric genes are upregulated in cells lacking the chromatin remodeler Fft3 [14] . To see whether these expression changes coincide with an altered chromatin structure , we performed genome-wide ChIP-chip for three hallmarks of active chromatin: RNA polymerase II ( Pol II ) , the histone variant H2A . Z and the histone modification H4K12Ac . All three marks show a striking increase over the subtelomeric chromatin domains on chromosomes I and II ( Fig . 1A ) in fft3Δ cells compared to wild type , allowing for a chromatin structure more permissive to transcription . These increases are significant when compared with the rest of the genome ( p<0 . 002 , Fig . 1 B-D ) and could be verified by ChIP qPCR ( S1 Fig ) . Based on these observations , we conclude that Fft3 affects both expression levels and chromatin properties at the subtelomeres . All data is shown as boxplot , with p-values calculated by circular permutation test ( see Materials and Methods ) . When we mapped interactions between the INM protein Man1 and chromatin , we observed that subtelomeres are strongly enriched for Man1 [7] . We hypothesized that the expression and chromatin changes in fft3Δ cells might be accompanied by an altered nuclear organization . Indeed , DamID mapping revealed that Man1 interactions with the subtelomeres are strongly reduced in fft3Δ cells ( Fig . 2A-C , S2 Fig ) . Interestingly , Fft3 itself does not bind the subtelomeric domains , only their borders ( Fig . 2A , D ) . Taken together , these results suggest that deleting fft3 leads to drastic changes in chromatin composition and positioning of these large domains while interacting only with the domain borders . Our data suggest that Man1 and Fft3 have essential roles in tethering subtelomeres to the NE . To explore this further , we monitored the intranuclear position of the telomeres in live cells using the telomere associated protein Taz1 . Based on their relative distance to the nuclear envelope , the Taz1 signals in each cell were assigned to one of three zones of equal volume ( Fig . 3A ) . As expected , about 75% of telomere signals are localized in the outermost zone , close to the nuclear envelope , in wild type cells ( Fig . 3B ) . We observed no change in telomere localization in fft3Δ cells ( Fig . 3B ) , suggesting that the telomeres themselves are unaffected by the changes in the subtelomeres . The anchoring of telomeres to NE has been shown to depend on the Bqt4 protein [23] . Indeed , we observed that deleting bqt4 reduced the number of telomeres in zone 1 to below 60% , with an increased number of cells showing telomere signals in the inner zones ( Fig . 3C ) . The loss of peripheral localization is exacerbated in cells lacking both Bqt4 and Fft3 ( Fig . 3D-E ) , with more than 50% of the cells now showing telomere signals in the two innermost zones . We obtained similar results using fluorescence in situ hybridization ( FISH , S3 Fig ) . Signals from a subtelomeric FISH probe showed a modest shift towards the interior in fft3Δ cells compared to wildtype . In fft3Δ bqt4Δ cells , the majority of FISH signals were found in the two innermost zones , while deletion of bqt4 by itself had no effect on localization . Taken together , these results demonstrate that Bqt4 and Fft3 work together in anchoring the subtelomeres to the nuclear envelope , with Bqt4 attaching the telomere end and Fft3 preserving the interaction with Man1 over the subtelomeres . We next asked what marks the subtelomeric borders aside from Fft3 binding . Interestingly , all four borders feature long terminal repeat elements ( LTRs ) either at the Fft3 binding sites or in close proximity ( Fig . 4A ) . Assuming that this is a sequence feature that Fft3 recognizes , we looked at all LTRs in the S . pombe genome and found that Fft3 is strongly enriched over these elements genome-wide ( Fig . 4B ) . To study how Fft3 affects nucleosome positioning and occupancy , we performed micrococcal nuclease digestion followed by sequencing ( MNase-seq ) . Interestingly , we observed a significant decrease in nucleosome occupancy over LTR elements in fft3Δ cells compared to wild type ( Fig . 4C ) . This suggests that Fft3 is required for either positioning or maintaining a nucleosome over LTRs . Since Fft3 alters interactions with the nuclear envelope at subtelomeres , we wondered whether LTRs are affected similarly . In wild type cells , LTR elements are enriched for binding of both Man1 and the nucleoporin Nup85 [24] ( Fig . 4D , S4A Fig ) . Binding for both is slightly reduced when Fft3 is deleted ( Fig . 4E , S4B Fig ) , but still higher than at other loci in the genome in the case of Man1 . When we looked at the LTRs in the border of subtelomere IIL specifically , we observed a marked decrease in Man1 association ( S4C Fig ) , suggesting that LTRs in the subtelomeric borders lose their interaction with the nuclear envelope . We wondered whether inserting an LTR into a locus is sufficient to recruit Fft3 , but found no increase in Fft3-myc occupancy by ChIP adjacent to the inserted LTR ( S4D Fig ) . This argues against a sequence-specific recognition of LTRs by Fft3 and rather suggests that other factors such as local chromatin structure are necessary for recruitment . In summary , we conclude that Fft3 binds to LTR elements , affecting their nucleosome occupancy and—in part—their peripheral positioning . This raises the question how Fft3 affects chromatin structure at subtelomeric borders . MNase-seq data shows several changes in nucleosome occupancy close to the Fft3 binding sites at the borders ( Fig . 5A , S5 Fig ) . In most cases , nucleosome occupancy was reduced in fft3Δ cells compared to wild type , suggesting that loss of Fft3 leads to reduced nucleosome stability in these regions . Based on these observations , we hypothesized that the catalytic activity of Fft3 is essential for its functions in subtelomeric chromatin regulation . We therefore constructed a variant of Fft3 with a point mutation in the ATPase domain ( fft3-K418R , Fig . 5B ) , which is known to lead to loss of enzymatic activity [25 , 26] . The resulting catalytically inactive variant of Fft3 is expressed at similar levels ( Fig . 5C ) and recruited to the same targets as unmodified Fft3 ( Fig . 5D , S6A–C Fig ) . However , fft3-K418R cells mimic the phenotype observed in fft3Δcells: they grow slightly slower than wild type cells at 30°C and show severe growth defects at 25°C and 37°C ( S6D Fig ) . Importantly , subtelomeric genes are upregulated in cells carrying the point mutation in Fft3 ( Fig . 5E ) , as observed in cells lacking Fft3 . Furthermore , we observed similar increases in PolII , H3K9Ac and H2A . Z occupancy in fft3-K418R and fft3Δcells ( S7A–C Fig ) . As in fft3Δ cells , Man1-interaction is reduced in fft3-K418R cells compared to wild-type ( S7D Fig ) . Together , these results strongly suggest that ATP-dependent remodeling by Fft3 is directly required to maintain silencing of subtelomeric genes and chromatin structure at the boundaries to the subtelomeres . After observing the function of Fft3 at subtelomeres and LTR , we set out to explore whether Fft3 also plays a role in regulating chromatin structure elsewhere in the genome . Among other features , we found a preference of Fft3 for snRNA genes , snoRNA genes and replication origins ( S8A Fig ) . Most prominently , we observed a significant enrichment of Fft3 at tRNA genes ( Fig . 6A ) compared to the rest of the genome . tRNA genes are transcribed by the RNA polymerase III ( Pol III ) machinery and require the transcription factor TFIIIC for recruitment of Pol III ( reviewed in [27] ) . We also observed Fft3 enrichment at 5S rRNA genes , which are transcribed by Pol III ( S8B Fig ) . When we compared the Fft3 binding profile to Pol III and TFIIIC maps [28] , we found a strong overlap in binding sites ( Fig . 6B ) . Several reports have identified loci called ETC ( Extra-TFIIIC ) or COC ( chromosome-organizing clamps ) sites across the genome that are occupied by TFIIIC but not by any other components of the Pol III machinery [28–30] . While Fft3 binding coincides with Pol III binding , we did not observe enrichment of Fft3 at ETC/COC sites ( Fig . 6C ) . This suggests that Fft3 binds to actively transcribed Pol III sites . To search for interaction partners of Fft3 , we carried out a yeast two-hybrid screen using the full-length Fft3 protein as the bait against an S . pombe cDNA library . This screen identified several cDNAs encoding Sfc4 ( Fig . 6D ) , a subunit of TFIIIC and homologous to S . cerevisiae Tfc4p and human TFIIIC102 [31] . The clones cover a set of tetratricopeptide repeats ( TPR ) which function as sites for protein-protein interactions [32] , suggesting that Fft3 could interact with Sfc4 through this domain . To examine whether Fft3 and Sfc4 also interact in vivo , we constructed a double-tagged strain ( Fft3-TAP / Sfc4-Myc ) and performed a co-immunoprecipitation assay . We observed an enrichment of Sfc4-Myc in the anti-TAP purified material from the double-tagged strain compared to the single-tagged strain ( Fig . 6E ) , confirming that Fft3 and Sfc4 physically interact in vivo . Taken together , our data indicate that the Fft3 chromatin remodeler directly interacts with the TFIIIC transcription complex at Pol III transcribed loci . Although tRNA genes are dispersed throughout the fission yeast genome , most of them cluster into a few foci in close proximity to centromeres , facilitated by condensin [33 , 34] . We found condensin binding strongly to tRNA genes both in wild type cells and fft3Δ cells ( S9 Fig ) , suggesting that clustering is unaffected the absence of Fft3 . Since the tRNA genes cluster close to the centromeres and therefore the nuclear periphery [34] , we asked whether their nuclear positioning changes in fft3Δ cells . While tRNA genes are enriched for Nup85 and Man1 interactions in wild type ( Fig . 6F-G ) , we observed a reduction of Man1 association in fft3Δ cells ( Fig . 6H ) . This suggests that tRNAs move away from the nuclear envelope in the absence of Fft3 . Interestingly , this effect seems to be specific to tRNA genes , since the 5S rRNA genes stay enriched for Man1 association when Fft3 is deleted ( S8B–D Fig ) . We then wondered if there are further effects at tRNA genes in fft3Δ cells . Therefore , we examined expression levels of two tRNA classes , proline and alanine , by northern blot and RT-qPCR ( S10A–C Fig ) . We observed an increase in transcription for proline , but not for alanine , suggesting there is no clear-cut effect of Fft3 on tRNA transcription . A clearer trend emerged when we looked at MNase sequencing data: like LTRs , tRNA genes show a pronounced decrease in nucleosome occupancy ( Fig . 6I ) . Similarly , 5S rRNA genes also showed a decrease in nucleosome occupancy ( S6E Fig ) . Together , these data indicate that Fft3 is involved in maintaining chromatin structure and intra-nuclear positioning of Pol III transcribed genes .
It has been established in different eukaryotic systems that the periphery of the cell nucleus harbors chromosomal domains with transcriptionally repressed chromatin . Insulators contribute to this three-dimensional nuclear organization by forming clusters to separate different chromatin domains [35] . Here we show that cells lacking the Fft3 remodeling enzyme or carrying a catalytically inactive version display drastic changes in chromatin marks and gene expression in the subtelomeric regions ( Fig . 7A ) . Fft3 is required to ensure the association of these domains with the nuclear envelope through the LEM domain protein Man1 . The budding yeast homolog of Man1 , Heh1p , is involved in regulating silencing of rDNA repeats [36] . Also in budding yeast , subtelomeres are associated with the INM protein Src1p [37] and subtelomeric genes change in expression when the Fft3 homolog Fun30 is missing [38] . Our data highlights that Fft3—even though itself not enriched at the subtelomeres per se—affects gene expression in these domains by acting on their borders . We show that local chromatin structure at the borders is altered in fft3Δ cells . This agrees with observations on the action of other chromatin remodelers at insulators , such as incorporation of H3 . 3 by PBAP at border elements in fruit flies [39] and binding of ISWI to ArsI insulators in sea urchins [40] . The borders of subtelomeres in S . pombe coincide with retrotransposon-derived LTR elements . In mouse cells , LTR elements can function as insulators [41] . Furthermore , the well-characterized gypsy insulator in Drosophila is derived from the gypsy retrotransposon ( reviewed in [42] ) . The Tf2 retrotransposons in fission yeast cluster within the nucleus to form Tf-bodies , and de-cluster in response to oxidative stress [43] . Our results show that Fft3 binds at or near LTR elements and affects their nucleosome occupancy and position in the nucleus . Taken together , these findings point to a conserved role for transposable elements in genome organization and insulation . It has been suggested that insulator elements function by changing higher-order chromatin structure ( reviewed in [44] ) . Insulators can interact with each other and tether the chromatin fiber to structural elements within the nucleus , e . g . nuclear pores . In this way , chromatin loops can form which separate euchromatin and heterochromatin domains . In S . pombe , Tf2 transposons cluster into Tf bodies that play a role in genome organization [43] . Furthermore , the Drosophila gypsy insulators form specialized insulator bodies at the nuclear periphery , and transposon-derived MAR/SAR sequences can create chromatin loops [42 , 45] . In agreement with this , we found that S . pombe LTR elements localize close to the nuclear periphery and nuclear pores . It is possible that this anchoring of LTR elements helps to divide loops of chromatin with different properties , such as the subtelomeric chromatin and neighboring euchromatin . In addition to LTR elements , Fft3 binds to tRNA genes and physically interacts with the Pol III transcription factor TFIIIC . These features may be conserved in the Fun30 remodeling family , since the S . cerevisiae homolog , Fun30 , is enriched over tRNA genes and the human homolog , SMARCAD1 , purifies together with the TFIIIC complex [16 , 17 , 21] . Based on our findings in S . pombe it is plausible that budding yeast and human Fun30 homologues also directly interact with TFIIIC . Like in S . pombe , tRNA genes function as insulators in other eukaryotes and play a role in genome organization by clustering at specific sites in the nucleus ( reviewed in [46] ) . It is noteworthy that both the insulating function and clustering of tRNA genes in yeast depend on TFIIIC [46] . It is therefore likely that Fft3 is recruited to tRNA genes through its interaction with TFIIIC and maintains the chromatin state at these insulators . Fft3 does not appear to be involved in condensin recruitment to tRNA genes , suggesting that condensin-dependent clustering of tRNA genes may not be affected . However , our results indicate that Fft3 is required for their anchoring at the nuclear envelope ( Fig . 7B ) . At the nucleosome level , we observed a reduction in nucleosome occupancy at Fft3 targets when the remodeler was missing . A similar decrease has also been observed at genes regulated by Fun30 in budding yeast [38] . This suggests that Fft3 is required to incorporate these nucleosomes or to stabilize them after incorporation . The nucleosomes could then serve either as a platform for binding or as a barrier for other proteins . Alternatively , Fft3 remodeling could affect chromatin mobility . INO80 , another Snf2 remodeling factor , facilitates chromatin movement inside the budding yeast nucleus by altering the stiffness of the chromatin fiber [47] . It is conceivable that Fft3 acts in a similar manner to allow for correct positioning of its targets within the nucleus . Beyond yeasts , subtelomeres are important in several other eukaryotic systems . In parasites such as Trypanosoma brucei and Plasmodium falciparum , the subtelomeres habor genes encoding surface markers that can be varied through recombination [48] and contribute to antigen variation . In T . brucei , these loci move from the nuclear periphery towards the nuclear interior when activated during differentiation [49] . In human cells , the D4Z4 insulator is located in the subtelomere of chromosome 4q and maintains subtelomeric heterochromatin [50] . Deletion of D4Z4 causes a type of muscular dystrophy , Facio-Scapulo-Humeral Dystrophy ( FSHD ) . Interestingly , insulator function of D4Z4 depends on CTCF and lamin A , and is involved in peripheral positioning of the telomere [51 , 52] . Taken together , these studies highlight the importance of links between subtelomeric chromatin states and nuclear positioning . In this study , we provide an example how a chromatin remodeler affects both local chromatin structure and genome-wide nuclear organization . Further studies will be necessary to shed light on this interplay between nuclear architecture and transcriptional regulation that seems to have a major role genome function in eukaryotes .
S . pombe cells were grown at 30°C and in YES medium unless stated otherwise . The S . pombe strains used in this study are listed in S1 Table . The point mutation in the ATPase domain of Fft3 was created using PCR with a primer containing the mutation . Genomic DNA was isolated from the Fft3-myc::hph strain ( Hu1911 ) and part of the gene , the myc-tag , the hygromycin resistance gene and part of the 3’UTR were amplified by PCR using primers ATPase-F and ATPase-R ( see S2 Table ) . The ATPase-F primer contained a mismatch introducing the K418R substitution ( AAA to AGA ) . To improve the recombination efficiency , the fragment was elongated by a second PCR using ATPaseL-F and ATPase-R primers . The PCR product was then purified and electroporated into a wild type strain ( Hu0029 ) . Strains in which the construct had replaced the wild type fft3+ gene through homologous recombination were selected by hygromycin resistance and confirmed by DNA sequencing . DNA was immunoprecipitated as described earlier [53] using 2μl of anti-H4K12Ac ( ab1761 , abcam ) , 2μl of anti-myc ( 9E10 , Sigma ) , 1 μl of anti-GFP ( ab290 , abcam ) , or 3μl anti-RNA polymerase II CTD repeat ( ab5408 , abcam ) antibodies per 100μl chromatin extracts . For microarray hybridization , immunoprecipitated DNA was amplified to 5 μg DNA as described in [53] , with the exception that in the second PCR , 5 mM dUTP was added to the reaction . Fragmentation , labeling and hybridization to the Affymetrix GeneChip S . pombe Tiling 1 . 0FR was performed by the Affymetrix core facility at Novum ( http://apt . bea . ki . se ) according to Affymetrix standard protocols . All experiments were done as biological duplicates . For real-time quantitative PCR , immunoprecipitated DNA was amplified in the presence of SYBR Green using Applied Biosystems 7500 real-time PCR machine . The primers used are listed in S2 Table . Genomic DNA was extracted as described previously [7] , with an additional clean-up of genomic DNA using the DNeasy Blood and Tissue Kit ( Qiagen ) . DamID experiments were done as described by [54] . The amplified material was cleaned using the Qiagen PCR purification kit and fragmented with DNAse . Fragmented DNA was labeled and hybridized to the Affymetrix GeneChip S . pombe Tiling 1 . 0FR using standard protocols by the Affymetrix core facility at Novum ( http://apt . bea . ki . se ) . All experiments were done as biological duplicates . RNA was extracted using the hot phenol method as described in [55] and reverse transcribed using random hexamers and the SuperScript II Reverse Transcriptase kit ( Invitrogen ) . Real-time quantitative PCR was performed in the presence of SYBR Green using an Applied Biosystems 7500 real-time PCR machine . Primers used are listed in S2 Table . All experiments were done as biological duplicates . Cells were grown to early log phase at 30°C in YES medium , fixed with adding paraformaldehyde to 1 , 75% for 40 min and processed for IF with mouse monoclonal anti-myc antibodies ( 9E10 , Sigma , 4μg/ml ) . For FISH , cells were refixed with 3% paraformaldehyde for 30min at room temperature . Chromosomal DNA was denatured by successive 15 min incubation time in 2SSC , 2SSC 10% formamide , 2SSC 20% formamide and 2SCC 40% formamide at room temperature . Subtelomeric FISH probes were generated using the sequencing cosmid c186 ( provided by the Wellcome Trust Sanger Institute , Hinxton , UK ) as template . c186 spans 30176bp on chr1R ( Range 5524764 to 5554939 ) , from SPNCRNA . 283 to SPAC186 . 09 . Images were taken using a Nikon A1+ laser scanning microscope with a 60x Lambda S oil-immersion objective ( NA 1 . 4 ) . Imaging was set up fulfilling the Nyquist criterion in xy and z , with a minimum zoom of 2 . For each cell , z-stacks were acquired with 0 . 2μm spacing and subjected to a blind 3D deconvolution algorithm using the NIS Elements Advanced Research software version 4 . 12 . After decapping ( removing the top three and bottom three z-stacks ) of each nucleus , distances from the center of each FISH spot to the nuclear periphery were measured . Measurements were then assigned to three concentric equal volume zones [56] . Significance tests were carried out using the Chi-square test . Cells were grown over night at 30°C in PMG liquid medium . For mounting , 35 mm glass bottomed culture dishes ( MatTek Corp ) were coated with soy lectin ( Sigma ) . A log phase cell suspension was added on top of the coated surface and cells were left to sediment for five minutes before pre-warmed PMG medium was added on top . During imaging , the culture dish was kept at 30°C inside the microscope incubator and cells were imaged for no longer than three hours . Images were taken using an inverted Nikon A1R laser scanning microscope with a 60x Lambda S oil-immersion objective ( NA 1 . 4 ) . Imaging was set up fulfilling the Nyquist criterion in xy and z , with a minimum zoom of 2 . For each nucleus , z-stacks were acquired with 0 . 125μm spacing in a 5 μm range around the center of the nucleus . Deconvolution of each stack was carried out using a 3D non-blind algorithm in NIS Elements ( Nikon , High Content Analysis software version 4 . 20 ) . After decapping ( removing the top three and bottom three z-stacks of the nuclear volume ) of each nucleus , distances from the center of each mCherry spot to the nuclear periphery were measured in the z-plane with the brightest signal . The radius of each nucleus was measured and used to normalize the distance measurement . Normalized distances were then assigned to one of three concentric equal volume zone [56] . Significance tests were carried out using a two-sided Chi-square test . Mononucleosomal DNA fragments were digested and purified as described in [57] . Fragments were then amplified and labeled with the NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina ( NEB #E6240 ) followed by sequencing on an Illumina Miseq v . 3 . Paired-end reads were mapped to the S . pombe genome using Bowtie2[58] with standard parameters . DANPOS [59] was then used to remove clonal reads , normalize by quantile normalization and calculate nucleosome occupancy . All calculations and visualization were carried out using R . All experiments were done as biological duplicates . MNAse-seq data can be accessed at NCBI GEO under the accession number GSE58013 . Proteins were extracted using a FastPrep-24 machine ( MP Biomedicals ) and separated by SDS PAGE . Immunoblot analysis was carried out using anti-myc ( 9E10 , Sigma ) and anti-actin ( ab8224 , abcam ) antibodies . The yeast-2-hybrid screen and data analysis were performed by Hybrigenics , Paris , France ( www . hybrigenics . com ) . The full-length open-reading frame of the fft3 gene ( SPAC25A8 . 01c ) was cloned into the pB27 vector as a C-terminal fusion to LexA . The construct was use as bait to screen a S . pombe cDNA library . 62 . 4 million interactions were tested . 50 ml of log phase growing cells were pelleted and resuspended in 500μl lysis buffer ( 20 mM Tris pH7 . 5 , 150mM NaCl , 3% glycerol , 0 . 05% Igepal , 0 . 5mM EDTA , 0 . 5 mM DTT and protease inhibitors ) and lysed in a FastPrep machine ( 5x30sec at 6 . 5 ) with 1 volume of glass beads . The extract was centrifuged for 20 min , 14 , 000 rpm at 4°C , and the supernatant was incubated with 60μl 50% IgG-bead slurry for 40 min at 4°C . The beads were washed five times with lysis buffer . Bound proteins were eluted with 30μl NuPAGE LDS Sample Buffer ( Invitrogen ) at 70°C for 10 min and subjected to immunoblot analysis using anti-myc antibody ( 9E10 , Sigma ) . The membrane was then stripped and incubated with anti-TAP antibody ( CAB1001 , Thermo Scientific ) 2 . 5ug of total RNA was electrophoresed in 10% polyacrylamide , 8M urea , 0 . 5x TBE gel and blotted onto Hybond Nx membrane ( GE Healthcare ) in 0 . 3x TBE using a semi dry blotter , followed by UV crosslinking . Oligonucleotide probes were 32P labelled using T4 PNK ( USB Affymetrix ) and hybridized in 5x Denhardt’s solution , 6xSSC , 10mM EDTA , 0 . 5% SDS , 0 . 1mg/ml salmon sperm DNA ( Invitrogen ) at respective temperatures for 16 hours . The blots were washed 3 times in 2xSSC 0 . 1% SDS and exposed to Phosphorimager screens ( Fuji ) . Screens were scanned using Molecular Imager FX ( BioRad ) . Band intensities were quantified using the Quantity One software ( BioRad ) . Probe sequences are listed in S2 Table . All analysis was performed in R ( http://www . r-project . org ) using the Bioconductor ( http://www . bioconductor . org ) packages “affy” , “affxparser” and “preprocessCore” with standard parameters . CEL-files were imported and normalized as described in [7] . For visualization , all probes were used , while only probes with one match in the genome were used for calculations and significance tests . To determine probe distributions for the subtelomeres , probes mapping to the following regions were used: 1–98950 bp and 5496300–5579133 bp on chromosome 1 , 1–96400 bp and 4437300–4539804 bp on chromosome 2 . For probe distributions across tRNA genes , probes were used which overlapped with sequence features marked “tRNA” in the GFF-annotation file downloaded from the Pombase website ( ftp://ftp . sanger . ac . uk/pub/yeast/pombe/GFF/pombe_09052011 . gff ) . For averaging of microarray scores across different genomic features , the “feature” column was used for all items with the following exceptions: features matching “LTRTF2” , “Tf2” ( together with the feature category “protein_conding_gene” ) , “origin_of_replication” and “external_name = intron” were annotated as LTRs , Tf2s , replication origins and introns , respectively . Microarray data published elsewhere was used as processed data when available or otherwise processed as described above . Boxplot were created in R using the “boxplot” function with standard parameters . Significance tests between data subsets ( subtelomeric and tRNA probes vs . all other probes ) were performed using a circular permutation as described in [7] . Microarray data can be accessed at NCBI GEO under the accession number GSE58013 . | In the genome of eukaryotic cells , domains of active and repressive chromatin alternate along the chromosome arms . Insulator elements are necessary to shield these different environments from each other . In the fission yeast Schizosaccharomyces pombe , the chromatin remodeler Fft3 is required to maintain the repressed subtelomeric chromatin . Here we show that Fft3 maintains nucleosome structure of insulator elements at the subtelomeric borders . We also observe that subtelomeres and insulator elements move away from the nuclear envelope in cells lacking Fft3 . The nuclear periphery is known to harbor repressive chromatin in many eukaryotes and has been implied in insulator function . Our results suggest that chromatin remodeling through Fft3 is required to maintain proper chromatin structure and nuclear organization of insulator elements . | [
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] | [] | 2015 | The Fun30 Chromatin Remodeler Fft3 Controls Nuclear Organization and Chromatin Structure of Insulators and Subtelomeres in Fission Yeast |
Governments have agreed to expand the global protected area network from 13% to 17% of the world's land surface by 2020 ( Aichi target 11 ) and to prevent the further loss of known threatened species ( Aichi target 12 ) . These targets are interdependent , as protected areas can stem biodiversity loss when strategically located and effectively managed . However , the global protected area estate is currently biased toward locations that are cheap to protect and away from important areas for biodiversity . Here we use data on the distribution of protected areas and threatened terrestrial birds , mammals , and amphibians to assess current and possible future coverage of these species under the convention . We discover that 17% of the 4 , 118 threatened vertebrates are not found in a single protected area and that fully 85% are not adequately covered ( i . e . , to a level consistent with their likely persistence ) . Using systematic conservation planning , we show that expanding protected areas to reach 17% coverage by protecting the cheapest land , even if ecoregionally representative , would increase the number of threatened vertebrates covered by only 6% . However , the nonlinear relationship between the cost of acquiring land and species coverage means that fivefold more threatened vertebrates could be adequately covered for only 1 . 5 times the cost of the cheapest solution , if cost efficiency and threatened vertebrates are both incorporated into protected area decision making . These results are robust to known errors in the vertebrate range maps . The Convention on Biological Diversity targets may stimulate major expansion of the global protected area estate . If this expansion is to secure a future for imperiled species , new protected areas must be sited more strategically than is presently the case .
In 2010 the 193 parties to the Convention of Biological Diversity ( CBD ) adopted a new strategic plan and set of targets to tackle the continuing decline in biodiversity [1] , [2] . A key element of this plan is Aichi target 11 , which includes a commitment to expand the global coverage of terrestrial protected areas from the current 13% to 17% by 2020 [1] . This could drive the most rapid expansion of the global protected area network in history [3] , but corresponding biodiversity benefits are far from guaranteed . This is because protected areas are often preferentially established in locations that are remote or have little agricultural value [4] , failing to protect the imperiled biodiversity found on more valuable land . Recognizing the failures of past protected area expansion , the current CBD text directs that protected areas should target places of “importance for biodiversity” that are “ecologically representative” [1] . However , these locations can be expensive to protect . For instance , the cost of expanding protected areas to cover all “important bird areas” ( IBAs ) has been estimated at US$58 billion annually ( although these sums are still small compared to government budgets ) [5] . Moreover , the majority of terrestrial regions have been identified as important for biodiversity by one or more global prioritization schemes [6] , which provides myriad alternatives for meeting protected area targets in locations that are cheap . Given this , where should new protected areas be located to deliver on the Aichi biodiversity targets ? One option could be based on Aichi target 12 , which aims to “prevent the extinction of all known threatened species and improve and sustain their conservation status . ” In situ conservation of viable populations in natural ecosystems has long been recognized as the fundamental requirement for the maintenance of biodiversity [7] . Hence measuring “biodiversity importance” in terms of protected area coverage of threatened species would help countries to simultaneously meet these two CBD targets . Using new data from the World Database on Protected Areas [3] and distribution maps for 4 , 118 globally threatened birds [8] , mammals [9] , [10] , and amphibians [10] , [11] , as well as ecoregions [12] , we first perform a gap analysis to determine the representation of these species in the current global protected area network . We then use a systematic conservation planning framework [13] to build scenarios for cost-efficiently expanding the global protected area network to contribute to meeting the protected area and threatened species Aichi targets . Recent works have investigated strategies for achieving Aichi Target 11 by protecting IBAs [5] , [14] or meeting the Global Strategy for Plant Conservation [15] . Our study is the first , to our knowledge , to use an optimization approach to develop scenarios for meeting the Aichi targets in a cost-efficient manner . Incorporating cost efficiency allows the identification of options for meeting Aichi target 11 that contribute optimally to target 12 while minimizing conflict with agricultural production .
To determine the extent of current protected areas , we extracted data on International Union for Conservation of Nature ( IUCN ) category I–VI protected areas from the 2012 World Database on Protected Areas [3] , excluding all proposed protected areas and those lacking “national” designation . For terrestrial protected areas with a known areal extent but lacking polygonal representation , we created a circular buffer of the appropriate area around its centroid . To prevent overestimation of the areal coverage of protected areas caused by overlapping designations , we merged buffered points and polygons into a single layer . Our final protected area layer contained 135 , 062 protected areas covering a total of 17 , 026 , 214 km2 , or 12 . 9% of the Earth's non-Antarctic land surface ( Figure 1A ) . We used distribution maps for birds [8] , mammals [10] , and amphibians [10] . We focused on these taxa as they are the only major terrestrial taxonomic groups that have been comprehensively assessed for their distribution and extinction risk [10] . We excluded marine species and areas , noting that there are specific coverage targets for protecting the marine realm . For all three taxonomic groups , we focused on those species that are listed by the IUCN Red List as Critically Endangered , Endangered , or Vulnerable , hereafter referred to as “threatened , ” resulting in 4 , 118 species in total ( birds = 1 , 135 , mammals = 1 , 107 , amphibians = 1 , 876; Figure 1B ) . We focus only on threatened species as these are by definition the most likely species to go extinct , and therefore are most important for slowing biodiversity loss and contributing to CBD Aichi target 12 . We excluded all portions of species ranges where the species was identified as extinct , introduced , or of uncertain origin . In addition to these data , we used data on the distribution of ecoregions as defined by the World Wildlife Fund [12] . To account for the spatial variation in the cost of protected area expansion , we used a dataset on agricultural opportunity cost [18] , converted to 2012 US$ and with no data values filled using regularized spline interpolation with tension ( Figure 1C ) . The dataset provides the estimated gross agricultural rents for terrestrial areas mapped at approximately the 5 km resolution . We use these data as our surrogate for the opportunity costs of establishing new protected areas , as agricultural expansion is the greatest single cause of habitat loss , as well as the one most commonly associated with habitat loss driven by multiple factors [19] , [20] . Agricultural opportunity costs also reflect the reduction in food security and tax revenue that national governments face when implementing protected areas . We applied a fixed cost of US$100 per km2 to reflect the transaction costs of acquiring new protected areas [21] , although we recognize there is likely to be considerable spatial variation in these costs . We did not attempt to estimate the ongoing management costs of protected areas following establishment , as this metric needs to account for a number of difficult-to-measure social and socioeconomic factors [22] , but a recent analysis estimated that these equate to ∼14% of the agricultural opportunity costs of protection [5] . We assessed the occurrence of threatened vertebrates within protected areas using a representation target and an adequacy target . The representation target was achieved if any portion of the species' distribution overlapped with the protected area network . To set adequacy targets we followed the method of Rodrigues et al . [23] to scale the target to the species' overall geographic range size . Complete ( i . e . , 100% ) coverage by protected areas was required for species with a geographic range of <1 , 000 km2 . For wide-ranging species ( >250 , 000 km2 ) , the target was reduced to 10% coverage , and where geographic range size was intermediate between these extremes , the target was log-linearly interpolated . To explore future scenarios for the growth of the global protected area network we used the systematic conservation planning software Marxan [24] . Marxan uses a simulated annealing algorithm to select multiple alternative sets of areas that meet prespecified conservation targets ( described in the following section ) while trying to minimize overall cost . All spatial data on the distribution of conservation features and conservation costs were summarized into a “planning unit” layer consisting of 30 km×30 km square pixels comprising the world's non-Antarctic terrestrial areas . We intersected this planning unit layer with the protected areas and agricultural opportunity layers and the geographic distribution of each of the 4 , 118 threatened species and ecoregions at a 500 m resolution . This allowed us to determine the agricultural opportunity cost of the unprotected portion of each planning unit and the protected and unprotected extent of each biodiversity feature within each planning unit . To explore the costs and benefits of alternate scenarios for achieving 17% protection of terrestrial areas , we developed four separate spatial scenarios using contrasting conservation targets . We accounted for the existing protected area network's contribution to the targets in each scenario , and then added additional protected areas to ensure all targets are met . In each scenario , the aim is to minimize the costs of meeting the conservation targets . However , to avoid the global protected area target being met only through increased protection in low-cost countries , which would reduce the total cost of the target , in all scenarios we maintain the constraint that each country must meet its national protected area target . Moreover , it is at the national level that the target is being interpreted and implemented . For each scenario , we used Marxan to perform 10 runs of 1 billion iterations each , each of which represents an alternate near optimal reserve network for meeting the relevant conservation targets at the lowest overall cost . From these 10 runs , we select and report on the results from the lowest cost solution . The IUCN [10] and Birdlife International and NatureServe [8] range maps used in this study comprise polygons showing distribution of 4 , 118 globally threatened birds , mammals , and amphibians . These maps may be subject to commission errors [26]–[29] , where the species is mapped as present in locations where it is in fact not present . As they affect range-based species conservation targets and lead to an overestimation of occurrence in existing or prioritized areas , commission errors could influence our study's main conclusions . We performed two analyses to determine the sensitivity of our primary results to commission errors ( Text S1 ) . First we created 100 range maps for each of the 4 , 118 species of birds , mammals , and amphibians that simulated commission error rates [25] by deleting 50% of the range of narrow-ranged species ( range<1 , 000 km2 ) , by deleting 25% of the range of wide-ranging species ( range>250 , 000 km2 ) , and by linearly extrapolating the deletion rate for species of intermediate ranges . Second , we identified the “Extent of Suitable Habitat” ( ESH ) using high-resolution species distribution models for 1 , 063 mammals [30] . The ESH maps were used to identify locations in the original maps for mammals that are likely to be commission errors . We then reran our analyses using ( a ) the maps with simulated commission errors and ( b ) the ESH maps , to quantify the effects of the simulated and mapped commission errors on our estimated biodiversity value of meeting the 17% protected area target , and the shape of the efficiency frontier between cost and threatened vertebrate coverage .
We find that 17% of threatened vertebrates are not found in a single protected area and 85% are not covered to the level of our adequacy targets ( Figure S1A ) . A decade ago , 20% of globally threatened terrestrial birds , mammals , and amphibians were not found in a single protected area and 89% were inadequately protected [15] . Our analysis using updated datasets indicates that the global protected area network has made little progress since then toward securing a future for the world's threatened biodiversity . We discover that if countries choose to expand their protected areas in a manner that minimizes agricultural opportunity cost , meeting their national-level targets for 17% coverage would entail a once-off transaction cost of US$0 . 9 billion and an annual agricultural opportunity cost of $4 . 9 billion ( Table 1 ) . As this option aligns with the previous pattern of protected area establishment , we view it as a likely business-as-usual scenario for meeting the terrestrial coverage aspect of Aichi target 11 . We find that this would result in only 852 ( 21% ) threatened vertebrates reaching targets for adequate coverage ( Figure S1B ) , an increase of only 249 species over existing protection ( Table 1 ) and arguably a failure to meet Aichi target 12 . Moreover , even if highly ambitious areal targets were to drive further growth of the global protected area network beyond 2020 , the costs of expansion would rise steeply without providing cost-effective coverage for threatened species ( Figure 2 ) . An alternative is to ensure a representative sample of major vegetation communities is protected , as this would protect a broader range of habitats and could lead to improved conservation outcomes . Target 11 calls for ecologically representative protected area coverage . We find that if countries meet their 17% coverage targets in a way that distributes protection across ecoregions equally , the opportunity cost of establishing the additional protected areas would be 4 . 5 times higher than the business-as-usual scenario ( $24 . 8 billion annually; Table 1 ) , but that coverage of threatened species would increase only marginally ( Figure S1C ) . Moreover , the majority of species that reach their adequacy targets are those with a geographic range size ≥250 , 000 km2 ( Figure S1C ) , as their wide distribution renders them more easily captured when distributing protected areas equitably across ecoregions . The species most likely to be left unprotected are narrowly distributed species , which often are those in greatest need of protection [31] , [32] . These results indicate that protected area expansion targeting either the cheapest land or representation of ecoregions is not an efficient approach for covering threatened species . Alternatively , we find that locating protected areas to ensure they meet targets for adequate coverage of all 4 , 118 threatened species would cost about $42 . 5 billion annually ( Table 1 ) , which is about 7 . 5 times more than the cheapest option for meeting the 17% target . This difference in cost is driven by low concordance between areas that are cheap to protect and those that capture the distributions of threatened species ( Figure 1D ) . Land selected for threatened species tends to align with tropical forest hotspots ( Figure 1B ) , such as the tropical Andes and eastern Madagascar , whereas the cheapest land to protect is remote and often in more arid zones ( Figure 1D ) . This lack of overlap helps explain why the existing protected area network , which has favored low-cost areas in each country [4] , represents threatened species rather poorly . How can countries reconcile the attraction of low-cost conservation with the benefits of protecting places that contribute to threatened species conservation ? By varying the importance placed on meeting targets for adequate coverage of threatened species , we discover a nonlinear tradeoff between the cost of establishing additional protected areas and the proportion of threatened vertebrates covered by these areas ( Figure 3 ) . The shape of the curve illustrates that large gains in the number of species potentially protected could be achieved for relatively small increases in cost . For instance , increasing by 5-fold the number of species protected relative to the low-cost , business-us-usual scenario would increase opportunity costs to only $7 . 4 billion annually ( 1 . 5 times as much; Table 1 ) . We find that our primary results are robust to randomly simulated commission errors in the range maps . Although the number of species meeting range-based coverage targets generally decreases once commission errors are simulated ( Text S1 ) , this drop averages only 5% across the tradeoff curve ( Figure S2 ) . Moreover , both a visual interpretation and a quantitative measure of the shape of the tradeoff curve reveals that the original and commission error updated curves are similarly nonlinear . Moreover , using high-resolution expert-based habitat suitability models for 1 , 063 threatened mammals , we again find that commission errors are unlikely to alter our primary findings ( Figure S3 ) .
A small minority ( 15% ) of threatened vertebrates are adequately covered by existing protected areas . However , the adoption of the Aichi targets marks an historic opportunity for achieving conservation of the world's biodiversity . If countries are to meet the protected area Aichi target , at least 5 . 8 million km2 of new protected areas will need to be created by 2020 . Although this is a significant opportunity for biodiversity conservation , we have shown that protected area expansion that targets low-cost areas in each country and ignores threatened species is unlikely to protect such species incidentally . This remains the case even if protected areas are further expanded to cover 30% of land areas , or if they are located to cover a representative sample of Earth's terrestrial ecoregions . On the other hand , we find that if protected areas are directed in a cost-efficient manner to protect threatened vertebrates , these species could be protected for an estimated agricultural opportunity cost of about $42 . 5 billion annually . We also find that there is a nonlinear relationship between cost and species protection , indicating that options exist for increasing threatened species protection above the business-as-usual level at little additional cost . Our estimate of the cost of reaching adequacy targets for all threatened birds , mammals , and amphibians is lower than the $58 billion annually estimated for protecting the world's IBAs [5] , though each option comprises a similar land area . There are three primary reasons for this . First , the estimated costs of protecting IBAs include management costs , which are estimated at ∼$7 billion annually [5] . Second , IBAs are identified for their contribution to global bird conservation , without consideration of the cost of protecting these areas , whereas we used an optimization approach to identify low-cost options for meeting conservation targets [33] , [34] . Third , IBAs are identified based on the presence of both threatened and nonthreatened species ( e . g . , congregatory species ) , while we focused on threatened species alone . Our analyses are subject to a number of caveats . First , we considered relative cost based on gross agricultural rents , not management costs or the opportunity costs for other land uses [33] , nor the practicalities of establishing reserves among these competing land uses . Second , overlay of coarse scale maps of species distributions onto fine-scale protected area maps generates commission errors [26] , [35] , though these are unlikely to qualitatively change our results . Still , as commission errors mean that species distributions overlap less than these coarse-scale maps suggest , our estimate of the area needed to protect all threatened species is a minimum [30] . Locations identified here should therefore be considered as broad indications of where specific areas for protection might be located , and our estimates of cost and the area requiring protection will be minima . Third , although we recognize that our analyses have limited taxonomic breadth , no other taxonomic groups ( e . g . , plants ) have undergone comprehensive assessment of both extinction risk and distribution at a sufficiently fine scale for a comparable analysis [10] . Yet good indications exist from the literature that protected areas identified for broad taxonomic groups cover the majority of species in other , nontarget groups [36] , [37] . Finally , our species-specific targets for protection do not account for minimum viable protected areas or connectivity and do not guarantee the long-term survival of all species . Moreover , many species are threatened by processes other than habitat loss and therefore require additional conservation actions both inside and outside protected areas [38] . For the global protected area network to fulfill its potential role as the cornerstone of biodiversity conservation [39] , and for governments to meet their commitments on protected areas and species extinctions , the distribution of threatened species must inform future protected area establishment . Preventing the further loss of all threatened species is a lofty goal and will require substantial efforts . But expanding protected areas requires managing tradeoffs among societal objectives [40] , and here we have shown that considerable increases in protected area coverage of species could be achieved at modest additional cost . Exploiting the nonlinearity of this tradeoff will require directly linking the Aichi targets on protected areas and threatened species ( as well as other targets , including target 5 on slowing habitat loss ) , thereby formalizing the interdependence of these key commitments . | Under the Convention on Biological Diversity ( CBD ) , governments have agreed to ambitious targets for expanding the global protected area network that could drive the greatest surge in new protected areas in history . They have also agreed to arrest the decline of known threatened species . However , existing protected areas perform poorly for coverage of threatened species , with only 15% of threatened vertebrates being adequately represented . Moreover , we find that if future protected area expansion continues in a business-as-usual fashion , threatened species coverage will increase only marginally . This is because low-cost priorities for meeting the CBD targets have little overlap with priorities for threatened species coverage . Here we propose a method for averting this outcome , by linking threatened species coverage to protected area expansion . Our analyses clearly demonstrate that considerable increases in protected area coverage of species could be achieved at minimal additional cost . Exploiting this opportunity will require directly linking the CBD targets on protected areas and threatened species , thereby formalizing the interdependence of these key commitments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"conservation",
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] | 2014 | Targeting Global Protected Area Expansion for Imperiled Biodiversity |
Argonaute proteins are often credited for their cytoplasmic activities in which they function as central mediators of the RNAi platform and microRNA ( miRNA ) -mediated processes . They also facilitate heterochromatin formation and establishment of repressive epigenetic marks in the nucleus of fission yeast and plants . However , the nuclear functions of Ago proteins in mammalian cells remain elusive . In the present study , we combine ChIP-seq ( chromatin immunoprecipitation coupled with massively parallel sequencing ) with biochemical assays to show that nuclear Ago1 directly interacts with RNA Polymerase II and is widely associated with chromosomal loci throughout the genome with preferential enrichment in promoters of transcriptionally active genes . Additional analyses show that nuclear Ago1 regulates the expression of Ago1-bound genes that are implicated in oncogenic pathways including cell cycle progression , growth , and survival . Our findings reveal the first landscape of human Ago1-chromosomal interactions , which may play a role in the oncogenic transcriptional program of cancer cells .
Argonautes ( Ago ) comprise a family of evolutionarily conserved proteins that are central to the RNA interference ( RNAi ) platform and miRNA function [1] , [2] . Ago proteins are often recognized by their cytoplasmic function in which they regulate gene transcripts via post-transcriptional gene silencing ( PTGS ) mechanisms . However , nuclear functions have also been well-characterized in fission yeast and plants in which they assist in mechanisms of transcriptional gene silencing ( TGS ) . In fission yeast , Ago partners with antisense transcripts to form the RITS ( RNA-induced transcriptional silencing ) complex at centromeric regions to induce heterochromatin formation [3] . Similarly , plant Argonautes interact with ribonucleoprotein complexes to induce histone and DNA methylation [4] . In mammals , the nuclear role of Ago proteins ( Ago1–4 ) has remained largely unexplored . There have been scattered examples implicating mammalian Ago members in several nuclear processes including TGS [5]–[8] , gene activation [9]–[11] , and alternative splicing [12] . In the present study , we investigate the nuclear functions of Ago1 and Ago2 – the major facilitators of miRNA activity [13] , [14] – from a global prospective using human cancer cells as a model system . Initial biochemical experiments indicate that nuclear Ago1 selectively interacts with RNA polymerase II ( RNAP II ) . Chromatin immunoprecipitation coupled with massively parallel sequencing ( ChIP-seq ) reveals nuclear Ago1 , but not Ago2 , is pervasively associated with promoters of actively transcribed genes involved in growth , survival , and cell cycle progression . Ago1 knockdown experiments further indicate a positive correlation between Ago1 binding and gene expression . Additional evidence suggests that Ago1-chromosomal interactions may be dependent on miRNA . Our data represents the first landscape of Ago1-chromosomal interactions in human cells and reveals a novel function for Ago1 in modulating gene transcription within the nucleus .
We have previously shown that Ago1 and Ago2 exist in the nuclear fraction of mouse cells [11] . To determine if this feature is conserved in human cells , we examined Ago1 and Ago2 cellular distribution in the nuclear and cytosolic fractions of PC-3 ( prostate adenocarcinoma ) and RWPE-1 ( normal prostatic epithelial ) cells by immunoblot analysis . Nuclear distribution of endogenous Ago1 and Ago2 proteins was readily detectable in both cellular compartments ( Figure 1A , 1B ) . Stable overexpression of exogenous HA-tagged Ago1 ( HA-Ago1 ) or Ago2 ( HA-Ago2 ) in PC-3 was also detected in both nuclear and cytosolic fractions ( Figure 1C ) . Immunofluorescence ( IF ) analysis confirmed that the distribution of Ago1 and Ago2 was evident in both the cytoplasm and nucleus of PC-3 cells expressing HA or GFP-tagged Ago proteins , although signal appeared more prominent in the cytoplasm when observing whole cell distribution ( Figure S1 ) . To determine if nuclear Ago proteins are associated with chromatin , we adopted a fractionation protocol [15] designed to selectively isolate chromatin-bound factors ( Figure 1D ) . Immunoblot analysis revealed that Ago1 and Ago2 were detected in both chromatin fractions ( P1 and S2 ) , as well as present in the Triton X-100 soluble fraction ( S1 ) comprising non-chromatin bound cellular proteins such as tubulin ( Figure 1E ) ; consistent with the canonical functions of Ago proteins in post-transcriptional gene silencing ( PTGS ) mechanisms . RNA polymerase II ( RNAP II ) was also detected and served as a marker for chromatin association ( Figure 1E ) . Taken together , these results suggest that Ago1 and Ago2 are present in the nucleus of human cells in which a subfraction is bound to chromatin . To analyze Ago protein distribution in only the nuclear compartment , we performed IF on isolated nuclei from the HA-Ago1 and HA-Ago2 stable cell lines . As shown in Figure 1F , 1G , Ago1 signals were generally scattered throughout the nuclear interior , whereas Ago2 was predominantly found on the inner nuclear periphery . Negative controls omitting the primary antibody or using cells without HA tag yielded no staining at all ( Figure S2 ) . This data indicates Ago1 and Ago2 have different nuclear localization patterns , which may reflect differences in their nuclear function . Ago proteins have been implicated in regulating transcriptional mechanisms mediated by small RNA duplexes including gene activation and silencing [11] , [16] . To determine if Ago proteins directly interact with transcriptional machinery , we performed immunoprecipitation ( IP ) assays on nuclear extracts from PC-3 cells using antibodies specific to endogenous Ago1 or Ago2 and immunoblotted for RNAP II . As shown in Figure 2A , RNAP II strongly co-precipitated with Ago1 , but not Ago2 . We further performed reciprocal RNAP II IP experiments followed by immunoblotting for Agos as well as TFIIB , a known RNAP II interacting protein , as a positive control ( Figure S3 ) . The result further confirmed RNAP II association with Ago1 , but not Ago2 ( Figure 2B ) . This interaction was also conserved in nuclear extracts from LNCaP ( human prostate adenocarcinoma ) cells ( Figure 2C ) . To address whether the Ago1-RNAP II interaction requires RNA species as intermediates , nuclear extracts were digested with a cocktail of RNase A and T1 ( RNase A/T1 ) prior to IP ( Figure 3A–C ) . RNase A/T1 treatment did not disrupt interactions between Ago1 and RNAP II ( Figure 3A ) . Although it is possible that RNA molecules may have been protected from digestion by Ago1 or its associated protein complex [17] , the data implies Ago1-RNAP II interactions are stable following depletion of nuclear single-stranded RNA species . To determine whether the interactions are DNA dependent , we treated the nuclear extracts with DNase and found that DNase treatment abolished Ago1-RNAP II association ( Figure 3B , 3C ) , suggesting that DNA is required for their interaction . To test if depletion of miRNA and/or components of the miRNA biogenesis pathway alter the Ago1-RNAP II interaction , we transfected PC-3 cells with siRNA designed to specifically knockdown Dicer ( siDicer ) or Drosha ( siDrosha ) ( Figure S4A , S4B ) . Treatment with either siDicer or siDrosha resulted in ≥50% declines in several highly expressed miRNAs implying global downregulation of miRNA maturation ( Figure S4C ) . It should be noted that siDicer and siDrosha treatments also upregulated endogenous protein levels of Ago1 including its nuclear abundance ( Figure 3D–G ) , which may have resulted from a possible compensation mechanism in response to miRNA depletion [18] . Regardless , a moderate decrease in the amount of Ago1-associated RNAP II was observed following Dicer knockdown; the ratio of bound RNAP II to nuclear Ago1 decreased by ∼70% following siDicer treatment ( Figure 3E ) , Mutation to Dicer at exon 5 has been used to generate a stable cell line ( Dicerexon5 ) derived from HCT116 ( colorectal carcinoma ) cells with impaired helicase function that interferes with miRNA maturation [19] . IP experiments revealed that co-immunoprecipitation of RNAP II with Ago1 antibody was reduced in Dicerexon5 cells compared to wild-type ( WT ) controls ( Figure 3H ) , although the protein levels of neither Ago1 nor RNAP II changed in Dicer knockout line compared to its parental cells ( Figure 3H ) . Taken together , these results indicate that Ago1 directly interacts with the core transcription machinery in human cells , which may require Dicer activity and/or the miRNA species it processes . The physical association between Ago1 and RNAP II strongly suggests that Ago proteins may participate in transcriptional gene regulation by interacting with chromatin . Previous studies have demonstrated that Ago proteins programmed with small RNAs can bind to gene bodies or promoters by using chromatin IP ( ChIP ) assays [11] , [12] , [20] . To provide a more global view of nuclear Ago interactions , we mapped Ago1 and Ago2 binding in the genome by ChIP coupled with massively parallel sequencing ( ChIP-seq ) . Antibody validation confirmed that ChIP antibodies for Ago1 and Ago2 had no detectable cross-reactivity ( [21] and Figure S5A–C , Figure 2A ) and are highly specific for RNA-protein IP and ChIP based applications ( [20] and Figure S5D ) . ChIP-seq was also performed for H3K4me3; a histone mark associated with active gene transcription [22] . DNA quality and fragment size distribution for each library was roughly equivalent ( Figure S6A ) . Approximately 80–100 million sequencing reads were obtained from each ChIP-seq library of which ∼80–90% could be uniquely mapped back to the human genome ( Table S1 , S2 ) . To identify Ago1 , Ago2 , and H3K4me3-enriched regions , we applied the CCAT ( control-based ChIP-seq analysis tool ) peak calling algorithm [23] to the raw reads and obtained 110 , 533 Ago1 , 144 Ago2 , and 16 , 729 H3K4me3 peaks ( Table S2 ) . By conservatively setting the false discovery rate ( FDR ) cutoff to 0 . 054 based on independent ChIP validation results ( Figure S6B–D ) , we obtained 44 , 684 Ago1 and 16 , 151 H3K4me3 bona fide peaks ( Table S2 , S3 , S4 ) . None of the Ago2 peaks passed the FDR cutoff ( Table S2 ) ; therefore , we focused our subsequent analyses only on Ago1 . On average , Ago1 peaks were found once in every 70 kb of genomic sequence ( Table S5 ) having a typical size of ∼1 kb , while the size of H3K4me3 peaks were generally broader ( Figure S6E , S6F ) . Ago1 peaks were neither evenly distributed on chromosomes nor on genes; rather , their distribution on chromosomes correlated strongly with gene density ( R2 = 0 . 75 , P<0 . 0001 ) and GC% ( R2 = 0 . 468 , P = 0 . 0001 ) , but not with % repetitive sequences ( R2 = 0 . 034 ) ( Figure 4A and Table S5 ) . For example , the highest Ago1 binding density was seen on gene-dense chromosomes 19 and 17 , while lowest Ago1 binding was on chromosomes Y and 13 , which have the lowest gene density ( Figure 4A , Figure S7 , and Table S5 ) . When multiple regression analysis was applied , gene density becomes the sole determinant of Ago1 binding density on chromosomes ( P<0 . 001 , Table S6 ) . In addition , the majority of Ago1 peaks do not overlap chromosomal “HOT” ( high occupancy transcription-related factors binding ) regions [24] , suggesting that Ago1 peaks we identified are not due to experimental or computational artifacts ( Text S1 ) . Overall , Ago1-bound sequences were largely ( 64 . 9% ) non-repetitive ( Figure 4B ) . Statistical analysis indicated that Ago1 is associated with significantly less ( 35 . 1% ) repetitive elements compared to overall abundance in the genome ( 49% , P = 4 . 9×10−324 ) ( Figure 4B ) . Nonetheless , the major fraction of bound repetitive sequence consisted primarily of SINE , LINE , and LTR transposable elements ( Figure 4B ) . SINE ( 56 . 8% ) , low complexity ( 4 . 1% ) and simple repeat ( 3 . 6% ) elements were overrepresented compared to their respective frequency in the genome , while LINE ( 18 . 2% ) and LTR ( 10 . 2% ) repeats were depleted in Ago1-bound sequences ( Figure 4B ) . Nuclear RNAi has been implicated in transposon regulation in yeast and other eukaryotes by interacting with noncoding transcripts generated from repetitive sequence [25] . It is possible that transposable elements also mediated Ago1 interactions in the nucleus of human cells by a similar manner . Ago1 peaks were also categorized based on gene proximity to include intragenic regions ( i . e . introns , exons , and UTRs ) and adjacent sequences ( i . e . promoters and 3′ flanking region ) within 5 kb of gene bodies . Overall , a majority of the reads corresponded to these genic locations . Compared to their respective composition in the genome , all genic regions were overrepresented in the Ago1 library including promoters , 5′UTRs , exons , introns , 3′UTRs , and 3′ flanking regions by 3 . 61- , 10 . 25- , 4 . 83- , 1 . 1- , 2 . 36- , and 2 . 27-fold , respectively ( Figure 4C ) . In contrast , Ago1 peaks were significantly underrepresented in intergenic regions ( 0 . 41-fold , P = 5×10−324 ) ( Figure 4C ) . Given that Ago1 binding was primarily genic , we evaluated Ago1 peak distribution within ±5 kb of transcription start sites ( TSS ) of annotated genes . We found that the majority of Ago1 peaks mapped to a region within ±1 kb of TSSs in a distribution pattern similar to H3K4me3 peaks ( Figure 4D , 4E ) . In fact , by further stratifying Ago1-bound genes ( AbGs ) for the presence or absence of H3K4me3 at TSSs , we found that within the ±1 kb region , 65 . 2% of Ago1 peaks overlapped with the H3K4me3 mark ( Figure 4D ) . This data implies that Ago1 pervasively associates with chromatin at TSSs of transcriptionally active genes . Select examples of AbGs include PIK3CA , PRKCH , CDC6 , and RRM1 , which have overlapping Ago1 and H3K4me3 peaks proximal to their TSSs ( Figure 4F ) . To determine if RNAP II was also bound to AbGs , we performed ChIP analysis at the promoters of each gene . As shown in Figure 4G , we detected an enrichment of RNAP II as well as Ago1 at each TSS . Collectively , these results indicate Ago1 , H3K4me3 , and RNAP II are present at the promoters of the example AbGs . To evaluate the impact of Ago1 perturbation on AbG expression , we performed microarray analysis in PC-3 cells following Ago1 knockdown with a pool of 3 Ago1-specific siRNAs ( siAgo1 ) ( Figure S8A ) . We identified a total of 3156 Ago1-responsive genes ( ArGs ) including 1592 up- and 1564 downregulated genes defined by >1 . 2-fold change in expression with a P value<0 . 05 ( Table S7 , S8 and Figure S8B ) . Twenty three genes were selected and independently assessed by qRT-PCR to confirm changes in gene expression ( Figure S8C , 8D ) . AbGs identified by ChIP-seq analysis were subsequently correlated to the changes in global gene expression following Ago1 depletion ( Figure 5A ) . The results indicated that 48 . 3% of up- and 55 . 4% of downregulated genes were also bound by Ago1 within 5 kb of their TSSs ( Figure 5A ) and the overlap between AbGs and ArGs are significantly higher than expected by chance ( P = 1 . 4×10−6 , Figure 5B , blue bars ) . However , when we stratified ArGs by up and downregulation , correlation was statistically significant only for downregulated ArGs ( P = 2 . 0×10−7 , Figure 5B , green bars ) and not upregulated ArGs ( red bars , P = 0 . 1 , Figure 5B , red bars ) , suggesting that AbGs are more likely to be downregulated when Ago1 is perturbed . Furthermore , we examined the positional effect of Ago1 binding ( within ±5 kb distance ) on changes in gene expression in response to Ago1 perturbation . To this end , we calculated the correlation between changes in gene expression and Ago1 binding events on the same gene for each location within −5 kb∼+5 kb region shifting one basepair each time . In consistent with the overall correlation analysis ( Figure 5B ) , Ago1 binding events have a better correlation with down- ( Figure 5C , green line ) than upregulated ( Figure 5C , red line ) ArGs . The closer Ago1 binding was to the proximal promoter region , the greater the statistical significance was for enrichment of ArGs , especially for downregulated ArGs , with the enrichment for downregulated ArGs peaked at +111 location ( P = 1 . 0×10−8 ) and upregulated ArGs at −135 location ( P = 0 . 002 ) ( Figure 5C ) . Taken together , the correlation between AbGs and downregulated ArGs suggests that Ago1 plays a positive role in maintaining transcription of a subset of genes . It is important to note that our data does not rule out the possibility Ago1 may also be functioning to suppress gene expression through promoter interactions for certain genes . miRNAs have been shown to regulate gene transcription by binding to promoter sequences in an Ago-dependent manner [7] , [11] , [16] , [26] . Since Ago proteins do not possess a known DNA binding domain based on protein sequence and structural analysis [27] , [28] , Ago1-chromosomal interactions might be mediated by miRNAs . As such , we performed miRNA target prediction analysis on the Ago1-bound DNA sequences identified by ChIP-seq . Compared to random selected matched control sequences , the frequency of putative target sites in Ago1-bound peaks were roughly equivalent for most miRNAs ( Figure 6A , 6B ) . However , a total of 49 miRNAs were found to have a statistically higher number of target sites in the Ago1-bound peaks compared to the control sequences ( >1 . 5 fold enrichment , P = 0∼6×10−41 ) , while only 3 miRNAs , function of which is unknown , have higher number of targets in the control sequences ( Figure 6A , 6B and Table S9 ) . Interestingly , approximately one third of the 49 miRNAs are known oncomiRs including those from the miR-17-92 and miR-106b-25 clusters , as well as the miR-520/373 family ( Figure 6B , Table S9 ) . We also preformed motif analysis on each miRNA with enriched target sites in Ago1-bound sequences . A common motif “AGUGCU/A” was found in 19 out the 49 miRNAs; 7 of which contained two incidences of this motif ( Figure 6C , 6D ) . Interestingly , a similar motif ( AGUGUU ) was identified in the 3′terminus of miR-29b , which functions as a nucleic acid-based nuclear localization signal ( NLS ) [29] . Although the significance of our motif in context to Ago1-bound sequences is unknown , it shares ∼83% homology with the miR-29b NLS ( Figure 6C ) . As certain miRNAs are known to preferably accumulate in the nucleus [30] , the identification of putative target sites at Ago1-bound peaks supports the idea that such miRNAs may play a role in directing Ago1-chromomal interactions . To test the regulatory effect of Ago1 binding on gene promoters , we depleted Ago1 in PC-3 cells using siAgo1 and evaluated its effect on 4 ArGs ( i . e . SMC1A , CDC20 , SMAD3 and BUB1 ) with overlapping Ago1 and H3K4me3 peaks at their TSSs ( Figure 7A ) . ChIP analysis revealed reductions in bound Ago1 at the promoters for each gene ( Figure 7B ) . Moreover , knockdown of Ago1 reduced RNAP II occupancy at TSSs ( Figure 7C ) with corresponding decreases in gene expression levels ( Figure 7D ) . We also generated stable cell lines in RWPE-1 ( non-malignant prostate epithelium ) cells overexpressing Ago1 or a deletion mutant lacking the PAZ domain ( Ago1 dPAZ ) ( Figure 8A ) , which is known to interfere with efficient miRNA loading into Ago proteins [31] . Ago1 overexpression resulted in a moderate induction of each gene ( Figure 8B ) , while PAZ deletion attenuated this response ( Figure 8B ) , further supporting a role for miRNAs in directing Ago1-chromosomal interactions . Furthermore , we performed Ago1 ChIP for the 4 example genes and were able to detect in Ago1 overexpressing RWPE-1 cells a concurrent increase in Ago1 binding at the same sites near TSSs detected in PC-3 cells ( Figure 8C ) . Collectively , these results suggest Ago1 contributes to positive gene regulation of select ArGs by interacting with gene promoters and stimulating RNAP II enrichment . Three overlapping AbG sets were defined to include AbGs-5 kb , -1 kb and -0 . 5 kb , which consist of genes with at least one Ago1 peak within ±5 , ±1 , and ±0 . 5 kb away from TSSs , respectively . AbGs-5 kb , -1 kb and -0 . 5 kb respectively contain 15503 , 10074 , and 8057 unique genes encompassing 27 . 5% , 17 . 9% , and 14 . 3% of all annotated genes in Ensembl human genome database ( Table S10 , S11 , S12 ) . Interestingly , clustering AbGs-5 kb , -1 kb or -0 . 5 kb genes by their chromosomal location reveal several cytobands implicated in different human cancers that are highly overrepresented ( Figure S9 , Table S13 , S14 ) . For example , the top-enriched cytobands 19p13 . 3 and 16p13 . 3 have been established by numerous studies to be susceptibility loci for several types of cancers including prostate , breast , thyroid , and lymphoma [32]–[35] . Gene pathway enrichment analysis further revealed a number of oncogenic pathways overrepresented by AbGs . The top 5 KEGG pathways highly enriched in AbGs-5 kb genes include “pathways in cancer” ( P = 1 . 9×10−10 ) , “MAPK signaling” ( P = 1 . 7×10−8 ) , “Wnt signing” ( P = 1 . 1×10−7 ) , “endocytosis” ( P = 4 . 3×10−7 ) , and “focal adhesion” ( P = 6×10−7 ) ( Figure 9A ) . Many proto-oncogenes and proliferation-promoting genes are exemplified in these pathways including growth factors , tyrosine/serine/threonine kinases , G-protein coupled receptors , membrane-associated G-proteins , and nuclear DNA-binding/transcription factors ( Table S15 ) . These enrichments hold when AbGs are narrowed down to AbG-1 kb and AbG-0 . 5 kb genes ( Figure S10A , S10B ) . For instance , SMC1A , CDC20 , SMAD3 and BUB1 are all example AbG-0 . 5 kb genes known to promote cell cycle progression and proliferation in various cancer cell types [36]–[39] . Gene Ontology ( GO ) classification of AbGs-5 kb genes also show enrichment for gene categories that regulate metabolic processes , transcription , cell cycle , chromatin modification , and cell death ( Figure S10C ) . KEGG pathway enrichment analysis also revealed that ArGs shared several cancer-related pathways with AbGs-5 kb genes including “MAPK signaling” , “p53 signaling” , “cell cycle” , “prostate cancer” , “colorectal cancer” , etc . ( Figure 9B ) . Further GO analysis revealed that up- and downregulated ArGs were enriched in distinct biological processes with the latter significantly overrepresented by processes important for cancer growth/development including cell cycle , mitosis , DNA repair , chromosome organization , etc . ( Figure S11A , S11B ) . Analysis of Ago1 protein levels in non-tumorigenic ( RWPE-1 and PWR-1E ) and cancerous ( PC-3 , DU145 , LNCaP , RV1 , CWR22R , and C4-2 ) prostate cell lines indicated Ago1 is generally expressed at significantly higher levels in cancer cell lines ( Figure S12A ) . Furthermore , knockdown of Ago1 in PC-3 cells caused G0/G1 arrest as indicated by the increase in G0/G1 cell number and corresponding reductions in S and G2/M populations ( Figure S12B , S12C ) . Our data suggests Ago1 may be involved in oncogenic processes , in part , through its nuclear activity by affecting the expression of genes involved in cell growth/survival . In support , integrated analysis of ChIP-seq and gene expression profiling places Ago1 in various major cancer-related signaling pathways involved in regulating DNA damage response , mitogenic signaling , cell cycle , angiogenesis , and apoptosis ( Figure 9C ) .
It has become clear that Ago proteins participate in gene regulation at multiple levels . In the present study , we reveal another layer to Ago1 in regulating gene expression within the nucleus of human cancer cells . We provide biochemical evidence that nuclear Ago1 , but not Ago2 , directly associates with RNAP II . ChIP-seq analysis indicates Ago1 is pervasively bound to multiple genomic loci including repetitive elements of transposons and euchromatic sites as defined by the histone mark H3K4me3 . Interestingly , this observation is consistent with the chromosomal binding profiles of drosophila Ago2 ( dAGO2 ) ; the primary Argonaute for mediating RNAi and miRNA function in the fly [15] , [40] . Additionally , Ago1 binding at gene promoters functionally impacts active gene transcription as its loss of function results in reduced Ago1 and RNAP II occupancy at TSSs with corresponding reductions in gene expression , whereas gain of function causes the opposite changes . Our data represents the first landscape of Ago1-chromosomal interactions in human cancer cells , while revealing a novel non-canonical function for Ago1 in regulating gene expression . It is currently unclear how Ago1 is targeted to selected chromosomal loci . Our analyses imply that miRNA may be involved in mediating interactions between nuclear Ago1 , chromatin , and/or RNAP II . Ago1-bound sequences contained putative miRNA target sites and its binding activity to RNAP II was suppressed by perturbing Dicer function; an essential protein involved in miRNA maturation . Additionally , deletion of the RNA-binding domain ( PAZ ) in Ago1 interfered with gene activation further implicating a role for RNA ( i . e . miRNA ) in this process . In support , it has been reported that transfection of exogenous miRNA can promote enrichment of Ago proteins at highly-complementary sites in gene promoters to manipulate transcription [7] , [11] , [16] . Depletion of nuclear single-stranded RNAs by RNase A/T1 did not interfere with Ago1-RNAP II association; however , Ago1 may be loaded with miRNA forming a duplex with complementary target sequence and protecting bound RNA from RNase A/T1 digestion in manner similar to canonical target recognition [17] , [41] . As we have not definitely confirmed the presence of miRNAs in these nuclear Ago1 complexes , it is also possible other classes of small RNA species mediate Ago1 interactions with chromatin . For instance , recent deep sequencing studies have shown that Ago1 can associate with small RNA species from non-miRNA sources [42] , [43] . In contrast to Ago1 , Ago2 apparently lacked pervasive association with chromatin . Additionally , it did not immunoprecipitate with basal transcription machinery ( i . e . RNAP II ) . Although we cannot absolutely rule out technical reasons for the lack of Ago2 binding , the difference in binding may be reflective of their differential nuclear distribution as revealed by IF microscopy ( Figure 1E ) . Ago1 and Ago2 have been reported to exhibit intrinsic preferences when selecting and/or loading RNA molecules . For instance , studies have shown that Ago2 binds perfect-complementary RNA duplexes ( e . g . siRNAs ) with higher affinity than Ago1; whereas , Ago1 preferably associates with duplexes containing bulges and mismatched bases ( e . g . miRNA ) [44] , [45] . This intrinsic segregation in RNA binding may also be a key determinant in mediating Ago interactions in the nucleus . Alternatively , nuclear Ago2 may be sequestered to the nuclear envelope and only associate with chromatin in a signal-dependent manner . In support , cellular senescence has been shown to trigger nuclear accumulation of Ago2 and binding at gene promoters [8] . It is noteworthy that the magnitudes of gene expression changes for a vast majority of genes in response to Ago1 perturbation were less than two-fold . This observation is consistent with post-transcriptional gene regulation by miRNA [46] and suggests that the role of Ago1 is fine-tuning gene expression in a miRNA dependent manner both at the transcriptional and post-transcriptional levels . The short term ( 48 hrs ) transfection of Ago1 siRNA may also be accounted for the subtle changes in gene expression . We chose this duration to minimize detecting potential secondary regulation but at the same time we might have missed the maximum responsiveness of gene expression to Ago1 perturbation . In the cytoplasm , Ago proteins elicit pleiotropic effects on gene expression by utilizing miRNA to silence multiple transcripts and regulate various cellular processes [1] , [2] . Similarly , nuclear Ago1 also possesses pleiotropy by affecting transcription of multiple genes . In PC-3 cells , Ago1 appeared to preferably drive the expression of genes involved in oncogenic pathways suggesting it may play a role in the cancer phenotype . In support , knockdown of Ago1 by siRNA inhibited cell cycle progression . However , its effects on cancer may be context dependent and vary between different cell types based on both its cytosolic and nuclear activities , as well as the gene profile it regulates . It would be of future interests to understand the crosstalk between Ago-mediated gene regulatory networks and oncogenic signaling pathways .
PC-3 , LNCaP , DU145 , LAPC4 , RV1 , CWR22R , C4-2 , and HCT116 cell lines ( ATCC ) were maintained in RPMI-1640 media ( UCSF Cell Culture Core ) supplemented with 10% fetal bovine serum ( Hyclone ) , penicillin G ( 100 U/mL ) , streptomycin ( 100 µg/mL ) in a humidified atmosphere of 5% CO2 at 37°C . RWPE-1 and PWR-1E cells were cultured in serum-free keratinocyte medium supplemented with 5 ng/ml human recombinant epidermal growth factor and 0 . 05 mg/ml bovine pituitary extract . Vectors pIRESneo-FLAG/HA-Ago1 ( Addgene #10820 ) and pIRESneo-FLAG/HA-Ago2 ( Addgene #10822 ) were used to establish stable cell lines overexpressing HA-tagged Ago1 ( PC3-HA-Ago1 ) and Ago2 ( PC3-HA-Ago2 ) , respectively . Briefly , PC-3 cells were transfected with each corresponding vector and single colonies were subcultured following selection with G418 . GFP-Ago1 ( Addgene #21534 ) and GFP-Ago2 ( Addgene #11590 ) plasmids were transiently transfected into PC-3 cells and imaged by fluorescence microscopy . Full-length human Ago1 and the PAZ deletion mutant ( Ago1 dPAZ ) were amplified from pIRESneo-FLAG/HA-Ago1 and pIRESneo-FLAG/HA-Ago1dPAZ , respectively . Each amplicon was cloned into the lentiviral cDNA expression vector pCDH-EF1-MCS-T2A-copGFP ( System Biosciences ) via EcoRI and BamHI restriction sites . For lentivirus mediated overexpression , lentivirus particles were generated by the ViraPower Lentiviral Expression System ( Invitrogen ) and used to infect RWPE-1 cells to generate stable cell lines . Expression of all constructs was confirmed by immunoblot analysis . PC-3 cells were seeded on coverslips at 50% confluency . The following day , cells were washed with PBS and fixed in 4% paraformaldehyde at room temperature for 15 min . Cells were permeabilized in PBS containing 0 . 3% Triton-X-100 for 10 min , rinsed with PBS , and blocked with 10% goat serum at room temperature for 1 hr . Coverslips were incubated with primary antibodies anti-HA ( Cell Signaling , cat # 2367 , 1∶200 ) or anti-Ago2 ( Wako , cat # 011-22033 , 1∶200 ) diluted in 10% goat serum at room temperature for 1 hr . Cells were washed with PBS and subsequently treated with anti-mouse FITC antibody ( Vector Lab; 1∶200 ) at room temperature for 1 hr . Coverslips were washed and mounted with mounting media containing DAPI . IF images were captured using a Zeiss AxioImager M1 fluorescence microscope . Purified nuclei for IF analysis were isolated as previously described [47] . Nuclei were fixed on slides with fixative reagent ( methanol: acetic acid , v/v 3∶1 ) at room temperature for 5 min and washed with 4×SSC containing 0 . 1% Tween 20 . The slides were subsequently incubated with anti-Ago1 ( Santa Cruz Biotechnology , cat #sc-32657 , 1∶200 ) , anti-Ago2 ( Wako , cat #011-22033 , 1∶200 ) , or anti-HA ( Cell Signaling , cat #2367 , 1∶200 ) diluted in dilution buffer ( 1% bovine serum albumin , 4×SSC , and 0 . 1% Tween 20 ) at 4°C overnight . Nuclei were washed and incubated with the appropriate Alexa Fluor® 488 secondary antibodies ( Molecular Probes; 1∶200 ) for 30 minutes at 37°C . Following a series of washes , slides were mounted with DAPI II ( Abbot Molecular ) and IF signals were analyzed using the CytoVision imaging system ( Applied Imaging ) . Chromatin fractionation was performed as previously described [15] . Cell pellets were collected from two 150 mm plates and washed with PBS . Approximately 1/10th of the cell pellet was resuspended in RIPA buffer ( 50 mM Tris , pH 7 . 4 , 150 mM NaCl , 1% Triton ×-100 , 0 . 5% deoxycholate , 0 . 1% SDS , protease inhibitor cocktail , and phosphatase inhibitor ) and incubated on ice for 30 min to generate whole cell lysate . The remaining pellet was lysed in cold CSKI buffer [10 mM PIPES , pH 6 . 8 , 100 mM NaCl , 1 mM EDTA , 300 mM sucrose , 1 mM MgCl2 , 1 mM DTT , 0 . 5% ( v/v ) Triton X-100 , and protease inhibitor cocktail ( Roche ) ] . The lysate was divided into two equal portions and centrifuged at 500×g for 3 min at 4°C . The resulting supernatant was collect and referred to as the S1 fraction . One pellet was washed twice in CSKI buffer and resuspended in RIPA buffer to generate the P1 fraction . The other pellet was resuspended in CSKII buffer [10 mM PIPES , pH 6 . 8 , 50 mM NaCl , 300 mM sucrose , 6 mM MgCl2 , 1 mM DTT , and protease inhibitor cocktail ( Roche ) ] and treated with DNase ( Qiagen ) for 30 min . The resulting sample was extracted with 250 mM NH2SO4 for 10 min at room temperature and centrifuged at 1200×g for 6 min at 4°C to generate the S2 ( supernatant ) and P2 fractions ( pellet ) . The P2 fraction was subsequently resuspended in RIPA buffer . Cytoplasmic and nuclear fractions were prepared by using the NE-PER Nuclear and Cytoplasmic Extraction Reagents ( Thermo Scientific ) . Whole cell lysate was obtained by lysing cells in RIPA buffer for 15 minutes at 4°C . Lysates were clarified by centrifugation for 15 minutes at 14 , 000 rpm and supernatants were collected . 30 µg of protein from all fractions was analyzed by immunoblot analysis . Immunoprecipitation was performed according to Cernilogar et al . [15] . Approximately 400–800 µg of protein from nuclear extracts was mixed with equal volumes immunoprecipitation buffer [10 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 1 mM DTT , 0 . 1% NP-40 , and protease inhibitor cocktail ( Roche ) ] . In Figure 3A–3C , nuclear extract was treated with 2 . 5 ul of RNase A/T ( Ambion ) cocktail for 30 min at 25°C or 100 ng/uL of DNAse I ( Roche ) for 20 min at 37°C . Each sample was subsequently treated with 5 µg of antibody and incubated overnight at 4°C . Antibody treatments included anti-Ago1 ( Wako , clone 2A7 , cat# 015-22411 ) , anti-Ago2 ( Wako , clone 4G8 , cat# 011-22033 ) , anti-RNAP II ( Millipore , cat# 05-623 ) , or mouse IgG ( Millipore , cat# 12-371 ) . The following day , 40 µl protein G dynabeads were added to each sample and rotated for 2 hrs at 4°C . The beads were subsequently washed five times with 500 µl immunoprecipitation buffer and resuspended in SDS-PAGE sample buffer . Immunoprecipitates were boiled for 5 min and the resulting supernatants were analyzed by immunoblot analysis . Sample protein concentration was determined by BCA protein assay ( Thermo Scientific ) . Equal amounts of protein were resolved by SDS-PAGE and transferred to 0 . 45 µm nitrocellulose membranes by voltage gradient . The resulting blots were blocked overnight in 5% nonfat dry milk and subsequently probed with primary antibody . The antibodies were used at the indicated dilutions: anti-Ago1 ( Cell Signaling . cat #5053 ) at 1∶1000 , anti-Ago2 ( Wako , cat# 011-22033 ) at 1∶1000 , anti-HA 6E2 ( Cell Signaling , cat # 2367 ) at 1∶1000 , anti-Tubulin ( Sigma , cat # T6074 ) at 1∶1000 , anti-Topoisomerase I ( Santa Cruz , cat #sc-10783 ) at 1∶500 , anti-RNAP II ( Millipore , cat # 05-623 ) at 1∶5000 , anti-Dicer ( Santa Cruz , cat #sc-30226 ) at 1∶1000 , anti-Drosha ( Cell Signaling , cat #3364 ) at 1∶1000 , and anti-TFIIB ( Cell Signaling , cat #4169 ) at 1∶1000 . Immunodetection occurred by incubating blots with appropriate secondary HRP-linked antibodies and utilizing the SuperSignal West Pico Chemiluminescent kit ( Thermo Scientific ) to visualize antigen-antibody complexes . All siRNAs were designed using the BLOCK-iT RNAi Designer Program ( Invitrogen ) . Ago1 knockdown was accomplished by using a pool of 3 siRNAs , while single duplexes were used to knockdown Dicer or Drosha . A pool of 3 non-specific siRNAs served as controls . Transfections were carried out using Lipofectamine RNAiMax ( Invitrogen ) according to the manufacturer's instructions . All siRNA sequences are listed in Table S16 . Total RNA was isolated by using the RNeasy Mini Kit ( Qiagen ) . ∼1 µg of total RNA was reverse transcribed into cDNA with MMLV reverse transcriptase ( Promega ) in conjunction with oligo ( dT ) primers . The resulting cDNA samples were subjected to real-time PCR analysis using gene-specific primers . All primer sequences are listed in Table S16 . Chromatin immunoprecipitation ( ChIP ) was performed as previously described with slight modification [11] . Chromatin was prepared from PC-3 cells following crosslinking with formaldehyde . DNA was sheared to an average size of ∼500 bp using the Bioruptor sonicator ( Diagenode ) set to ‘high’ with 30 sec ON/OFF pulses for 8 min for a total of 8 cycles . Chromatin was immunoprecipitated overnight at 4°C using 5 µg of the following antibodies: anti-Ago1 ( Wako , clone 2A7 ) , anti-Ago2 ( Wako , clone 2D4 ) , anti-H3K4me3 ( Millipore , cat# 07-473 ) , and mouse IgG ( Millipore , cat# 12-371 ) . The following day , the samples were incubated with 25 µl Protein G Dynabeads ( Invitrogen ) for 2 hrs at 4°C . Immunoprecipitates were sequentially washed with low salt , high salt , and TE buffer . Eluates were collected and reverse crosslinked at 65°C overnight . ChIP DNA was treated with Proteinase K , purified with phenol/chloroform , treated with RNase A , and purified using the Qiaquick PCR purification kit ( Qiagen ) . Target amplification and detection was performed by the 7500 Fast Real-Time System ( Applied Biosystems ) . All reactions were prepared in 10 µl volumes containing 2 µl DNA , 2× Fast SYBR Green master mix ( Applied Biosystems ) , and region-specific primer sets ( Table S16 ) . Each sample was analyzed in triplicate . Enrichment was determined by using the 2−ΔCT method relative to input DNA or IgG control . Primer specificity was confirmed by evaluating dissociation curves and independently analyzing amplified product on an agarose gel . For Ago ChIP-western analysis , IP was performed essentially the same way as above and the beads were resuspended in 2× SDS sample buffer and boiled for 5 min . Supernatant was collected and analyzed by western blotting analysis . Each library was prepared by combining the eluates from two ChIP experiments and following the Illumina ChIP-seq library preparation protocol . Briefly , ∼10 ng DNA was end-repaired and subsequently labeled with an additional “A” base on the 3′ ends of the DNA fragments . The resulting DNA samples were ligated to oligonucleotide adaptors and amplified by PCR to construct the individual libraries . Each library was size-selected for DNA fragments ranging between ∼200–300 bp by gel electrophoresis purification . Sample quality was assessed on a Bioanalyzer ( Agilent ) using the Hypersensitive DNA kit ( Agilent ) prior to sequencing . Libraries were diluted to 10 nM and sent to the Vincent J . Coates Genomics Sequencing Laboratory at UC Berkeley ( http://qb3 . berkeley . edu/gsl ) for sequencing analysis on a Hiseq2000 Sequencing System ( Illumina ) . Additional detail on ChIP-seq is available in Text S2 . The ChIP-seq and microarray data from this study have been deposited into the GEO database under the accession numbers GSE40536 and GSE42600 . Other experimental procedures are available in Text S2 . | Argonaute ( Ago ) proteins are an evolutionarily conserved family of proteins indispensable for a gene regulation mechanism known as RNA interference ( RNAi ) which is mediated by small RNA including microRNA ( miRNA ) and small interfering RNA ( siRNA ) and occurs mainly in the cytoplasm . In mammalian cells , however , the function of Agos in the nucleus is largely unknown despite a few examples in which Agos are shown to be involved in regulating gene transcription and alternative splicing . In this study , by taking a genome-wide approach , we found that human Ago1 , but not Ago2 , is pervasively associated with gene regulatory sequences known as promoter and interacts with the core component of the gene transcription machinery to exert positive impact on gene expression in cancer cells . Strikingly , the genes bound and regulated by Ago1 are mostly genes that stimulate cell growth and survival , and are known to be involved in the development of cancer . The findings from our study unveil an unexpected role of nuclear Ago1 in regulating gene expression which may be important both in normal cellular processes and in disease such as cancer . | [
"Abstract",
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"Results",
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"and",
"Methods"
] | [] | 2013 | Ago1 Interacts with RNA Polymerase II and Binds to the Promoters of Actively Transcribed Genes in Human Cancer Cells |
The role of actin dynamics in clathrin-mediated endocytosis in mammalian cells is unclear . In this study , we define the role of actin cytoskeleton in Kaposi's sarcoma-associated herpesvirus ( KSHV ) entry and trafficking in endothelial cells using an immunofluorescence-based assay to visualize viral capsids and the associated cellular components . In contrast to infectivity or reporter assays , this method does not rely on the expression of any viral and reporter genes , but instead directly tracks the accumulation of individual viral particles at the nuclear membrane as an indicator of successful viral entry and trafficking in cells . Inhibitors of endosomal acidification reduced both the percentage of nuclei with viral particles and the total number of viral particles docking at the perinuclear region , indicating endocytosis , rather than plasma membrane fusion , as the primary route for KSHV entry into endothelial cells . Accordingly , a viral envelope protein was only detected on internalized KSHV particles at the early but not late stage of infection . Inhibitors of clathrin- but not caveolae/lipid raft-mediated endocytosis blocked KSHV entry , indicating that clathrin-mediated endocytosis is the major route of KSHV entry into endothelial cells . KSHV particles were colocalized not only with markers of early and recycling endosomes , and lysosomes , but also with actin filaments at the early time points of infection . Consistent with these observations , transferrin , which enters cells by clathrin-mediated endocytosis , was found to be associated with actin filaments together with early and recycling endosomes , and to a lesser degree , with late endosomes and lysosomes . KSHV infection induced dynamic actin cytoskeleton rearrangements . Disruption of the actin cytoskeleton and inhibition of regulators of actin nucleation such as Rho GTPases and Arp2/3 complex profoundly blocked KSHV entry and trafficking . Together , these results indicate an important role for actin dynamics in the internalization and endosomal sorting/trafficking of KSHV and clathrin-mediated endocytosis in endothelial cells .
Endocytosis is a constitutive cellular process that results in the internalization of cell surface receptors and ligands , and membrane components , often initiating the activation of signal transduction cascades [1] . The endocytic pathway is often exploited by a variety of pathogens to gain entry into the cells [2] . The best-described endocytic pathway is clathrin-mediated endocytosis [3] . In this process , the clathrin-coated pits assemble at the plasma membrane and acquire cargo . The plasma membrane proceeds to invaginate and constrict to generate a clathrin-coated vesicle , which is subsequently transported to the interior of the cell , where it loses its clathrin coat and fuses with the early endosome [3] . The orderly transport of endocytic cargo from the cell exterior to the interior is highly regulated , and requires the participation of numerous lipid components and accessory proteins , as well as alterations of fine cellular structures and controlled mechanical force to overcome the physical resistance and propel the vesicle into the cell [4] . The actin cytoskeleton has been proposed to participate in either a structural role in clathrin-mediated endocytosis , or by providing the mechanical force necessary to complete endocytosis [5] , [6] . The evidence for a role of actin in this process primarily comes from studies of yeast , in which actin dynamic assembly and disassembly are essential for endocytosis [7]–[9] . However , the role of actin in endocytosis in mammalian cells is less clear [5] , [10]–[12] . Studies have shown a close association between components of the endocytic machinery and actin cytoskeleton [13] while regulators of actin polymerization such as Arp2/3 and neural Wiskott-Aldrich syndrome protein ( N-WASP ) are found to be recruited to clathrin-coated vesicles during endocytosis [14] . However , chemical disruption of actin dynamics has resulted in only partial inhibition of endocytosis in mammalian cells [10]–[12] , [15] . Since these studies analyzed endocytosis in the entire population of cells , it is possible that the results may have been confounded by the use of an alternate non-clathrin-dependent pathway , or the requirement for actin in only specific subsets of clathrin-coated vesicles [16] . In addition , since mammalian cells use actin to maintain plasma membrane tension , reduced plasma membrane tension caused by actin disruption may actually enhance invaginations during the initiation of endocytosis [17]–[19] . Endocytosis occurring at the apical surface of MDCK cells seems to be actin-dependent , while basolateral endocytosis does not appear to require an intact actin network [15] . Internalization of influenza virus by epithelial cells requires an intact actin cytoskeleton at the apical surface , while viral entry at the basolateral surface did not [20] . More recent studies using total internal reflection fluorescence microscopy ( TIR-FM ) technology to observe individual endocytic events recorded a burst of actin polymerization with concomitant internalization of clathrin-coated pits and movement of clathrin-coated vesicles into the cell interior , but did not determine the potential effects of actin disruption on these events [16] , [21] , [22] . The subcortical actin network that lies immediately beneath the plasma membrane is tethered to the membrane via linking molecules such as ezrin [23] and cortactin [24] , and may participate in endocytosis by providing the mechanical force to deform the plasma membrane , allowing invagination and scission of clathrin-coated pits and/or caveolae pits [5] . Actin may also participate in later steps of the endocytic pathway by facilitating the movement of endosomes towards the cell interior [5] , [16] . However , the exact role of the actin cytoskeleton in endosomal sorting and trafficking remains largely unknown . The dynamic nature of the actin cytoskeleton is essential for its function; existing actin filaments undergo severing and depolymerization in response to cellular requirements and stimuli [25] , [26] while new actin filaments are polymerized from monomeric actin subunits and by branching off from existing filaments [27] . These processes are regulated by Rho GTPases , WASP/N-WASP/WAVE , the ARP2/3 complex , and the formin family of actin-binding proteins [27]–[29] . Studies of clathrin-mediated endocytosis typically utilize fluorescently labeled molecules such as transferrin to assess the role of various accessory and regulatory proteins in endocytosis . Although transferrin and its receptor are well-known to be internalized via clathrin-mediated endocytosis , they primarily enter the recycling endosomal pathway [30]–[34] , and thus , may not be entirely relevant to molecules that progress to other endosomal pathways , such as the lysosomal pathway . Several microbial pathogens exploit the endocytic pathway to gain access into the cell interior [2] , and some such as Listeria monocytogenes are also known to specifically manipulate the actin cytoskeleton to travel through the cell [35]–[37] . However , viruses , which are inert particles that rely on existing cellular pathways to enter the target cell , make ideal candidates to study the endocytic processes . Enveloped viruses such as Herpesviruses enter cells either by direct fusion between viral envelope and the plasma membrane or by pH-dependent fusion between the viral envelope with the endosomal membrane , followed by trafficking to the nucleus to initiate infection [38] , [39] . The route of viral entry can differ between cell types [40]–[42] . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a γ2-herpesvirus implicated as the causative agent of Kaposi's sarcoma ( KS ) and primary effusion lymphoma ( PEL ) . KS lesions are primarily composed of KSHV-infected spindle cells with endothelial markers , suggesting that the in vivo targets of KSHV might be endothelial cells [43] . Previous studies have shown that KSHV infects fibroblasts via clathrin-mediated endocytosis , and that microtubules are not required for virus internalization but required for the trafficking of viral particles [44] , [45] . In addition , KSHV entry induces RhoA GTPase , and rearrangements of both microtubules and the actin cytoskeleton in fibroblasts [46] , and RhoA GTPase is important for virus entry in HEK293 cells [47] and association of viral particles with microtubules in dermal microvascular cells ( DMVEC ) [48] . Nevertheless , in these studies the actin cytoskeleton was found to be involved in neither virus internalization nor virus trafficking though others have shown a potential role for the actin cytoskeleton in clathrin-mediated endocytosis [5] , [13] , [14] , [16] . It is possible that the requirement for actin dynamics during endocytosis may differ considerably among different cell types . In addition , KSHV entry and trafficking , as well as the role of actin cytoskeleton in clathrin-mediated endocytosis in endothelial cells , have not been extensively examined so far . To better understand the potential role of the actin cytoskeleton in KSHV infection in endothelial cells , we have used an immunofluorescence-based assay ( IFA ) to visualize viral capsids during KSHV entry and trafficking in the target cells . This assay does not rely on the expression of any viral genes , but instead directly visualizes the accumulation of viral capsids at the nuclear membrane as an indicator of successful viral entry and trafficking . To distinguish between plasma membrane fusion and endocytosis , cells were treated with inhibitors of endosomal acidification before and during infection . In addition , cells were examined for the presence of enveloped viral particles at the early stage of KSHV infection . The results suggested endocytosis as the primary route of entry used by KSHV to infect primary human umbilical vein endothelial cells ( HUVEC ) . Additional studies indicated that clathrin-mediated endocytosis is the major route of KSHV entry into endothelial cells , and KSHV infection induces dynamic rearrangement of actin cytoskeleton . Both KSHV viral particles and transferrin were observed to colocalize with actin filaments and markers of endosomal maturation . Disruption of the actin cytoskeleton and inhibition of regulators of actin nucleation such as Rho GTPases and Arp2/3 complex with chemical agents profoundly reduced viral entry and trafficking , suggesting that actin dynamics might play an important role in multiple endosomal steps during KSHV infection and clathrin-mediated endocytosis in endothelial cells .
To study the mechanism used by KSHV to infect endothelial cells , an assay to analyze the entry and trafficking of individual viral particles was developed . KSHV is an enveloped virus , with a cell-derived membrane enclosing a tegument layer and a central capsid containing the viral genome [43] . For herpesviruses that enter the cells via fusion of the viral envelope at the plasma membrane , the viral envelope glycoproteins are undesirable for monitoring virus entry and trafficking [38] , [39] . The fate of the poorly-characterized tegument during KSHV entry and trafficking is also unclear [43] . Thus , the component of the virion best suited for visualizing the entry of a viral particle into cells and subsequent intracellular trafficking is the viral capsid . The viral capsid remains intact throughout the infection process and carries the viral genome to the host nucleus . We used a monoclonal antibody directed against the KSHV small capsid protein Orf65 to stain the viral capsid in an IFA . HUVEC inoculated with KSHV were processed for IFA at various time points post-infection to visualize the Orf65+ capsids , actin filaments , and the cell nucleus . Z-stacks were acquired with a confocal laser scanning microscope and used to generate 3D projection XY overview images ( Figure 1A and Video S1 ) and the corresponding cross-sectional YZ images ( Figure 1B and Video S2 ) . Images were further magnified to show the nucleus and individual viral particles ( Figure 1C and D , and Videos S3 and S4 ) . Within 4 h post-infection ( hpi ) , we observed a large number of viral capsids in the range of 10–70 particles per cell at the perinuclear region . We quantified the total number of viral particles reaching the perinuclear region as an indication of successful viral entry and trafficking . The entry of enveloped viruses such as herpesviruses can occur by two different routes: fusion of the viral envelope with the plasma membrane followed by delivery of the viral particles into the host cytoplasm , or by attachment to a cellular receptor ( s ) and subsequent endocytosis of the receptor proteins and viral particles [39] . The plasma membrane fusion route is pH-independent , while viral entry via endocytosis is pH-dependent , and most of enveloped viruses require low pH in the endosomal vesicle to initiate viral envelope fusion with the endosomal membrane . In both cases , the viral capsids are required to traffic to the perinuclear regions and deliver the viral genome into the nucleus to complete the infection process . We co-stained the KSHV-infected cells for both Orf65+ capsids and glycoprotein B ( gB ) , a viral envelope protein . The cells were visualized with an epifluorescence microscope . If a virion enters the cells via plasma membrane fusion , it should lose its envelope at the time of internalization or membrane fusion . However , if a virion enters the cells via endocytosis , it should retain its envelope immediately after internalization but lose it when subsequent fusion with endosomal membrane occurs . At 5 min post-infection ( mpi ) , over 90% of the Orf65+ particles inside the cells were positive for gB ( Figure S1A ) . In contrast , at 4 hpi , almost all the Orf65+ particles inside the cells were negative for gB ( Figure S1B ) . As expected , at both time points , 100% of the Orf65+ particles outside the cells were positive for gB ( Figure S1A and B ) . These results suggest that KSHV likely enters endothelial cells by endocytosis rather than plasma membrane fusion . To further analyze the role of endocytosis in KSHV entry into endothelial cells , we used a number of chemical inhibitors of endosomal acidification . NH4Cl is an acidotropic weak-base amine used to nonspecifically increase intravesicular pH [49] . Monensin is a sodium ionophore that is able to cross membranes and bind monovalent cations , resulting in an increase in endosomal pH [50] . Bafilomycin A1 is a potent inhibitor of the vacuolar ATPase and specifically prevents acidification of endosomal vesicles [51] . HUVEC pretreated with inhibitors for 1 h were inoculated with KSHV in the presence of the inhibitors , fixed at 8 hpi , and stained for Orf65+ capsids and nuclei . The cells were then visualized with an epifluorescence microscope , and the percentage of the nuclei with at least one Orf65+ particle as well as the total number of viral particles per nucleus was counted ( Figure 2 ) . While other methods such as infectivity and reporter assays have been used for monitoring the effects of inhibitors on viral entry and trafficking , they often rely on the expression of viral and reporter genes , and thus , can not distinguish the entry events of single versus multiple viral particles . In contrast , this method directly measures the entry and trafficking of individual viral particles to the perinuclear region . As shown in Figure 2A , B , D , E , G , and H , all inhibitors of endosomal acidification tested significantly reduced the total number of nuclei bearing at least one Orf65+ viral particle in a dose-dependent manner , suggesting that endocytosis rather than plasma membrane fusion , is the primary route of entry into HUVEC used by KSHV . These results are consistent with a previous study using viral infectivity as a method to monitor KSHV entry of human fibroblasts [44] . The effect of inhibition of endosomal acidification becomes more apparent when the absolute numbers of viral particles successfully reaching the nucleus is quantified ( Figure 2C , F and I ) . The nuclei of untreated cells had significantly more Orf65+ viral particles than the nuclei from cells treated with inhibitors . To ensure that the observed results were not due to the side effects of the inhibitors , we examined the viability of the cells . HUVEC were subjected to the same treatments with inhibitors and stained with propidium iodide ( PI ) . None of the inhibitors increased the number of PI+ cells even at the highest concentrations used ( Figure 2B , E and H , and Figure S2 ) . These chemical inhibitors also efficiently inhibited the endocytic trafficking of AlexaFluor 488-transferrin , thus demonstrating their effectiveness in inhibiting endosomal maturation ( Figure 2J ) . Together , these results suggest that KSHV requires endosomal acidification to efficiently infect endothelial cells and traffic to the perinuclear region . Mammalian cells utilize multiple endocytic pathways , including macropinocytosis , clathrin-mediated endocytosis , caveolae-mediated endocytosis , and a poorly understood non-clathrin , non-caveolae-mediated endocytosis pathway [4] . During clathrin-mediated endocytosis , clathrin-coated pits assemble on the cytoplasmic side of the plasma membrane in response to internalization signals from the receptor [52] . Hypertonic media has been shown to inhibit the formation of clathrin-coated pits at the plasma membrane , while the cationic amphiphilic agent chlorpromazine causes misassembly of clathrin-coated pits and inhibits clathrin-mediated endocytosis [53] . The role of the clathrin-mediated endocytosis pathway during KSHV entry into HUVEC was analyzed by adding dextrose at 100 to 300 mM to the media to generate hypertonic conditions , or by treating the cells with chlorpromazine at 1 to 15 µg/ml . Because of the high toxicity , cells treated with chlorpromazine were fixed and stained for Orf65+ capsids at 2 hpi . Under these conditions , both inhibitors had minimal toxicity ( Figure 3B and E , and Figure S3 ) . As shown in Figure 3A , B , D and E , inhibition of clathrin assembly by either dextrose or chlorpromazine significantly reduced the total number of nuclei bearing at least one Orf65+ viral particle . In addition , inhibition of clathrin assembly also reduced the absolute number of viral particles reaching each nucleus ( Figure 3C and F ) . The inhibitors also efficiently inhibited the internalization of AlexaFluor 488-transferrin , which is internalized by cells through clathrin-mediated endocytosis ( Figure 3G ) ; however , they did not inhibit the internalization of Cholera Toxin B ( CTB ) , which is internalized by cells through caveolae-mediated endocytosis ( Figure 3H ) . CTB binds GM1 ganglioside and triggers its internalization by caveolae/lipid raft-mediated endocytosis [54]–[56] . Thus , internalization of AlexaFluor 488-CTB was used as an indicator for caveolae/lipid raft-mediated endocytosis . As expected , inhibitors of caveolae-mediated endocytosis prevented the internalization of CTB ( see below ) . These results indicated that dextrose and chlorpromazine specifically inhibited clathrin- but not caveolae-mediated endocytosis . Furthermore , in a separate experiment , we found colocalization of KSHV capsids with clathrin ( Figure 4A , and Videos S5 , S6 , S7 and S8 ) and AlexaFluor 488-transferrin ( Figure 4B , and Videos S9 , S10 , S11 and S12 ) . Together , these results suggest that KSHV entry of endothelial cells is primarily mediated by clathrin-mediated endocytosis . Several viruses such as HIV-1 , SV40 , Ebola virus , Marburg virus , polyoma virus and echovirus use caveolae/lipid raft-mediated endocytosis to infect cells [57] . EBV entry into lymphocytes also requires cholesterol [58] . Endothelial cell membranes are highly enriched in caveolae , and uptake of albumin for transcytosis by these cells is facilitated by caveolae-mediated endocytosis [59] , [60] , suggesting that caveolae-mediated endocytosis in endothelial cells is a highly efficient process that could be exploited by viruses for entry into cells . We examined the involvement of caveolae-mediated endocytosis in KSHV entry into HUVEC . The chemical agents filipin , nystatin , and methyl-β-cyclodextrin ( MβCD ) deplete cholesterol from the plasma membrane and prevent caveolae/lipid raft-mediated endocytosis [57] . These agents were used at different concentrations to determine the role of caveolae/lipid rafts in KSHV entry . As shown in Figure 5 , none of these inhibitors had any effect on KSHV entry into HUVEC . The percentage of nuclei in the drug-treated cells bearing at least one Orf65+ viral particle was similar to the untreated controls ( Figure 5A , B , D , E , G , and H ) . Similarly , the absolute number of viral particles attached to each nucleus was not affected by any of the inhibitors ( Figure 5C , F and I ) . As controls , the inhibitors of caveolae/lipid raft-mediated endocytosis effectively prevented the internalization of CTB resulting in membrane accumulation of the marker ( Figure 5J ) . These results indicate that KSHV was not prevented from entering the cells and trafficking to the perinuclear region though the chemicals successfully inhibited caveolae/lipid raft-mediated endocytosis . These results are consistent with a previous study showing that nystatin and MβCD do not have any effect on KSHV entry into fibroblasts [44] . However , they contradict a later report showing that MβCD affects the association with microtubules and intracellular trafficking of viral particles in DMVEC [48] . Thus , there might be an alternative route for trafficking of KSHV particles in HUVEC . While the actin cytoskeleton was not involved in KSHV entry and trafficking in fibroblasts [44] , recombinant KSHV gB was sufficient to induce the protrusion of lamellipodia and filopodia [46] , both of which depend on actin polymerization [5] . We examined the rearrangements of the actin cytoskeleton during KSHV infection ( Figure 6 ) . Before infection , HUVEC had distinct actin stress fibers ( white arrow head ) and cortical actin structures ( white arrow ) . At as early as 0 . 5 mpi , we observed the dissolution of the actin stress fibers and increased intensity of the cortical actin structures , which continued for over 10 min . However , at 15 mpi , we started to observe the growth of new actin filaments , and dissolution of the cortical actin structures . At the same time , we also observed membrane ruffling , lamellipodia and filopodia ( red arrow ) . The new actin filaments were shorter and thicker than the stress fibers observed before viral infection , and were sustained for up to 120 mpi , at which time point , they resembled distinct actin tails or spikes ( red arrow head ) . These results indicate that KSHV induces dynamic remodeling of the actin cytoskeleton at the early stages of infection in endothelial cells . We further examined the role of actin cytoskeleton in KSHV entry and trafficking in endothelial cells by using chemicals to disrupt their dynamics . Latrunculin A reversibly disrupts actin dynamics by targeting monomeric G-actin and preventing actin polymerization [61] , [62] . Cytochalasin D reversibly targets F-actin , inducing depolymerization of existing actin filaments and increasing the cellular pool of ADP-bound actin monomers [63] , [64] . Jasplakinolide reversibly inhibits normal cellular actin dynamics by hyperstabilizing actin filaments , preventing depolymerization , and depleting the cellular pool of free actin monomers available for de novo polymerization [65] . Disruption of actin dynamics did not significantly reduce the total number of nuclei bearing at least one Orf65+ viral particle ( Figure 7B , F and J ) . Although these results are consistent with previous observations that the actin cytoskeleton was not involved in KSHV entry and trafficking in fibroblasts measured by non-viral particle-based assays [44] , when the total number of viral particles docked at each nucleus was quantified , the cells treated with the actin-disrupting agents had significantly fewer viral particles per nucleus than those of untreated cells ( Figure 7A , C , E , G , I and K ) . In addition , the effect of inhibition also varied with the actin-disrupting agents , which could reflect their distinct modes of action . These results suggest that actin dynamics are essential for the entry of most KSHV particles; however , it appears that a small number of viral particles are able to enter the cells even in the absence of a functional actin cytoskeleton system , which could partially account for the failure to observe an effect of actin-disrupting agents on KSHV entry and trafficking when measured with non-viral particle-based infectivity assays , especially when high viral multiplicity of infection ( MOI ) was used [44] . Under our experimental conditions , we did not observe any effect of these inhibitors on cell viability ( Figure 7B , F and J , and Figure S4 ) . Examination of the actin cytoskeleton showed that they were effectively disrupted by all three inhibitors ( Figure 7D , H and L ) . Internalization of Alexafluor 488-transferrin and accumulation in the perinuclear region was used as an indicator of active clathrin-mediated endocytosis . As shown in Figure 7M , internalization of transferrin was inhibited by the disruption of actin dynamics , resulting in reduced levels of transferrin in the perinuclear region of the treated cells , further illustrating the important role of actin dynamics during endocytosis in endothelial cells . While disruption of actin dynamics affected the entry of KSHV particles , our results indicated that its effect on KSHV infectivity might only be obvious at a low MOI . To confirm these observations , we infected HUVEC at 10 and 2 MOI . Since there is no plaque assay for KSHV , we quantified viral MOI based on the number of internalized viral particles per cell . To track viral infectivity , we stained the cells for LANA and counted the percentage of LANA-positive cells . As expected , cytochalasin D and jasplakinolide effectively reduced the average numbers of viral particles per nuclei ( Figure 8A ) . At a high MOI ( 10 ) , these inhibitors did not have any effects on the percentage of cells containing at least one viral particle ( Figure 8B ) , as well as viral infectivity measured by the percentage of LANA-positive cells ( Figure 8C ) . In contrast , at a low MOI , both cytochalasin D and jasplakinolide effectively reduced the percentage of cells containing at least one viral particle ( Figure 8B ) and viral infectivity ( Figure 8C ) . Thus , the effect of disrupting actin dynamics on KSHV infectivity is MOI-dependent . The Rho family of small GTPases such as Rho , Rac and Cdc42 are important regulators of actin cytoskeletal assembly and organization , as well as intracellular trafficking events during endocytosis [66] . Rho induces the formation of stress fibers , while activation of Rac and Cdc42 induces the polymerization of actin and the formation of a network of actin filaments underlying the plasma membrane [67] . Considering these known functions , Rho GTPases are excellent candidates for mediating the signaling between actin and endocytic traffic [68] , and may therefore be involved in the endocytic entry and trafficking of KSHV . Clostridium difficile Toxin B ( CdTB ) glucosylates and inactivates Rho GTPases , leading to the disruption of actin dynamics [69] . A previous study showed that KSHV infection of HEK293 cells activated RhoA GTPase and treatment with CdTB inhibited the internalization of KSHV DNA in fibroblasts and DMVEC [47] . However , whether these effects were mediated by actin dynamics and whether the same effects are also present in HUVEC remain unclear . To examine the role of Rho GTPases in KSHV entry of HUVEC , cells were treated with CdTB at different concentrations before and during infection with KSHV . CdTB significantly reduced the total number of nuclei bearing at least one Orf65+ viral particle ( Figure 9A and B ) . The absolute number of Orf65+ viral particles per nucleus was also reduced in a dose-dependent fashion ( Figure 9A and C ) . As expected , treatment with CdTB led to the disruption of the actin cytoskeleton ( Figure 9D ) ; however , it did not affect the viability of the cells ( Figure 9B and Figure S5A ) . These results implicate a role for Rho GTPases in KSHV entry and trafficking to the nucleus . Following the activation of Rho GTPases , actin filament assembly is regulated by N-WASP and the Arp2/3 complex . N-WASP activity is required for the activation of Arp2/3 . Both Arp2/3 and N-WASP are recruited to sites of clathrin-mediated endocytosis [27] , [70] . Arp2/3 binds to existing actin filaments and directly nucleates new actin filaments to form a branched network [27] . Wiskostatin is a cell-permeable chemical that inhibits N-WASP by stabilizing the auto-inhibited conformation , and thus preventing the activation of the Arp2/3 complex [71] . To further investigate the role of de novo actin assembly in viral entry and trafficking , HUVEC were pretreated with wiskostatin , infected with KSHV , and stained for Orf65+ capsids . At 25 µM , wiskostatin decreased the total number of nuclei with at least one Orf65+ viral particle ( Figure 9E and F ) . In addition , the absolute number of viral particles docked at each nucleus was reduced in wiskostatin-treated cells as compared to the untreated cells ( Figure 9G ) . Similar to CdTB , treatment with wiskostatin also led to the disruption of the actin cytoskeleton ( Figure 9H ) but it had minimal effect on cell viability ( Figure 9F and Figure S5B ) . Thus , the regulation of actin polymerization through the WASP family members and Arp2/3 activation is required for KSHV particles to enter HUVEC and traffic to the perinuclear region . These results are consistent with those of Figure 7 , and Figure 9A , B , C and D , and demonstrate the importance of de novo actin assembly and regulation of actin dynamics during KSHV entry . While a previous study has shown that disruption of lipid raft with MβCD and nystatin enhanced KSHV internalization but inhibited subsequent microtubule-mediated trafficking of viral particles in DMVEC [48] , our results have shown that neither inhibitor has any effect on the internalization and trafficking of viral particles but actin cytoskeleton-disrupting agents do . We speculated that actin dynamics might be involved in the endosomal sorting and/or intracellular trafficking of KSHV particles in HUVEC . It has been shown that the role of actin in endocytosis is variable in mammalian cells depending upon the cell type and/or the ligand/receptor . To further confirm the role of actin dynamics in clathrin-mediated endocytosis during KSHV entry and trafficking in endothelial cells , HUVEC were infected with KSHV for 30 min , fixed and stained for Orf65+ capsids , actin filaments , microtubules and the cell nucleus . As shown in confocal images ( Figure 10A ) , ORF65+ viral particles were closely associated with both actin fibers and microtubules . We next sought to determine at what point during the viral entry and trafficking process actin might be involved . In a time-course experiment , HUVEC were inoculated with KSHV and fixed at 2 , 5 , 15 , 30 , and 60 mpi . Figure 10B reveals colocalization of actin filaments with ORF65+ KSHV particles at all time points examined . The association of KSHV particles with actin filaments peaked at around 10–15 mpi reaching over 90% of all viral particles before it started to drop ( Figure 10C ) . Interestingly , we observed the association of viral particles with actin filaments as late as 4 hpi albeit endocytosis is a relative rapid process . However , such association with actin filaments at the later time points of infection might simply reflect the unsynchronized nature of the infection . Nevertheless , these results confirmed a potential role for actin dynamics in multiple steps of clathrin-mediated endocytosis , and KSHV entry and trafficking in endothelial cells . To further identify the stage of endocytosis during KSHV entry and trafficking in endothelial cells that might be regulated by actin dynamics , HUVEC were infected with KSHV , and stained for markers of endosomes as well as Or65+ KSHV particles and actin filaments . The image in Figure 10D reveals the colocalization of Orf65+ viral capsids , actin filaments , and EEA1 , a marker for the early endosome ( Video S13 ) . Figure 10E reveals the association of viral capsids with actin filaments , and Rab11 , a marker for the recycling endosome ( Video S14 ) . Figure 10F demonstrates the colocalization of viral capsids with actin filaments , and LAMP-1 , a marker for the late endosome/lysosome ( Video S15 ) . These results suggest that actin dynamics are involved in multiple steps of endocytosis and endosomal trafficking of KSHV , from the early endosome through the late endosome/lysosome . The association of KSHV particles with both actin filaments and markers of early and recycling endosome , and lysosome suggest that actin dynamics might be involved in several post-internalization steps of endocytosis including endosome sorting and trafficking in endothelial cells . We further examined the association of Alexafluor 488-transferrin with actin filaments , and markers of endosomes ( Figure 11 ) . HUVEC were treated with Alexafluor 488-transferrin for 60 min , fixed and stained for early , intermediate , and late endosomes as well as actin filaments . Consistent with previous observations in epithelial cells that the internalization of transferrin is primarily mediated by transferrin receptor 1 , and once internalized , is sorted to early and recycling endosomes [72] , [73] , we observed colocalization of 34 . 8% and 24 . 5% of Alexafluor 488-transferrin with EEA1 ( Figure 11A and E , and Video S16 ) and Rab11 ( Figure 11B and E , and Videos S17 and S18 ) , respectively . Significantly , as much as 8 . 6% of transferrin was colocalized with both EEA1 and actin filaments , and as much as 3 . 5% of transferrin was colocalized with both Rab11 and actin filaments ( Figure 11F ) , suggesting an active role of actin cytoskeleton in these endosomal steps . We also observed colocalization of 2 . 0% of the Alexafluor 488-transferrin with Rab7 , a late endosome marker ( Figure 11C and E , and Video S19 ) , which is consistent with the observation that a small portion of transferrin is internalized through transferrin receptor 2-mediated endocytosis and sorted to late endosome/lysosome [74] . In spite of the low ratio of association of Alexafluor 488-transferrin with Rab7 , surprisingly , as much as 1 . 9% of the transferrin was colocalized with both Rab7 and actin filaments ( Figure 11F ) , indicating that most of the transferrin-containing Rab7 late endosomes were associated with actin filaments . These results suggest that actin dynamics might be heavily involved in late endosomal sorting and/or trafficking . Interestingly , we observed colocalization of as much as 21 . 6% of the Alexafluor 488-transferrin with LAMP-1 ( Figure 11D and E , and Video S20 ) , which probably reflected the accumulation of transferrin in lysosomes over time . Accordingly , only 0 . 8% of the transferrin was colocalized with both LAMP-1 and actin filaments ( Figure 11F ) , suggesting that once reaching the lysosomes , the transferrin-containing endosomes no longer require actin filaments for sorting and trafficking . Together , these results suggest that , in addition to ligand internalization , actin dynamics are also involved in several other steps of clathrin-mediated endocytosis including endosomal sorting and trafficking from the early endosome through the late endosome/lysosome in endothelial cells .
The objective of this study was to identify the cellular pathways and factors that are exploited by KSHV for its entry and trafficking in endothelial cells . We demonstrate here the utility of an assay based on immunostaining of viral capsids to visualize KSHV virions that have successfully entered the cells and docked at the nucleus . Previous studies relied on the expression viral or reporter genes as indicators for successful viral entry [44] , [75] . However , even a single viral particle can potentially enter a cell , travel to the nucleus , inject viral DNA into the nucleus and initiate an infection . Thus , these methods are less sensitive and are confounded by factors that influence the post-entry and trafficking events . Our results derived from assessing the effects of actin cytoskeleton disrupting agents on KSHV entry and infection have clearly illustrated this point ( Figures 7 and 8 ) . While we detected an effect of these inhibitors on the internalization of viral particles , the effect on KSHV infectivity was MOI-dependent . Similar to other herpesviruses , KSHV might use more than one route to enter cells [39] . In this case , inhibition of one pathway might not affect viral entry via another pathway , resulting in a reduced number of viral particles entering the cells , but the reduction in viral/reporter gene expression and viral infectivity will not be detectable . In fact , it has been demonstrated that if one endocytic pathway is blocked , the cells can actually upregulate alternate endocytic routes [76] . In addition , extended exposure to chemical inhibitors might affect the expression of viral/reporter genes simply because they are toxic to the cell rather than affecting the entry or trafficking events . Furthermore , inhibition of the later steps of endocytosis or endosomal trafficking does not necessarily prevent viral attachment or early internalization . Finally , removal of the inhibitors will allow the virus to complete the infection process if the inhibition is reversible . Certainly , the use of multiple inhibitors with differing modes of action , in a range of concentrations , to target a single pathway could impart more validity to the results of these experiments . The results of this study clearly demonstrate an essential role for actin dynamics in KSHV entry and trafficking in endothelial cells . Endothelial cells are a highly relevant cell type for investigating KSHV infection as the neoplastic component of KS lesions is primarily composed of KSHV-infected endothelial cells . Although our results apparently contradict those in other studies , which did not identify actin cytoskeleton as a requirement for viral entry and trafficking [44] , [45] , several explanations can account for the discrepancy . First of all , if only the total number of KSHV-infected cells ( nuclei with at least one Orf65+ viral capsid ) is quantified , actin-disrupting agents did not seem to prevent viral entry ( Figure 7B , F and J ) . However , when the absolute number of viral particles docked per nucleus is quantified , the effect of actin disruption on viral entry becomes clear ( Figure 7C , G and K ) . Consistent with these results , when the infection was performed at a low MOI , the effect of actin-disrupting agents on the total number of KSHV-infected cells was also demonstrated ( Figure 8B ) . These results were confirmed by LANA-based infectivity assay ( Figure 8C ) albeit the assay could be affected by post-entry events as discussed above . The results from the experiments using inhibitors of actin regulators ( Figure 9 ) including CdTB , an inhibitor of Rho GTPases , and wiskostatin , an inhibitor of N-WASP , and the observation of viral capsids closely associated with actin filaments ( Figure 10 ) further confirmed this conclusion . In fact , these results are consistent with previous observations that RhoA GTPase is important for KSHV entry in HEK293 cells [47] and association of viral particles with microtubules in DMVEC [48] . While these studies have never established a role for actin dynamics in KSHV entry and trafficking , they , nevertheless , support an essential role of Rho GTPases in KSHV entry and trafficking . Together , these observations are consistent with KSHV induction of actin dynamics during KSHV infection of endothelial cells , which include the disruption of actin stress fibers and growth of cortical actin structures at the early stage of KSHV entry ( <15 mpi ) , and reappearance of actin filaments as actin tails or spikes at the later stage ( >15 mpi ) ( Figure 6 ) . Secondly , the results reported here are from experiments that exclusively used endothelial cells as the target cells for KSHV infection , while previous studies mainly used fibroblasts and HEK293 cells [44]–[48] . Herpesviruses have been found to use different entry pathways for different types of target cells . For instance , HSV-1 infects some cells through plasma membrane fusion but other cell types through endocytosis [40]–[42] . Plasma membrane fusion may not require the participation of actin cytoskeleton for virus internalization , while endocytosis does . Importantly , while we have identified clathrin-mediated endocytosis as the major route of entry used by KSHV to infect endothelial cells , none of the inhibitors used was able to completely block viral entry , suggesting the existence of an alternate route of entry in endothelial cells . The existence of such an alternative route could obviously confound delineation of the role of cellular components such as the actin cytoskeleton in regulating KSHV entry and trafficking if inappropriate assays and controls are used . Studies of influenza virus entry have demonstrated that viral entry occurring at the apical surface of cells requires actin , while viral entry from the basolateral side did not [15] , [20] . This suggests that the greater degree of membrane curvature at the apical surface might necessitate actin dynamic activity to induce membrane invagination , while the basolateral side does not [17]–[19] . In mammalian cells , the actin cytoskeleton is critical for the maintenance of plasma membrane tension . Chemical disruption of the entire actin cytoskeleton will relieve membrane tension , which may in some cases facilitate the formation of membrane invaginations , and obviate the need for actin polymerization to drive this step of endocytosis [5] , [17] , [18] , [77] . In addition to forcing membrane invagination , actin might participate in several other steps of the endocytic pathway , beginning with the aggregation of receptors into clathrin-coated pits , scission of the newly-formed clathrin-coated vesicle away from the plasma membrane , movement of the clathrin-coated vesicle towards the cell interior , and trafficking of the vesicle as it matures along the endocytic pathway . Vesicle trafficking at the cell cortex might be actin-dependent , prior to transfer of the vesicle to microtubules to complete the journey to the perinuclear region . In fact , depolymerization of microtubules did not prevent KSHV binding or entry , but did prevent the delivery of viral particles to the nucleus [45] . KSHV infection also activated the formin family member mDia2 [45] . mDia1 and 2 coordinate actin assembly at the cell cortex [78] , stabilize microtubules independently of actin nucleating activity [79] , and localize to endosomes [80] . While there is evidence suggesting the involvement of the actin cytoskeleton in the internalization of cargo and initial formation of endosomes during clathrin-mediated endocytosis in mammalian cells , information on its role in endosome sorting and trafficking is limited [5] . In our study , KSHV capsids were observed to colocalize with actin filaments as well as markers for early and recycling endosomes , and lysosome . The fact that KSHV capsids were associated with actin filaments at the early time points of infection until the docking of the capsids at the nucleus suggests that actin dynamics might be involved in multiple steps of internalization , and endosomal sorting and trafficking of viral particles in endothelial cells . In agreement with these results , we have also observed colocalization of transferrin with actin filaments as well as markers of early , recycling and late endosomes , and to a lesser degree , lysosomes , further supporting the involvement of actin dynamics in multiple endosomal steps of clathrin-mediated endocytosis in endothelial cells . Additional studies are required to delineate the specific mechanism by which actin dynamics mediate different steps of endosomal sorting and trafficking during clathrin-mediated endocytosis . In this context , KSHV can be considered as a useful tool for elucidating the detailed mechanism of a vital cellular pathway .
Early passage of HUVEC were obtained from Clonetics , Lonza , and maintained in complete EBM-2 culture media ( Allendale , NJ ) . KSHV-infected BCP-1 cells isolated from peripheral blood mononuclear cells of a PEL patient [81] were maintained in culture in RPMI1640 containing 10% fetal bovine serum ( FBS ) . To induce virus production , BCP-1 cells in log-phase were serum-starved overnight in RPMI1640 . FBS was added back to the culture media to a final concentration of 10% , together with 12-O-tetradecanoyl-phorbol-13-acetate ( TPA ) at 30 ng/ml and sodium butyrate at 200 µM . At 2 day post-induction , the cells were washed and the media was replaced with fresh RPMI1640 containing 10% FBS without TPA or sodium butyrate . At 6 days post-induction , the BCP-1 cells were centrifuged at 1 , 000×g for 5 min , and the supernatant containing viral particles was collected , and either used immediately for infection or stored at 4°C . For viral infection , supernatant from induced BCP-1 cells was centrifuged for 10 min at 10 , 000×g . The pellet containing infectious viral particles was resuspended in one-fourth of the original volume in EBM-2 media with or without chemical inhibitors . To infect the cells , HUVEC were seeded onto glass cover slips overnight to achieve 70–80% confluency . For assays using chemical inhibitors , cells were pretreated with the inhibitors in EBM-2 media for 1 h prior to infection . The cells were then inoculated with the virus preparation and incubated for the indicated times in the presence of the inhibitors , fixed in 2% paraformaldehyde and processed for immunostaining of viral capsids and cellular markers . A monoclonal antibody isotype IgG2a ( clone 6A ) to KSHV small capsid protein ( Orf65 ) was used to stain KSHV capsids [82] . A rabbit antibody to gB was a kind gift of Dr . Bala Chandran at the Rosalind Franklin University of Medicine an Science , Chicago , Illinois . A monoclonal antibody to β-actin was obtained from Sigma ( St . Louis , MO ) . A rat monoclonal antibody to LANA , and rabbit antibodies to clathrin heavy chain , β-tubulin , EEA1 , Rab11 , Rab7 and LAMP-1 were purchased from Abcam ( Cambridge , MA ) . Chemical inhibitors of endosomal acidification bafilomycin and monensin were purchased from Sigma . NH4Cl was obtained from Fisher Scientific ( Pittsburgh , PA ) . Chemical inhibitors of clathrin assembly chlorpromazine and dextrose , and caveolae/lipid raft-mediated endocytosis filipin , nystatin , and MβCD were purchased from Sigma . Chemical disruptors of the actin cytoskeleton latrunculin A and cytochalasin D were from Sigma while jasplakinolide was from Calbiochem , EMD Chemicals , Inc . ( Gibbstown , NJ ) . The chemical inhibitor of WASP wiskostatin and inhibitor of Rho GTPases CdTB were from Calbiochem . All chemical inhibitor stock solutions were prepared according to the manufacturer's directions . AlexaFluor 488-phalloidin , AlexaFluor 488-transferrin , AlexaFluor 488-CTB , secondary antibodies AlexaFluor 568 goat-anti-mouse IgG1 , AlexaFluor 568 and 647 goat anti-mouse IgG2a , and AlexaFluor 488 goat anti-rabbit IgG were from Molecular Probes , Invitrogen ( Carlsbad , CA ) . DAPI was from BioChemika Ultra , Sigma . PI labeling kit was obtained from Roche ( Nutley , NJ ) . For quantification of entry and trafficking of viral particles to the nuclei , images were acquired using a Zeiss Axiovert 200 M epifluorescence microscope equipped with a 63× oil immersion objective ( Carl Zeiss Microimaging Inc . , Thornwood , NY ) . Images were acquired for 5 fields of view per coverslip to allow counting of Orf65+ capsids docked at nuclei . All experiments were performed in triplicate . Results are expressed as the mean±SD . For colocalization experiments , images were acquired with an Olympus FV1000 scanning confocal microscope equipped with a 60× objective , NA 1 . 42 ( Olympus Life Science , Center Valley , PA ) . Z-stacks were acquired at 0 . 25 µm per slice by sequential scanning . Olympus FV1000 software was used to generate cross-sectional images and 3D-projection images ( Olympus Life Science ) . To examine the cytotoxicity of the inhibitors , HUVEC grown in 48 well plates were treated with the inhibitors at the indicated concentrations for the stated period of time . One hour prior to fixation , the cells were labeled with PI to identify nonviable cells . Cells were washed twice with PBS , fixed with 2% paraformaldehyde for 30 min , and counterstained with DAPI . The cells were visualized with a 20× objective using the Axiovert 200 M fluorescence microscope . The total number of cells and the corresponding PI-positive cells from 5 representative fields were counted , and calculated as PI+ cells ( % ) . In all the experiments , cells left to air-dry for 20 min to induce cell death were used as positive controls . All results were expressed as the means±s . d . . | Endocytosis , an essential biological process mediating cellular internalization events , is often exploited by pathogens for their entry into target cells . The role of actin cytoskeleton in clathrin-mediated endocytosis in mammalian cells remains unclear . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a gammaherpesvirus linked to the development of Kaposi's sarcoma , an endothelial malignancy commonly found in AIDS patients , and several other malignancies . In this study , we found that KSHV uses the clathrin-mediated endocytosis pathway to enter endothelial cells , and this process is regulated by actin dynamics . We found KSHV particles in early and recycling endosomes , and lysosomes , which are docked on actin filaments at the early time points of viral infection . Similarly , transferrin , which enters cells by clathrin-mediated endocytosis , is associated with actin filaments together with early and recycling endosomes , and , to a lesser degree , with late endosomes and lysosomes . Disruption of the actin cytoskeleton and inhibition of regulators of actin nucleation such as Rho GTPases and Arp2/3 complex profoundly blocked KSHV entry and trafficking in endothelial cells . Together , these results define an important role for actin dynamics in multiple endosomal steps during KSHV infection and clathrin-mediated endocytosis in endothelial cells . | [
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] | 2009 | Actin Dynamics Regulate Multiple Endosomal Steps during Kaposi's Sarcoma-Associated Herpesvirus Entry and Trafficking in Endothelial Cells |
Lymphatic filariasis and onchocerciasis are disabling and disfiguring neglected tropical diseases of major importance in developing countries . Ivermectin is the drug of choice for mass drug administration programs for the control of onchocerciasis and lymphatic filariasis in areas where the diseases are co-endemic . Although ivermectin paralyzes somatic and pharyngeal muscles in many nematodes , these actions are poorly characterized in adult filariae . We hypothesize that paralysis of pharyngeal pumping by ivermectin in filariae could result in deprivation of essential nutrients , especially iron , inducing a wide range of responses evidenced by altered gene expression , changes in metabolic pathways , and altered developmental states in embryos . Previous studies have shown that ivermectin treatment significantly reduces microfilariae release from females within four days of exposure in vivo , while not markedly affecting adult worms . However , the mechanisms responsible for reduced production of microfilariae are poorly understood . We analyzed transcriptomic profiles from Brugia malayi adult females , an important model for other filariae , using RNAseq technology after exposure in culture to ivermectin at various concentrations ( 100 nM , 300 nM and 1 μM ) and time points ( 24 , 48 , 72 h , and 5 days ) . Our analysis revealed drug-related changes in expression of genes involved in meiosis , as well as oxidative phosphorylation , which were significantly down-regulated as early as 24 h post-exposure . RNA interference phenotypes of the orthologs of these down-regulated genes in C . elegans include “maternal sterile” , “embryonic lethal” , “larval arrest” , “larval lethal” and “sick” . These changes provide insight into the mechanisms involved in ivermectin-induced reduction in microfilaria output and impaired fertility , embryogenesis , and larval development .
Lymphatic filariasis ( LF ) is caused by the transmission of Wuchereria bancrofti , Brugia malayi , and Brugia timori by infected mosquitos and is endemic in 60 countries , mainly in subtropical and tropical regions , with over 120 million people infected [1] . Onchocerciasis ( river blindness ) is caused by Onchocerca volvulus and afflicts 25 million people in 24 countries , mainly in Sub-Saharan Africa [2] . Long-running control and elimination programs aim to reduce transmission of both infections by mass drug administration ( MDA ) of microfilaricidal drugs and to alleviate suffering and disability in onchocerciasis patients [3 , 4] . The macrocyclic lactones ( ML ) are a class of broad-spectrum anthelmintic drugs which include the avermectins and milbemycins ( e . g . , ivermectin ( IVM ) and moxidectin , respectively ) [5] . Although similar in structure , they differ in pharmacokinetics and dynamics as well as physicochemical properties [6 , 7] . These drugs have been extensively used in veterinary medicine for the treatment or prevention of nematode infections and ectoparasite infestations . In human medicine , IVM is one of the three mainstay drugs in the LF global elimination program [8] and is the only drug used in onchocerciasis MDA campaigns [2] . IVM is predominantly microfilaricidal , the killing effect on adults being only moderate and requiring repeated treatment cycles over several years [9] . IVM pseudo-irreversibly activates nematode and arthropod glutamate-gated chloride channels ( GluCls ) , resulting in hyperpolarization of neurons and pharyngeal muscle cells , leading to paralysis of movement and pharyngeal pumping , and ultimately starvation of the worm [10–12] . Treatment results in a dramatic reduction in microfilaria ( mf; first-stage larvae ( L1 ) production by adult female filariae , but the mechanisms involved are not understood . GluCl expression in B . malayi mf was localized to a muscle structure surrounding the excretory-secretory ( ES ) vesicle , suggesting that release of proteins was regulated by GluCls ( 14 ) . In the same study , IVM exposure resulted in a significant reduction of protein release , possibly preventing the secretion of proteins that allow evasion of the host’s immune system [13] . Recently , the B . malayi GluCl gene avr-14 was found to be highly expressed in reproductive tissues and embryos , suggesting an involvement of GluCls in gamete production and embryogenesis which may account for the sterilizing effect observed in adult filarial worms treated with IVM [14] . Our limited understanding of IVM’s mode of action on adult filarial worms hampers treatment optimization and sustainability of the efficacy of this drug , and investigations into its pharmacology are of paramount importance . We used adult female B . malayi , a well-characterized model of filarial infections , to study the time-dependent transcriptomic changes induced by exposure to IVM in vitro . We performed two separate studies: 100 nM IVM exposure for up to 72 h , and 300 nM and 1 μM for up to 5 days , to gain insight into the mechanisms which underlie the drug-induced long-term sterility of adult females and subsequent reduction of mf in skin or blood of treated patients . The drug concentrations used in this present study have previously been tested on adult B . malayi females in vitro [15] and have also been tested in C . elegans [7] . The rationale for using the lowest concentration ( 100 nM ) in the first study was to expose worms to a concentration similar to plasma levels detected in humans after administration of a dose typically used in MDA [16–18] . It is recommended that in vitro experimental analyses of drug effects include concentrations higher than the maximal blood/plasma concentration achieved in an animal [19] . We therefore tested higher IVM concentrations in a second phase . These results allowed us to also compare our results with those previously observed in B . malayi at these concentrations .
All animal procedures were approved by the University of Georgia , Institutional Animal Care and Use Committee , and complied with U . S . Department of Agriculture's regulations ( USDA Assurance No . A3437-01 ) Adult male jirds ( Meriones unguiculatus ) were infected subcutaneously with ≈400 infective B . malayi larvae ( L3 ) . After a minimum of 90 days post-infection ( ranging from 3 to 6 months ) , jirds were euthanized by exposure to CO2 and adult worms were collected from the peritoneal cavity via lavage . RNA extraction was performed as described previously [20] . Briefly , live worms were washed several times with PBS; immotile worms were excluded from the study . RNA was extracted from 5 to 8 worms per group using nuclease-free reagents . On ice , 125 μl 0 . 1X Tris-EDTA ( TE ) buffer , pH 8 . 0 ( Ambion , Life Technologies , Burlington , ON ) and 375 μl Trizol LS reagent ( Ambion ) were added to each tube . Worm extracts were obtained by conducting three cycles of flash-freezing in liquid N2 followed by crushing of worms using plastic pestles . One-hundred ( 100 ) μl chloroform was added to each sample in a 1 . 5 ml Eppendorf tube , which was vortexed and incubated 3 min at room temperature . The extracts were transferred to phase-lock gel heavy tubes ( 5 PRIME , Gaithersburg , MD ) and centrifuged at 11 , 900 x g at 4°C for 15 min for phase separation . The aqueous phase ( approximately 150 μl ) was transferred to fresh 1 . 5 ml Eppendorf tubes and mixed with 250 μl ice-cold isopropanol . RNA was precipitated by centrifugation at 12 , 200 x g at 4°C for 30 min followed by an overnight incubation at -20°C . Pellets were washed twice with 80% EtOH , allowed to dry and resuspended in 50 μl 0 . 1X TE prior to heating at 55°C for 10 min . Total RNA was purified and concentrated on columns ( RNeasy Min-Elute Cleanup Kit , Qiagen , Valencia , CA ) . Samples were treated with DNase ( Ambion DNA-free Kit , Life Technologies , AM1906 , Burlington , ON ) according to the manufacturer’s instructions to remove contaminating DNA . Assessment of RNA concentration and purity was done by spectrophotometry ( NanoDrop 1000 , Wilmington , DE ) and RNA samples were shipped overnight on dry ice to the NIH-FR3 ( Molecular Division ) at Smith College ( Northampton , MA ) for cDNA library preparation and Illumina sequencing . RNA concentration , purity and integrity were precisely measured using the Qubit RNA BR Assay Kit ( Life Technologies , Q10210 , Burlington , ON ) and on an Agilent 2100 Bioanalyzer ( Santa Clara , CA ) . Messenger RNA was isolated with a NEBNext Poly ( A ) mRNA Magnetic Isolation Module ( New England Biolabs , E7490 ) . The enriched mRNA served as template for cDNA library preparation with a NEBNext Ultra RNA Library Prep Kit Illumina ( NEB , E7530 ) and NEBNext Multiplex Oligos for Illumina ( Index Primer 1–12 ) ( NEB , E7600 ) following the manufacturer’s instructions . Assessment of quality , concentration and size of the cDNA , was performed for each library using a Qubit 2 . 0 Fluorometer ( Life Technologies , Q32866 ) , Qubit dsDNA BR assay kit ( Life Technologies , Q32850 ) , High Sensitivity DNA Analysis Kit ( Agilent , 5067–4626 ) and Agilent 2100 Bioanalyzer . cDNA libraries were sequenced on an Illumina MiSeq Platform employing a 150 base pair paired-end NGS setting . Sequencing data were uploaded and stored in BaseSpace ( https://basespace . illumina . com ) for subsequent analysis .
Procedures for data analysis were previously described [20] . Unaligned raw sequencing data ( fastq ) files were downloaded to the Mason-Galaxy platform hosted at Indiana University ( http://galaxy . iu . edu/ ) [21 , 22] . Raw sequence data were first processed using Fastq Groomer ( v 1 . 0 . 4 ) to convert file format to Fastq Sanger format and quality scores were verified using FastQC ( version 0 . 52 ) . Based on quality statistics , sequences were trimmed from both the 5’ and 3’ ends using Fastq Quality Trimmer ( v 1 . 0 . 0 ) and Trim Galore ( v 0 . 2 . 8 . 1 ) . Mapping of gapped reads to the B . malayi reference genome ( ftp://ftp . wormbase . org/pub/wormbase/species/b_malayi/sequence/genomic/b_malayi . PRJNA10729 . WS243 . genomic . fa . gz ) was performed with Tophat2 ( v 0 . 6 ) and RNA sequencing metrics were retrieved using Picard tools ( Table 1 ) . Picard alignment summary metrics ( http://broadinstitute . github . io/picard/ ) were generated from BAM files using SAM/BAM Alignment Summary Metrics ( version 1 . 56 . 0 ) tool . The read count table was then generated by counting the number of reads per gene feature with HTSeq-count ( version 1 . 0 . 0 ) [23] using the union mode to handle reads covering more than one feature and default settings for all other parameters . Differential gene expression analysis between time points and drug concentrations was realized in edgeR ( version 3 . 10 . 5 ) [24] through the web interface provided by NetworkAnalyst ( http://networkanalyst . ca ) [25 , 26] . To correct for different library sizes and reduce RNA compositional effects , read counts for gene features were normalized in edgeR using the trimmed mean of M-values ( TMM ) method [27] . The empirical Bayes method based on weighted conditional maximum likelihood was employed to estimate tagwise ( gene-specific ) dispersion values [28] . Once negative binomial models were fitted and dispersion values estimated , the exact test was used for pairwise differential expression testing between treated and untreated groups . Significance was set as an experiment-wide false discovery rate ( FDR ) <0 . 20 ( after the Benjamini-Hochberg method [29] ) . After filtering , gene lists for each pairwise comparison were imported into Microsoft Excel for further analysis . Common dispersion , which provides information on the overall variability across the genome for a dataset , and Biological Coefficients of Variation ( BCV ) were calculated; BCV plots were generated using the edgeR Bioconductor package ( version 3 . 12 . 0 ) [24] .
Table 1 displays the total number of transcripts obtained for each sample in both studies and the number of sequence reads for each cDNA library . On average for study 1 ( 100 nM IVM ) , 85 . 82% of the high quality sequence reads were mapped to the B . malayi transcriptome after removal of ambiguous sequence matches ( Table 1 ) . Gene mapping fluctuated from 1 to 58487 sequence read ( s ) per gene . On average for study 2 ( 300 nM and 1 μM IVM ) , 81 . 03% of the high quality sequence reads were mapped to the B . malayi transcriptome after removal of ambiguous sequence matches , and gene mapping fluctuated from 1 to 27507 sequence read ( s ) per gene ( Table 1 ) . In study 1 , 93 . 28–95 . 79% of reads aligned in pairs to a transcript; in study 2 , 90 . 43–93 . 52% of reads aligned in pairs . BCV values ranged from 17 to 27% in the first study and 15 to 35% in the second study ( S3 Table ) . BCV plots show that the BCV values in the first study tended to increase over time and are shown in S1A–S1C Fig . In the second study , the BCV values were higher at the 48 h time point than at 5 days for both IVM concentrations . We used gene expression levels from untreated ( control ) worms at each time point as a baseline to identify genes with differential expression after exposure to IVM in each sample . Pairwise comparisons revealed 15 ( 100 nM , 72 h ) to 421 ( 100 nM , 48 h ) DE genes ( Table 2 , S1 Table ) . Between 20 . 59 and 65 . 15% of DE genes could be assigned GO terms , while similar proportions had a C . elegans ortholog . The greatest overlap of DE genes was seen between the 300 nM and 1 μM groups , with 66 dysregulated genes . When comparing the overlap of DE genes from the first and second study , only 5 genes were found to overlap across the 3 concentrations , with 10 genes in common between the 100 nM and 300 nM groups and 20 genes in common between the 100 nM and 1 μM groups . GO term enrichment showed that 5 of these genes were enriched in “structural molecule activity” molecular function ( GO:0005198; FDR = 0 . 009 ) . Five genes were common to all three IVM treatments ( Bm8439 and Bm4605 ( two collagens ) , Bm9776 , Bm6450 , and Bm9380 ) . Bm9380 is a serpin precursor ( Bm-spn-2 ) and was significantly up-regulated in both studies ( 100 nM 72 h; 300 nM and 1 μM 5 days; log2 fold-changes 1 . 37–2 . 57 ) . Bm9776 encodes a hypothetical protein and was also significantly up-regulated at four time points between both studies ( log2 fold-changes 1 . 06–1 . 85 ) . Lastly , Bm6450 is annotated as a GTPase activating protein for Arf containing protein and was found to be down-regulated across three different time points between both studies ( log2 fold-changes -1 . 06 to -1 . 43 ) .
Dyneins and kinesins play important roles in mitosis and meiosis [38–40] and both were differentially expressed by exposure to IVM . CG7507-PA ( Bm5897 ) , orthologous to dynein heavy chain-1 in C . elegans , was down-regulated after IVM exposure . These molecular motors use ATP to move along microtubules and function in carrying cargo such as vesicles and organelles which are too large to diffuse to other cellular destinations [41] . In C . elegans , dynein is associated with nuclear envelopes , centrosomes and meiotic and mitotic spindle poles [42] . Kinesin-1 and cytoplasmic dynein function together to move the meiotic spindle to the oocyte cortex [39] . During anaphase , the meiotic spindle is attached to the cortex by one pole , allowing selective disposal of half the chromosomes in a polar body [39] . In C . elegans , dynein light chain 1 ( dylt-1 ) is expressed in adult body wall muscle , larval and adult pharynx , posterior intestine and nervous system and is required for normal embryonic and larval viability . The GO term “establishment of meiotic spindle orientation” was enriched in this study . One gene associated with this term was Bm2777 ( IQ calmodulin-binding motif family protein ) , a homolog of aspm-1 in C . elegans and mammalian ASPM ( Abnormal Spindles & Primary Microcephaly ) [43] . ASPM-1 is required for oocyte meiotic spindle pole assembly and is essential for the meiotic spindles to align orthogonally and in close proximity to the overlying plasma membrane [43] . Another gene that may play a role in meiosis is Bma-zyg-9 ( Bm3504 ) , which was down-regulated at the two highest IVM concentrations . In C . elegans , ZYG-9 acts to ensure correct microtubule assembly throughout the cell cycle of early embryos during mitosis , interphase and meiosis [44–46] . A major facilitator superfamily protein ( Bm6076 ) , down-regulated at 5 days at the two highest IVM concentrations , is highly expressed in the digestive tract of B . malayi [47] . In C . elegans , RNAi phenotypes associated with the ortholog of Bm6076 include “embryonic lethal” , “asymmetric cell division defective early embryo” , and “spindle pole pulling force variant” , indicating that this gene is probably important for embryo development . Calcium signaling plays a crucial role in reproduction and fertilization and has been extensively studied in C . elegans [48] . During meiotic maturation of the oocyte , calcium signaling is linked to inositol 1 , 4 , 5-triphosphate receptor ( ITR-1 ) , N-methyl D-aspartate type glutamate receptor subunit ( NMR-1 ) and UNC-43 , which encodes a type II calcium/calmodulin-dependent protein kinase [49] . Phospholipase C catalyzes the hydrolysis of phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) into diacylglycerol ( DAG ) and inositol ( 1 , 4 , 5 ) triphosphate ( IP3 ) [50] . IP3 binds receptors on the endoplasmic reticulum to mobilize Ca2+ into the cytosol . We observed down-regulation of a phospholipase C homolog ( Bm3716 ) and of 1-phosphatidylinositol-4 , 5-bisphosphate phosphodiesterase beta 4 ( Bm1768 ) . The latter is an ortholog of egl-8 ( egg-laying defective ) and is predicted to have phosphatidylinositol phospholipase C activity and Ca2+ ion binding activity . Interestingly , EGL-8 also plays a role in regulating the release of acetylcholine from motor neurons , thus affecting locomotion , and is expressed in all neurons in C . elegans [51] . One of the most down-regulated DE genes we observed was inositol-1 ( Bm11145 ) , which is required for the biosynthesis of phosphatidylinositol [52] . Cumulatively , down-regulation of these genes suggests that Ca2+ signaling ( via IP3 ) may be affected following exposure to IVM , with adverse effects on ovulation . IVM selection on β-tubulin has been reported in O . volvulus and H . contortus; IVM selection changes the frequency of β-tubulin alleles [53] . Microtubules , which are assembled from heterodimers of α- and β-tubulin , drive chromosome motion in mitosis and meiosis [54] , and so IVM selection for β-tubulin changes could have downstream effects on meiosis . A recent study in H . contortus has also shown that IVM binds to and alter the tubulin polymerization equilibrium , which can lead to mitotic arrest [55] . A number of other genes that function in fertilization were dysregulated by IVM exposure . Bma-emo-1 ( Bm14080 ) , which is involved in ovulation , intracellular protein transport and oocyte growth in C . elegans , was down-regulated . C . elegans emo-1 mutants fail to ovulate after meiotic maturation and oocytes remain trapped in the gonad arm and become endomitotic [56] . Fanconi anemia complementation group D2 protein ( Bm6522 ) was decreased following IVM treatment; deletion of the C . elegans ortholog results in egg-laying defects , precocious oogenesis , and partial defects in fertilization , and may play a role in double-stranded DNA break repair during embryogenesis [57] . IVM exposure downregulated the expression of several genes involved in RNA transport , with consequences on embryo development . Nuclear pore complexes ( NPCs ) are highly conserved proteinaceous structures embedded in the nuclear envelope that provide a connection between the nucleus and the cytoplasm [63] . Several genes ( Bm6397 , Bm9537 , Bm5288 ) orthologous to NPC proteins in C . elegans ( npp-13 , npp-14 and npp-21 respectively ) were down-regulated . Nucleoporins comprise NPCs and may function in transcriptional events , mRNA export , and genome organization [58] . In the nucleoplasm , dynamic nucleoporins may be functioning in the activation of developmental genes [58] . A subclass of nucleoporins plays a role in orienting the mitotic spindle in C . elegans embryos , and RNAi depletion of any of these nucleoporins leads to spindle orientation defects [64] . One of these nucleoporins is NPP-13 . NPP-21 , an ortholog of human TPR ( translocated promoter region ) , is also involved in embryo development , locomotion and nematode larval development; RNAi knockdown results in embryonic lethal , larval arrest and locomotion variant phenotypes [59] . No obvious RNAi phenotypes have been reported for NPP-14 . Lastly , we observed down-regulation of Bm5608 , which is orthologous to mel-46 ( maternal effect lethal ) . In C . elegans , this gene encodes a DEAD-box protein required in the germ line for proper oogenesis and zygotically for post-embryonic development [60] . Interestingly , we saw a significant down-regulation of Bma-nep-1 ( Bm7419 ) , which has 45 . 8% identity to nep-1 in C . elegans . Neprilysins are transmembrane zinc-metalloproteases that have roles in cell-to-cell signaling [61] . The expression pattern of nep-1 in C . elegans is limited to pharyngeal cells and a single head neuron; it is an effector of locomotion and pharyngeal pumping [62] . The locomotion of nep-1 knockouts is significantly impaired and RNAi knockdown results in a maternal sterile phenotype [62] . We noted a significant enrichment of dysregulated genes involved in oxidative phosphorylation ( 100 nM , 48 h ) . Expression of ubiquinol-cytochrome C reductase ( Bm6047 ) , succinate dehydrogenase ( Bm3783 ) , ATP synthase epsilon chain ( Bm3758 ) , ATP synthase f chain ( Bm5403 ) , NADH dehydrogenase subunit 1 ( Bm10539 ) and cytochrome b5-like heme/steroid binding domain containing protein ( Bm2497 ) was down-regulated . In contrast , NADH dehydrogenase subunit 1 was up-regulated at 1 μM ( 48 h ) . The down-regulation of these genes may suggest that ATP synthesis is directly or indirectly inhibited by IVM . Abamectin ( ABA ) , another ML in the avermectin family , caused concentration-dependent ( 5–25 μM ) inhibition of the respiratory chain [63] . Reduced expression of ribosomal proteins , histones , and prosaposins [64] are a signature of oxidative stress in C . elegans , but the homologs of these genes were not DE or only marginally so in our study . In nematodes , ATP is obtained by glycolysis or oxidative phosphorylation , using a variety of substrates [65 , 66] . Several anthelmintics have been proposed to target helminth oxidative phosphorylation enzymes [67] . IVM binding to GluCls induces a slow onset , pseudo-irreversible glutamate-gated current [10 , 68 , 69] . Given the nature of the drug-receptor association , one can speculate on the necessity for a new cohort of GluCls to be expressed to overcome the effects of IVM . Little is known about the rate of receptor turn-over and synthesis in adult parasitic nematodes , but this scenario could contribute to the prolonged sterilization of adult filariae observed after a single dose of IVM . In this context , it might have been expected that this drug-receptor interaction would lead to increased expression of GluCl genes , which was not observed; expression of the putative receptor for IVM , Bm-avr-14 ( Bm1710 isoforms ) [10] , was not dysregulated following exposure to the drug . GluCl-encoding genes are abundantly expressed in B . malayi female reproductive tissue ( among other body parts , such as the pharynx ) , suggesting that the sterilizing effects of IVM may occur at a very early embryonic stage [14] . In males , GluCl expression in reproductive tissues was less abundant , but was localized to some extent to somatic muscle and the vas deferens , likely controlling body movement as well as sperm cell development and release [14] . Using a high-level IVM-resistant C . elegans strain ( the strain DA1316 , which has null mutations in three GluCl subunits: avr-14; avr-15; glc-1 ) [70] , no differential expression was noted in the genes encoding the GluCl subunits avr-14 or avr-15 , whereas a log fold-change of 0 . 78 ( FDR = 0 . 06 ) in glc-1 was observed following treatment with 1 . 1 μM IVM . Exposure to the drug impaired pharyngeal pumping to some extent in that strain . The authors concluded that IVM induced differential expression of several genes in response to food deprivation , which are likely unrelated to GluCl expression [70] . Twenty-nine cuticle collagen genes showed differential expression across both studies , of which 17 were significantly down-regulated following exposure to IVM . Previously , we examined the effect of in vitro cultivation , without anthelmintic , on the transcriptome of B . malayi and found 21 cuticle collagen genes to be significantly DE over time [20] . Thirteen of the DE collagen genes detected in the current work overlapped with dysregulated genes from the previous study ( Bm11024 , Bm11095 , Bm11338 , Bm1249 , Bm2110 , Bm2605 , Bm2854 , Bm4605 , Bm7408 , Bm7894 , Bm8043 , Bm8439 and Bm9021 ) . Bm11024 and Bm11095 were significantly down-regulated in the in vitro cultivation study at 48 h in culture , yet in the present work , we found these genes to be up-regulated at 300 nM IVM for 5 days . Collagen gene dysregulation likely represents a non-specific marker of stress [20 , 64] . Scanning electron microscopy has shown that IVM exposure leads to damage of the surface of B . malayi third-stage larvae , inducing the loss of regular cuticular annulations and causing morphological changes in the cuticular surface in the head , body , and tail regions [71] . Previously , we observed the pronounced up-regulation of a serpin precursor ( Bm9380 ) , and of a hypothetical protein ( Bm337 ) , attributed to in vitro cultivation [20] . In the current study , both genes were up-regulated , but to a much lower extent . This suggests that our untreated control corrects , to some extent , for differential gene expression solely due to life in liquid culture media . We compared the IVM transcriptional response in C . elegans to B . malayi to look for common GluCl-independent , IVM-dependent gene expression signatures . The transcriptional response to IVM in C . elegans using whole genome microarray led to the identification of genes involved in early food deprivation response ( i . e . , fat mobilization ) [70] . Using the DA1316 mutant strain ( null mutations in three GluCl subunits ) , which is resistant to the paralytic effects of IVM on the body wall , worms were exposed to 100 nM or 1 . 1 μM IVM for 4 h . This resulted in the up-regulation of 3 genes and down-regulation of 12 genes in the 100 nM IVM group , with 216 up-regulated and 153 down-regulated in the 1 . 1 μM group . Comparing our dataset with the C . elegans study showed that only 5 genes overlapped , of which 2 were significantly down-regulated in both studies: 6-pyruvoyl tetrahydropterin synthase ( Bm1701 ) and cytochrome b5-like heme/steroid binding domain containing protein ( Bm2497 ) . We have shown that IVM exposure alters the expression of genes that are likely to function in the B . malayi female reproductive system . Through several biological pathways , genes involved in meiosis were particularly affected . These findings provide some insight into the mechanisms involved in IVM-induced reduction in mf output and impaired fertility , embryogenesis , and larval development and the transcriptomic dataset provides important experimental avenues to pursue the mechanism by which IVM sterilizes worms . This is also the first report of IVM exposure having an effect on oxidative phosphorylation at the transcript level , a metabolic pathway believed to be vital for nematodes . | Lymphatic filariasis and onchocerciasis are tropical diseases caused by infections with parasitic nematodes . Resulting chronic diseases can be strongly blinding and disfiguring , and contribute to an entrenched cycle of poverty in affected populations . Ivermectin is one of the pivotal drugs used to control these infections . The mechanism of antifilarial action of the drug is incompletely resolved . It kills circulating larval stages ( microfilariae ) , but only reversibly sterilizes adult worms without killing them . Our limited understanding of the involved mechanisms hampers treatment optimization and sustainability of the efficacy of the drug , and investigations into its pharmacology are of paramount importance . Working with Brugia malayi adult females , we employed RNA sequencing and bioinformatics analyses to identify genes for which expression levels changed as a result of exposure to the drug in vitro . Ivermectin exposure altered the expression of genes that are likely to function in the B . malayi female reproductive system even at the lowest concentration tested . Through several biological pathways , genes involved in meiosis were particularly affected . These findings provide some insight into the mechanisms involved in ivermectin-induced reduction in microfilaria output and impaired fertility , embryogenesis , and larval development . | [
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"analy... | 2016 | The Effects of Ivermectin on Brugia malayi Females In Vitro: A Transcriptomic Approach |
Improved serodiagnostic tests for typhoid fever ( TF ) are needed for surveillance , to facilitate patient management , curb antibiotic resistance , and inform public health programs . To address this need , IgA , IgM and IgG ELISAs using Salmonella enterica serovar Typhi ( S . Typhi ) lipopolysaccharide ( LPS ) and hemolysin E ( t1477 ) protein were conducted on 86 Nigerian pediatric TF and 29 non-typhoidal Salmonella ( NTS ) cases , 178 culture-negative febrile cases , 28 “other” ( i . e . , non-Salmonella ) pediatric infections , and 48 healthy Nigerian children . The best discrimination was achieved between TF and healthy children . LPS-specific IgA and IgM provided receiver operator characteristic areas under the curve ( ROC AUC ) values of 0 . 963 and 0 . 968 , respectively , and 0 . 978 for IgA+M combined . Similar performance was achieved with t1477-specific IgA and IgM ( 0 . 968 and 0 . 968 , respectively; 0 . 976 combined ) . IgG against LPS and t1477 was less accurate for discriminating these groups , possibly as a consequence of previous exposure , although ROC AUC values were still high ( 0 . 928 and 0 . 932 , respectively ) . Importantly , discrimination between TF and children with other infections was maintained by LPS-specific IgA and IgM ( AUC = 0 . 903 and 0 . 934 , respectively; 0 . 938 combined ) , and slightly reduced for IgG ( 0 . 909 ) , while t1477-specific IgG performed best ( 0 . 914 ) . A similar pattern was seen when comparing TF with other infections from outside Nigeria . The t1477 may be recognized by cross-reactive antibodies from other acute infections , although a robust IgG response may provide some diagnostic utility in populations where incidence of other infections is low , such as in children . The data are consistent with IgA and IgM against S . Typhi LPS being specific markers of acute TF .
Salmonelloses are a group of potentially fatal bacteremias caused by different serovars of Salmonella enterica . Typhoid fever ( TF ) , caused by the human-specific serovar S . Typhi , is a global health problem , especially in developing countries [1 , 2] . In 2010 there were an estimated 26 . 9 million TF episodes worldwide , with a case-fatality rate of ~ 1% [3] . Salmonellosis caused by nontyphoid Salmonella ( NTS ) serovars are caused predominantly by the zoonotic serovars , S . Typhimurium and S . Enteritidis [4–7] . These are emerging in sub-Saharan Africa as an important cause of bacteremia in young children , typically when associated with malnutrition , malaria , severe anemia , and/or HIV co-infection [6 , 8–11] . Case-fatality rates for blood-borne , or invasive , NTS ( iNTS ) infection is higher than that for typhoid , typically ~20% [4 , 6 , 12] , although the antibiotic treatment regimen is the same . Over-use of empiric broad-spectrum antibiotic treatment for undifferentiated febrile disease has led to an increase in antibiotic resistance in both typhoidal and nontyphoidal serovars , and the potential for new antibiotics is not encouraging [1 , 13 , 14] . The development of effective vaccines to prevent invasive salmonellosis is therefore an important global health priority [1 , 15] Accurate diagnosis of salmonellosis remains a challenge in endemic settings . Clinically , initial presentation with typhoid or NTS disease is usually with non-differentiating fever alone , and often without symptoms of gastroenteritis that would indicate a Salmonella infection [7] . Bacterial culture is the gold standard for diagnosis of both typhoid and iNTS disease . However , culture suffers from poor sensitivity , and culture facilities are very limited in resource poor settings such as Nigeria and other countries in Africa . Even when such facilities are available , the time to a laboratory diagnosis is around 48 hours , and is often unaffordable for most patients . An inactivated-Salmonella agglutination test , developed by Widal >100 years ago , is a rapid and affordable single-step test . It remains the mainstay of diagnosis in many developing countries , even when culture facilities are available . However , the Widal’s test has poor specificity , thought to be caused by antigens shared between S . enterica serovars , and between other species of bacteria , such as Brucella melitnesis [16] . The Widal’s test also fails to discriminate between current and previous exposure , thus requiring two samples to be taken 7–10 days apart to monitor for an increase in titer . In practice , the decision to treat with antibiotics has to be made on the basis of the first test , and confirmatory convalescent testing is often not practicable or irrelevant for immediate patient management . It is also less sensitive in the acute stage of infection when IgG titers are lower . The lack of accurate tests for surveillance also has resulted in only limited understanding of epidemiology of salmonellosis in Africa . The high mortality , particularly in children with iNTS infections , and the recent emergence of drug resistance , emphasize the need for a better understanding of the epidemiology before the rational design and implementation of control measures , including vaccines , can be effectively deployed . In this study we have addressed the development of improved serodiagnostics with well-defined serum samples collected from febrile children in Nigeria . Based on proteome microarray screening data published recently [17] , we hypothesized that LPS and/or the hemolysin E ( HylE , t1477 ) antigen may have diagnostic utility for TF . However , it was unknown from the original study if these antigens were cross-reactive for other bacteremias . Here we have evaluated IgG , IgM and IgA ELISAs using purified S . Typhi LPS and HlyE using culture-confirmed pediatric bacteremias , including typhoid , iNTS disease , and ‘other’ febrile diseases , as well as healthy Nigerian children , and healthy adults from the U . S . We find LPS-specific IgA and IgA+M ELISA , in particular , are sensitive in diagnosing acute typhoid in these children , and descriminate well between typhoid and healthy , and other febrile bacteremias commonly encountered in Nigeria .
In a previous study [17] we confirmed the potential utility of S . Typhi LPS-specific IgA for the diagnosis of acute typhoid in sera from Nigerian children . Before pursuing this further , we wished to investigate potential cross-recognition of LPS by antibodies from other acute infections that might lead to a false-positive diagnosis of typhoid . For this we conducted a pilot study using a microarray displaying LPS from 7 different bacterial pathogens ( S . Typhi , S . Typhimurium , F . tularensis , B . pseudomallei , B . melitensis , V . cholerae and E . coli ) , and probed it with sera from Nigerian pediatric samples and adult samples available from other febrile diseases , and controls . The data for IgA and IgG reactivity are summarized in the box plots in Figs 1 and 2 , respectively . Panels A-D show Nigerian pediatric samples . As reported previously , IgA reactivity for S . Typhi LPS was strongest in typhoid cases ( N = 16; Fig 1A ) , largely absent from ‘No Growth’ ( N = 16; Fig 1C ) and healthy control ( N = 16; Fig 1D ) samples , while present in a few individuals with culture-confirmed NTS ( N = 16; Fig 1B ) presumably owing to the antigenic similarities between LPS from related Salmonella serovars . Although there is a range of signals from the typhoid cases , only one sample was negative . We then examined the reactivity of sera from other bacteremias for other locations outside Nigeria , as follows: tularemia from Spain ( N = 12; Fig 1E ) , melioidosis from Thailand ( N = 7 acute , and N = 7 convalescent; Fig 1F ) , brucellosis from Peru ( N = 12 acute , and N = 16 convalescent; Fig 1G ) , cholera from Bangladesh ( N = 7 acute , and N = 7 convalescent; Fig 1H ) , and C . difficile infections ( CDI ) from the UK ( N = 16; Fig 1I ) . Also probed were malaria samples from Mali , PNG and Kenya ( N = 16; Fig 1J ) and healthy controls from the U . S . ( N = 20; Fig 1K ) . With the exception of two melioidosis cases and two malaria cases , IgA from these other infections did not cross-react with S . Typhi or S . Typhimurium LPS in this study . Of note , IgA from other gram negative bacteremias did recognize the LPS appropriate to the infecting organism . Thus , IgA in individuals with acute tularemia specifically recognized F . tularensis LPS ( Fig 1E ) , melioidosis IgA specifically recognized B . pseudomallei LPS ( Fig 1F ) , brucellosis IgA specifically recognized LPS from B . melitensis ( Fig 1G ) and cholera IgA specifically recognized V . cholerae LPS ( Fig 1H ) . This shows the lack of cross-reactivity for S . Typhi LPS was not due to a lack of anti-LPS antibodies in these other infections . In several cases , the signal intensity of the LPS-specific IgA response correlated with stage of infection . For example , of the 12 tularemia samples , the six 2nd time-point samples after MA seroconversion ( late acute stage ) gave maximal signals against F . tularensis LPS , while the remaining samples were the 1st time point prior to seroconversion ( early acute ) . For IgG , the most robust signals against S . Typhi and S . Typhimurium LPS were from the Nigerian acute typhoid cases . However , IgG was not a reliable marker of acute typhoid in Nigerian children in this array study . IgG against these antigens were particularly common among all the samples tested , but particularly in the Thai melioidosis , Peruvian brucellosis , malaria samples from various locations ( Fig 2F , 2G and 2J , respectively ) , and the US negative controls ( Fig 2K ) , consistent with the exposure to Salmonella species being widespread globally . These data are consistent with IgG being associated with both acute and convalescent ( previous ) exposure . Indeed , most individuals tested had IgG to multiple LPS species . For example , many of the Nigerian individuals in panels A-D also have LPS-specific IgG to E . coli and B . pseudomallei , which may indicate a previous exposure to these organisms and/or cross-reactivity from other infections . Antibodies against F . tularenisis are also quite common among different populations where tularemia is non-endemic ( e . g . , U . S ) , and may reflect cross-recognition of antibodies to other non-pathogenic Francisella species [18] . The data from the pilot study described above indicated that LPS-specific IgA may have utility for discriminating between acute and convalescent typhoid or other acute infections . While a more deployable array format is currently under development [19] , in parallel we decided to develop an ELISA test for typhoid . The ELISA is inexpensive , robust and provides results more quickly than blood culture . Two batches of HlyE were used in the course of this study which , when compared by ELISA using all 349 Nigerian samples correlated with an r2 = 0 . 922 and a slope = 1 . 05 using Spearmann’s rank correlation ( S1 Fig ) . The optimal conditions for ELISAs were initially determined using individual serum samples from three Nigerian typhoid patients and a healthy control . The optimized concentrations of the coating antigens were determined by titration to be 1 . 25μg/ml for LPS , and 2 . 5μg/ml for HlyE ( t1477 ) . Two serum dilutions , 1/100 and 1/200 , were evaluated for the highest ratio when comparing heathy controls and culture-confirmed typhoid . For t1477 ELISA , 1/100 was selected , while for LPS ELISA , 1/200 was found to give the higher ratio and selected for subsequent studies . Two secondary antibody dilutions recommended by the manufacturer , 1/12 , 500 and 1/25 , 000 , were evaluated for optimal signal to background ratio . For IgA and IgM ELISA , 1/12500 dilution was selected , while for IgG ELISA 1/25000 dilution was used . Once established , a standard operating procedure was used throughout the study . Batches of ELISA plates were prepared by pre-coating plates with antigen , blocking , and then storing dried at 4°C in desiccated pouches until required for use . A total of 495 serum samples were used ( Table 1 ) and tested for LPS-specific IgG , IgA , IgM and IgA+IgM in separate ELISAs . The samples comprised 369 Nigerian pediatric samples , consisting of culture-confirmed typhoid ( “S . Typhi” , n = 86 ) , non-typhoid Salmonella ( “NTS” ) disease ( n = 29 ) or other bacteremias ( “Other” , n = 28; listed in Table 2 ) , as well as febrile cases that were blood culture-negative for any bacteria ( “No Growth” , n = 178 ) , and healthy Nigerian control children ( “Healthy” , n = 48 ) . Also tested by ELISA were well-defined sera from tularemia , brucellosis , and malaria cases , as well as U . S . controls . Results of all ELISAs are summarized as box plots in Fig 3; the same data are also presented as bar charts in Supporting Information S2 Fig . In IgA ELISAs , the median OD value of the typhoid group was statistically different from all other groups when tested by the Wilcoxon method . Of these other groups , the NTS disease group showed the highest reactivity , due presumably to antibodies to LPS from NTS serovars cross-reacting with LPS from S . Typhi . While the difference between the medians of the S . Typhi and “No Growth” groups were highly significant ( P < 0 . 0001 ) , the latter group contained a large number of outliers that may correspond to blood culture false-negatives . Significantly , none of the 48 healthy Nigerian controls or 28 Nigerian “other” infections were seropositive for LPS IgA . Reactivity by non-Nigerian other infection groups was generally very low , although some Peruvian brucellosis cases had reactivity or cross-reactivity to S . Typhi LPS . There were two outliers in the malaria group ( N = 48 ) with an LPS-IgA response . Although it is not known whether these individuals had a co-infection with Salmonella , association of malaria with salmonellosis is well known to the medical community . The IgG response to LPS ( Fig 3B ) was elevated in all groups , consistent with widespread previous exposure to Salmonella sp . The P-values for NTS , tularemia and brucellosis were larger than for IgA , with brucellosis failing to reach significance . Overall , the pattern of reactivity by IgM to LPS ( Fig 3C ) was similar to that of IgA , with the notable exception of NTS which was not significantly different to typhoid cases . As for IgA , IgM reactivity in healthy Nigerian children was very low , whereas IgM reactivity by the ‘other’ infections from Nigeria and elsewhere were overall higher than for IgA . Overall , the data indicate that the LPS-specific IgA has the best potential of the three isotypes for the diagnosis of acute typhoid from other febrile diseases . Previous experiments using a S . Typhi full proteome array [17] revealed very few protein antigens with utility for diagnosing typhoid fever in Nigerian children . However , the hemolysin E protein ( HylE , t1477 ) did emerge as a potential candidate , and is examined further here and in the following section for sensitivity and specificity using the full serum collection ( N = 495 as described for LPS above ) by ELISA for IgA , IgG and IgM ( Fig 4 ) . The same data are also presented as bar charts in Supporting Information S3 Fig . Overall , IgA reactivity was low among all the groups . Nevertheless , the ‘S . Typhi’ and ‘No Growth’ groups had the largest number of seropositive individuals ( Fig 4A ) . IgA-responses to t1477 provided better discrimination between ‘S . Typhi’ and ‘NTS’ groups , although sensitivity of detection in both groups was low ( detailed in the next section ) . By comparison , the IgG response to t1477 was elevated in all groups ( Fig 4B ) . The highest median IgG signal was seen in the pediatric typhoid group , with the ‘No growth’ and ‘NTS’ groups having the next highest signals overall . Interestingly the Nigerian healthy children were the lowest of the Nigerian groups , although there were a number of outliers with IgG signals . IgG alone does not allow discrimination between ongoing or previous episodes of typhoid , although the negligible reactivity by this group to LPS by Ig of any isotype tested ( seen in Fig 3 ) would support the notion the outliers with IgG responses to t1477 are convalescent cases . The IgM reactivity against t1477 reflected the IgA response . A notable exception was broadly similar levels of IgM reactivity by ‘S . Typhi’ , and ‘No Growth’ groups . This contrasts with the highly significant difference seen when LPS-specific IgM was measured ( Fig 3C ) . This appears to be caused by the reduced sensitivity for detection of typhoid by t1477-specific IgM , rather than any increase in sensitivity for detection of potential typhoid cases among the ‘No Growth’ group . As with LPS-specific IgM , t1477-specific IgM did not discriminate well between typhoid and ‘NTS’ groups , although again , this appears to be caused by the reduced sensitivity for detection of typhoid . The accuracy of LPS and t1477 ELISAs to discriminate between Nigerian pediatric S . Typhi patients and controls were determined by ROC analysis . Plots of true positive rate ( sensitivity ) and false positive rate ( 1-specificity ) for discriminating between typhoid cases and healthy children are shown for LPS and t1477 in Fig 5A and 5B , respectively . Table 3 shows corresponding percent specificity and sensitivity with either set at 90% , and areas under the curve ( AUC ) . With LPS , IgA and IgM both gave 94% sensitivity ( at fixed specificity ) when used alone , which was increased slightly ( to 95% ) by combining the detection of IgA and IgM in the assay . Combining IgA and IgM could also be achieved in silico by summing the OD450nm data for IgA and IgM ELISAs performed individually ( S4 Fig ) . LPS-specific IgA and IgM also give identical specificity when used alone ( 98% at fixed sensitivity ) which was unchanged by combining both isotypes . The AUC of IgA and IgM were very similar ( 0 . 963 and 0 . 968 , respectively ) and increased slightly ( 0 . 978 ) after combining . Despite similar performance of IgA and IgM in the ROC analysis , the IgA ELISAs were characterized by lower backgrounds in the control groups compared to IgM , as can be seen from the raw data in S2 Fig . In contrast , LPS-specific IgG provided the lowest accuracy for distinguishing typhoid cases from healthy controls . In the t1477 ELISAs , although AUC values of IgA and IgM were identical ( 0 . 968 ) , IgA provided superior sensitivity than IgM ( 94% and 86% , respectively , at fixed specificity ) and specificity ( 96% and 88% , respectively , at fixed sensitivity ) . Multiplexing IgA and IgM did not increase sensitivity or specificity over IgA alone , although there was a modest increase in AUC ( to 0 . 976 ) . As with LPS , t1477-specific IgG also gave lower accuracy than IgA or IgM for diagnosing acute typhoid . These data indicate both LPS and t1477-specific IgA and IgM provide good discrimination between healthy Nigerian children and those with acute typhoid fever , which is improved by detection of both IgA and IgM isotypes together . We then compared acute typhoid with 28 Nigerian ‘other’ ( non-Salmonella ) infections ( listed in Table 2 ) , since this is more relevant to the diagnosis of typhoid in the clinical setting . ROC Plots are shown in Fig 5C and 5D , with corresponding AUC , and percent sensitivity and specificity given in Table 4 . Here , LPS-specific IgA and IgM give comparable sensitivity when used alone ( 86% and 87% , respectively , at fixed specificity ) , which is increased to 90% when IgA and IgM are combined . LPS-specific IgM provided considerably greater specificity than IgA when used alone ( 82% and 75% , respectively , at fixed sensitivity ) , which is dramatically increased ( to 96% ) when combined . The AUC is also increased slightly by combining IgA and IgM to 0 . 938 . LPS-specific IgG provides the lowest sensitivity and specificity of all three isotypes . By comparison , the relative accuracy of t1477 in ELISAs for diagnosing acute typhoid was lower for all three Ig isotypes compared to LPS , and also reduced relative to discrimination of typhoid vs . healthy controls . Unexpectedly IgG emerged as the isotype with the highest sensitivity and specificity of t1477-specific Igs . It is possible this is restricted to childhood , where there is relatively less lifetime exposure to Salmonella than older children and adults , combined with a robust IgG response during typhoid fever . We also compared the ability of LPS and t1477 to discriminate between Nigerian pediatric typhoid and additional samples from ‘other’ non-Salmonella infections obtained from locations outside Nigeria , namely tularemia ( Spain , N = 12 ) , brucellosis ( Peru , N = 16 ) and malaria ( various sources , N = 48 ) . ROC plots are shown in Fig 6A ( LPS ) and 6B ( t1477 ) , with corresponding AUC and percent sensitivity and specificity given in Table 5 . Data were broadly similar to that seen with Nigerian ‘other’ infections , with combined IgA+IgM providing the most accurate test when using LPS , and IgG providing the most accurate test when using t1477 . As noted earlier , brucellosis samples were prominent among the ‘other’ infections for cross-reactivity to S . Typhi LPS . If these samples were removed from the analysis ( Fig 6C and 6D ) there was a slight increase in sensitivity and specificity in almost all situations , with the exception of t1477-specific IgG ( Table 5 , values in parenthesis ) . Finally we explored the effect of multiplexing LPS and t1477 antigens on the accuracy of the test for typhoid compared to each antigen alone ( Fig 7 and Table 6 ) . Multiplexing LPS IgA with t1477IgG in silico increased accuracy compared to either alone , while multiplexing LPS IgA+IgM ( as mixed secondary antibodies ) with t1477 IgG in silico increased the accuracy further .
In countries in Sub-Saharan Africa , where typhoid and non-typhoidal salmonellosis are major causes of bacterial sepsis in children , accurate and rapid point-of-care tests are urgently needed to replace existing diagnostic methods . Culture of S . Typhi organisms from bone marrow is the gold standard , but because it is invasive , blood culture is often a more practical , albeit less sensitive , alternative . Blood or bone marrow culture is also slow ( 2–3 days to arrive at a diagnosis ) , and empiric broad-spectrum antibiotic treatment is often initiated without a diagnosis being made . The traditional Widal’s test , which is based on the agglutination of inactivated Salmonella Typhi and Paratyphi A organisms by antibodies to flagellin and LPS ( H and O antigens , respectively ) is rapid , inexpensive and requires no instrumentation . However , interpretation of the results must be made with caution . Sensitivity of the Widal’s test is lower in the early stage of infection when antibody titers are low . The test also fails to discriminate between acute from convalescent infection , leading to reduced sensitivity in endemic settings [20] . Although sensitivity can be improved if a follow-up sample is tested [21] , this is not an option for rapid diagnosis . The test also lacks specificity owing to cross-reactivity with antibodies against closely-related NTS serovars [22] and other bacteria , notably Brucella [16] . Misuse of the Widal’s test has contributed to over-diagnosis of Salmonella infection , inappropriate antibiotic use , and the emergence of drug resistance [23] . Recent alternatives for serodiagnosis of typhoid include the Tubex test for LPS-specific IgM and the Typhidot test for IgG or IgM against a 50kDa outer membrane protein [24] . The Tubex test format is based on the interference by patient serum antibodies with the agglutination of latex beads coated with O9-specific monoclonal antibody and S . Typhi LPS-coated magnetic beads . The Typhidot test is a pre-dotted antigen strip . Neither test is currently configured for detection of IgA . Both have been evaluated in several Asian and African study sites; Tubex and Typhidot show comparable performance and were more specific although less sensitive than the Widal test ( http://www . who . int/bulletin/volumes/89/9/11-087627/en/ ) . In this study we have focused on the use of LPS and t1477 ( hemolysin E ) as antigens to discriminate between S . Typhi infection and other bacterial infections , including commonly-encountered bacteremia seen in Nigeria . LPS has long been recognized as dominant in the response to Salmonella , while the identity of t1477 has come from studies using proteome-wide serological screens using microrrays [17 , 25 , 26] . Although the microarray has the potential to diagnose multiple infectious diseases on a single chip , it is currently unsuitable as a point-of-care test for many clinics in its current format . An accurate , more deployable test , particularly if configured into a format able to provide a result in <30 minutes , could help curb the inappropriate use of antibiotics and stem the rise in antibiotic resistance in Nigeria . The data presented here indicate LPS-specific IgA ( or IgA+M combined ) discriminate well between Nigerian children with typhoid and healthy Nigerian children ( AUC = 0 . 963 and 0 . 978 , respectively; Table 3 ) . More importantly for the clinical setting , LPS-specific IgA ( or IgA+M combined ) also discriminates between Nigerian children with typhoid and children with ‘other’ ( non-Salmonella ) infections ( AUC = 0 . 903 and 0 . 938 , respectively; Table 4 ) . Similarly , discrimination between typhoid cases and healthy children using t1477-specific IgA ( Table 3 ) was comparable to that obtained with LPS-specific IgA , although discrimination between typhoid and ‘other’ cases using t1477-specific IgA ( Table 4 ) was far less accurate than for LPS-specific IgA . One possibility is proteins antigenically related to S . Typhi t1477 hemolysin E are found in one or more of the other bacterial infections represented in the collection ( see Table 2 ) . Such potential cross-reactivity would reduce the diagnostic utility of the antigen for typhoid . LPS-specific IgG provided less accuracy for discriminating between Nigerian pediatric typhoid and healthy Nigerian children ( Table 3 ) , which was reduced further when discriminating typhoid with ‘other’ infections ( Table 4 ) . It is possible that IgG titers remain elevated for longer than IgA , thereby making it more difficult to discriminate between acute and previous or convalescent infections using IgG . This may be less of an issue in children where lifetime exposure to Salmonella species will likely be less than in adults . Although we have not examined Nigerian adults in this study , the expectation is they will have higher and more durable IgG titers to both LPS and t1477 than in children . This notion is supported for LPS by the pilot LPS array ( Fig 2 ) in which all the non-Nigerian samples ( i . e . , panels E through K ) were from adults . Thus , the median IgG signal of the healthy Nigerian children was lowest among all the groups tested , including adults from two non-endemic sites , the US ( Fig 2K ) and UK ( Fig 2I ) . It remains to be determined whether LPS- and/or t1477-specific IgA has any utility for diagnosing typhoid in adults . Unexpectedly , t1477-specific IgG performed better than LPS-specific IgG for discriminating between Nigerian pediatric typhoid and healthy Nigerian children ( Table 3 ) , and between Nigerian pediatric typhoid with ‘other’ infections ( Table 4 ) . Indeed , t1477-specific IgG also performed better than t1477-specific IgA and IgM for discriminating between typhoid and ‘other’ infections in Nigerian children . It is possible this diagnostic performance occurs only in children , where there is less lifetime exposure to Salmonella . It is anticipated that t1477-specific IgG will have less utility for diagnosing acute typhoid in older children and adults where the IgG titers from convalescent infections are likely to be much higher . Finally we also compared the ability of LPS and t1477 to discriminate between typhoid and non-Nigerian ‘other’ infections from other locations around the world . In the ELISA , LPS-specific IgA+M provided excellent sensitivity and specificity , although we did notice detection of some Peruvian brucellosis cases using S . Typhi LPS ( Fig 3 and Table 5 ) . There are accounts in the literature of antigenic cross-reactivity between Brucella sp . and S . enterica serotype Urbana [16 , 27 , 28] which raises the possibility of cross-reactivity between antibodies generated during human brucellosis and Salmonella antigens . Although brucellosis is rare in Nigeria , if discrimination between acute typhoid and brucellosis is necessary , one option might be to utilize serodiagnostic B . melitensis antigens discovered previously [29–32] or B . melitensis LPS ( Fig 1G ) to assist in positive identification of brucellosis cases . The accuracy of t1477-specific IgA ( or IgA+M ) was lower than for LPS , consistent with its performance in discriminating between typhoid and Nigerian “other” infections . LPS has received considerable interest as a potential diagnostic antigen for typhoid and for the basis of alternative assays to the Widal’s test . In one longitudinal study [33] , anti-LPS IgA and IgM titers were seen to peak around d11-21 and decline thereafter , whereas IgG titers remained elevated and did not decline as rapidly . Other studies have also shown the transient nature of anti-LPS IgA in typhoid in saliva samples [34 , 35] as well as in sera of gastroenteritis caused by non-typhoidal Salmonella serovars [36] . Thus , LPS-specific IgA appears to be a useful marker of acute Salmonellosis owing to its transient appearance after infection . The transient nature of IgA appears to be a peculiarity of LPS , and possibly other T-independent antigens , since serum and mucosal IgA responses to bacteria are generally long-lived [37–39] . In the present study , the LPS molecule did not discriminate well between typhoid and NTS , presumably because of the presence of shared epitopes present in the conserved lipid A and core oligosaccharide regions [40] . However , the more variant O-polysaccarides where serovar-specific epitopes of the O-antigen are located may discriminate between antibodies engendered by typhoid and NTS serovars . Salmonella O-polysaccharides have been produced from bacterial extracts and conjugated to protein carriers for use as subunit vaccines [41–43] , although their utility as specific diagnostics is less well explored . In one such study , the S . Typhi O-polysaccharide O:1 , 9 , 12 performs well in IgG dot blots as a discriminator between typhoid and other acute infections or healthy controls , although IgA and the ability to discriminate between typhoid and NTS were not examined [44] . Neither the use of LPS nor measurement of IgA for diagnosis of typhoid is novel , but when used together appear to represent a good marker for acute infection in Nigerian children . The t1477/hemolysin E ( HylE , also known as cytolysin A or CylA ) protein is a known dominant antigen in the antibody response to S . Typhi infection [17 , 25 , 26 , 45] . Its utility as a potential serodiagnostic for typhoid has been demonstrated independently in a study of different Ig isotypes in 50 culture-confirmed typhoid cases [46] . In that study , IgA was the most sensitive , detecting 28/50 cases using a cut-off defined by the isotype-matched responses by healthy controls and other febrile infections . IgG was second most sensitive ( 19/50 ) , and IgM least sensitive ( 3/50 ) . A subsequent pilot study has demonstrated the utility of anti-HylE IgA in saliva as a biomarker for acute typhoid fever [47] . S . Typhi HylE is a 302 amino-acid long transmembrane protein with a helix hydrophobic segment located between residues 179 and 199 . Along with homologs in other bacteria , such as the prototypic ClyA in E . coli , S . Typhi HylE belongs to a family of important pore-forming virulence factors of bacterial pathogens that assemble in cell membranes [48] . The HylE gene ( t1477 ) is present in human-specific typhoid serovars ( Typhi and Paratyphi ) but absent from others ( e . g . , S . Typhimurium ) . In Fig 4 , IgA reactivity by the 29 Nigerian children with invasive NTS ( iNTS ) is negligible with the exception of two outliers with low reactivity . However , sensitivity of t1477-specific IgA for detection of typhoid is also low , indicating this antigen is unlikely to have utility for discriminating iNTS and typhoid .
This study was conducted with informed consent and approved by the Ethics Committees of the Federal Capital Territory of Nigeria , Federal Medical Center Keffi , Aminu Kano Teaching Hospital and University of Nebraska Medical Center ( UNMC ) , Omaha Institutional Review Board ( IRB ) . We used written consent provided by parent or guardian of each child . The process was approved by both local IRB and UNMC IRB . Sera from Spanish tularemia cases were provided by Drs . Raquel Escudero and Pedro Anda , Instituto de Salud Carlos III , Madrid , Spain . Human subjects approval from Comité de Bioética y Bienestar Animal , Instituto de Salud Carlos III ( approval no . PI 33 ) . Sera from Thai melioidosis cases were provided by Direk Limmathurotsakul and Narisara Chantratita , Mahidol University , Thailand . Ethical approval for the study was from the Ministry of Public Health , Royal Government of Thailand , and the Oxford Tropical Research Ethics Committee . Sera from Peruvian brucellosis cases were collected with human subjects approval from the Human Research Protection Committee of the University of California San Diego , the Comite de Ética of Universidad Peruana Cayetano Heredia , Lima , Peru , and the Comite de Ética of Asociación Benéfica PRISMA , Lima , Peru . Sera from Bangladeshi cholera cases were provided by Drs . Edward Ryan , Richelle Charles and Firdausi Qadri , Massachusetts General Hospital , Boston , MA . Human subjects approval by IRB protocol # 1999P009116 and International Centre for Diarrhoeal Disease Research , Bangladesh ( ICDDRB ) #PR-11041 . Sera from Clostridium difficile infections were collected with ethical approval from the University of Liverpool Research Ethics Committee ( #08/H1005/32 ) , and each patient provided written informed consent prior to recruitment . Malaria sera were collected with human subjects approvals from Institutional Review Boards at University Hospitals Case Medical Center and the Kenya Medical Research Institute Ethical Review Committee [49] , the Medical Research Advisory Council , PNG [50] , and the Ethics Committee of the Faculty of Medicine , Pharmacy , and Odonto-Stomatology and the Institutional Review Board at the National Institute of Allergy and Infectious Diseases , National Institutes of Health [51] . Sera from healthy US adults were collected under UCI IRB protocol #2007–5896 . Sera were provided to the University of California Irvine ( UCI ) for assay without patient identifiers and were classified as exempt status by the UCI Institutional Review Board . A retrospective study was designed using a convenience series of sera samples from Nigerian pediatric febrile cases and healthy controls , as well as other infectious diseases from other locations outside Nigeria , which were assayed by ELISA and/or LPS microarray ( Table 1 ) . The Nigerian samples were collected between 2009 and 2014 from children aged 8 months—13 years ( median approximately 4 years ) who presented to primary or secondary health centers in central and northwest Nigeria with an acute febrile illness and other symptoms that were suggestive of bacteremia . The duration of symptoms ranged from about 3–10 days with a median of 5 days , as documented in the clinical data captured during enrollment . S . Typhi is the leading cause of childhood bacteremia in this area [52] . Baseline demographics of this population have been described previously [52 , 53] . Following informed consent from the parent or guardian , blood was obtained aseptically from a peripheral vein for blood culture and simultaneously an aliquot for serum separation was saved . Blood sampling and processing were as previously described [52 , 53] . Briefly , only aerobic blood culture bottles were used and held in a Bactec 9050 incubator ( Becton Dickinson , Temse , Belgium ) for a maximum of 5 days . Bacteria were identified by morphology , and for Enterobacteriacae , by use of an API 20 E rapid identification system ( BioMerieux , Marcy-l'Étoile , France ) . Bacterial isolates were stored in skimmed milk at -70°C , and further characterized at the Clinical Microbiology laboratory , University of Nebraska Medical Center . Bacteremia was defined as the isolation of at least 1 noncontaminant bacteria from the admission blood culture . These samples comprised children with typhoid ( N = 86 ) , non-typhoid Salmonella ( NTS ) infections ( N = 29 ) , other bacteremias ( N = 28 ) , and febrile cases that were culture negative ( ‘No Growth’ , N = 178 ) . Samples sizes were determined by availability during the collection period . No samples with missing or indeterminate culture test results were used in this study . In addition , we also obtained sera from healthy Nigerian children enrolled from immunization clinics in the same facilities as controls ( N = 48 ) . These children present for routine immunizations and typically are in a stable state of health . Only children who were asymptomatic and did not have a history of a febrile illness in the past month , or had taken any antibiotic during the same period , were eligible . No blood cultures were performed on the healthy controls . For the pilot LPS array ( detailed below ) , an expanded collection of samples from “other” control infections from other countries were tested in addition to Nigerian samples discussed above , as follows . 1 ) Tularemia sera ( N = 12 ) from a 2007 Spanish outbreak of Francisella tularensis subsp . holarctica . These consisted of paired samples from 6 acute cases that were seronegative by microagglutination ( MA ) test at the 1st time point at presentation and which seroconverted by the 2nd time point approximately 2 weeks later . These samples were found previously to be seropositive for F . tularensis subsp . tularensis ( FTT ) strain Schu S4 antigens at both time points using a proteome microarray [18] . 2 ) Melioidosis sera from Thailand ( N = 14 ) . Samples were collected in 2004 from patients presenting with symptoms of melioidosis , and were diagnosed by indirect hemagglutination assay ( IHA ) and blood and throat swab culture , as described previously [54] . 3 ) Brucellosis sera collected prior to 2008 from an endemic region of Peru ( N = 28 ) , previously shown to be seropositive using a Brucella melitensis proteome microarray [31 , 32] . Samples probed were culture positive/Rose-Bengal positive ( N = 12 ) and culture negative/Rose Bengal positive ( N = 16 ) . These correspond to samples taken on the first day ( acute infection ) and within 6 weeks after obtaining the first sample ( convalescent infection ) . 4 ) Cholera sera from Bangladesh collected between 2008 and 2010 presenting to the International Centre for Diarrhoeal Disease Research , Bangladesh ( ICDDRB ) hospital with acute watery and stool culture confirmed V . cholerae O1 infection . Following informed consent , venous blood was collected from adults ( age 18–55 years ) at the acute phase of infection ( N = 7 ) after clinical stabilization ( day 2 ) , and again at convalescent phases of infection ( d7 and 30; N = 7 ) . 5 ) Sera from Clostridium difficile infections ( CDI ) from diagnosed acute cases in the UK collected between 2008 and 2012 ( N = 16 ) [55] . Each patient was followed-up for minimum period of 30 days initially and then for 1 year from notes for the collection of additional demographics clinical outcome information . 6 ) Symptomatic malaria cases from Kenya , Papua New Guinea and Mali ( N = 48 ) . These were diagnosed with Plasmodium falciparum parasitemia , and all defined as seropositive using different iterations of P . falciparum protein arrays derived from strain 3D7 [56] . 7 ) Healthy US adults from a non-endemic area ( Orange County , CA ) , Adherence to Standards for Reporting of Diagnostic Accuracy Studies ( STARD ) is shown by the flowchart ( S5 Fig ) and checklist ( S1 Text ) in the Supporting Information . Lipopolysaccharides ( LPS ) were obtained as follows: 1 ) LPS from S . Typhosa ( = S . Typhi ) was purchased from Sigma-Aldrich ( Cat . #L2387 ) ; 2 ) LPS from S . Typhimurium was purchased from Sigma-Aldrich ( Cat . #L6511 ) ; 3 ) LPS from Francisella tularensis Subsp . novicida was purified from the live vaccine strain ( LVS ) ( DSTL batch #B07/3564 ) , as described [57]; 4 ) LPS from Burkholderia pseudomallei was purified from strain K96243 ( DSTL batch #B07/3558 ) , as described [58]; 5 ) LPS from Brucella melitensis was purified from strain 16M , as described [31]; 6 ) LPS from V . cholerae O1 was purified from Ogawa ( strain X-25049 ) and Inaba ( strain T19479 ) serotypes , as described [59]; 7 ) Escherichia coli 055:B5 LPS was purchased from Sigma-Aldrich ( Cat . #L2880 ) . Each LPS species was diluted in PBS buffer , pH 7 . 3–7 . 5 ( EMD Millipore Corp . , Billerica , MA; Cat . #6506-OP ) and printed on nitrocellulose-coated glass slides ( Oncyte Avid from Grace Bio-Labs , Bend , OR ) using a GeneMachines Omnigrid 100 array printer , and printed at a concentration of 0 . 1 μg/ml . This concentration was determined previously in titration experiments to be the lowest concentration able to provide near maximal signals . Performers of the LPS microarray assays were blinded to the identity of the samples until after the assays were completed . LPS arrays were probed for 18h at 4°C with sera diluted 1/100 in protein microarray blocking buffer ( Maine Manufacturing , GVS North America , Sanford , ME ) supplemented with E . coli lysate ( Antigen Discovery Inc , Irvine , CA ) . Bound IgG and IgA were then detected using secondary antibodies conjugated to biotin followed by streptavidin conjugated to quantum dots , and then visualized in an ArrayCAMarray imager , as described previously [19] . Hemolysin E protein ( HylE , gene t1477 from S . Typhi Ty2 strain ) was expressed in E . coli and purified as described previously [17] . LPS from S . Typhi was as described above for microarrays . ELISAs were performed as described [60] . Briefly , antigens were coated onto microtiter plates ( ThermoScientific , Walham , MA ) at concentrations 1 . 25 μg/ml ( LPS ) and 2 . 5 μg/ml ( HylE ) in TBS ( 100μl/well ) overnight at 4°C . The coating concentrations were determined previously for each antigen by serial dilution experiments . The following day , plates were washed 4 times in 1x TBS containing 0 . 05% Tween20 ( T-TBS; ThermoScientific ) and blocked with casein/TBS blocking buffer ( ThermoScientific ) for 1-2h ( 300 μl/well ) . Blocking buffer was then decanted , and the plates air-dried and stored in desiccated foil pouches at 4°C until required for use . Performers of the ELISAs were blinded to the identity of the samples until after the assays were completed . For ELISA assay , sera were diluted to 1/200 ( LPS ) and 1/100 ( HylE ) in casein/TBS blocking buffer containing E . coli lysate ( GenScript , Piscataway , NJ ) at 1 . 5 mg/ml final concentration , and incubated for 30 min prior to placing into the plates . Plates were incubated for 45 min with gentle rocking at room temperature ( RT ) . After washing with T-TBS goat anti-human IgG- , IgA- or IgM-HRP conjugates ( Bethyl Laboratories , Inc . , Montgomery , TX ) diluted 1/25 , 000 ( IgG ) or 1/12 , 500 ( IgA , IgM ) in Guardian Stabilizer ( ThermoScientific ) were added to wells ( 100 μl /well ) and incubated for 45 min at RT ( 100 μl/well ) . After washing with T-TBS , plates were developed by adding 100 μl/well SureBlueReserve TMB developer ( Kirkegaard and Perry Laboratories , Inc . , Gaithersburg , MD ) for 10 min in the dark . Development was stopped by addition of 100 μl/well of 0 . 2M H2SO4 and OD read at 450 nm in a Multiskan FC plate reader . ELISA data were collected at OD450nm and data were corrected by the positive control between runs . Dot plots and comparisons between medians of different groups using the Wilcoxon method , were produced in JMP ( SAS Institute , Inc . , Cary , NC , USA ) . Receiver operator characteristic ( ROC ) analyses were performed between patient groups for each antigen with a varying threshold cut off in the R statistical environment using ROCR . Plots of false positive vs . true positive plots were made , from which areas under the curve ( AUC ) and sensitivity and 1-specificity values were calculated for each antigen ( s ) . | In many African countries , clinical management of children that present with symptoms of bacterial sepsis , such as typhoid fever ( TF ) caused by Salmonella Typhi , consists of empiric broad spectrum antibiotics . Blood culture remains the gold-standard for diagnosis , but is slow , suffers from poor sensitivity , and often unavailable . Consequently multi-drug resistant bacteria have emerged that are difficult to manage with antibiotics . There is an urgent need to develop rapid , sensitive and affordable tests for patient diagnosis , help curb antibiotic resistance , and inform public health preventive strategies such as the deployment of vaccines . Here , we have assessed antibodies to S . Typhi lipopolysaccharide ( LPS ) and hemolysin E ( HylE , t1477 ) in the sera of Nigerian children with acute TF and compared them with heathy children , children with other febrile infections , and adults from around the world with a variety of other bacterial infections . The key finding concerns LPS . This is a common cell-wall component present in many bacterial species . Yet despite this , S . Typhi LPS-specific IgA and IgM are excellent markers of acute TF in Nigerian children , and insensitive to other non-salmonelloses . This surprising finding suggests a rapid point-of-care test for TF can be developed based on detection of LPS-specific IgA+IgM . | [
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... | 2017 | Development of ELISAs for diagnosis of acute typhoid fever in Nigerian children |
Porphobilinogen deaminase ( PBGD ) catalyzes the formation of 1-hydroxymethylbilane ( HMB ) , a crucial intermediate in tetrapyrrole biosynthesis , through a step-wise polymerization of four molecules of porphobilinogen ( PBG ) , using a unique dipyrromethane ( DPM ) cofactor . Structural and biochemical studies have suggested residues with catalytic importance , but their specific role in the mechanism and the dynamic behavior of the protein with respect to the growing pyrrole chain remains unknown . Molecular dynamics simulations of the protein through the different stages of pyrrole chain elongation suggested that the compactness of the overall protein decreases progressively with addition of each pyrrole ring . Essential dynamics showed that domains move apart while the cofactor turn region moves towards the second domain , thus creating space for the pyrrole rings added at each stage . Residues of the flexible active site loop play a significant role in its modulation . Steered molecular dynamics was performed to predict the exit mechanism of HMB from PBGD at the end of the catalytic cycle . Based on the force profile and minimal structural changes the proposed path for the exit of HMB is through the space between the domains flanking the active site loop . Residues reported as catalytically important , also play an important role in the exit of HMB . Further , upon removal of HMB , the structure of PBGD gradually relaxes to resemble its initial stage structure , indicating its readiness to resume a new catalytic cycle .
Heme , the second most abundant tetrapyrrole , serves as the cofactor for proteins involved in respiration and metabolism [1] , [2] . Heme is synthesized through a well conserved and established heme biosynthetic pathway in all eukaryotes and most prokaryotes [3] , [4] . Porphobilinogen deaminase ( PBGD ) , an important enzyme in the pathway , is present in most of the organisms . PBGD ( EC 2 . 5 . 1 . 61 ) , a transferase , catalyzes the stepwise polymerization of four molecules of porphobilinogen ( PBG ) into a linear tetrapyrrole 1-hydroxymethylbilane ( HMB ) , preuroporphyrinogen ( Figure 1 ) . PBGD is associated with acute intermittent porphyria , a hereditary autosomal dominant disorder , caused due to mutations in human PBGD ( hPBGD ) resulting in elevated levels of the heme precursors ALA ( 5-aminolevulinic acid ) and PBG in the urine [5] . Dual functionality of PBGD has been reported in Leptospira interrogans [6] and has also been hypothesized in Plasmodium falciparum [7] . In these organisms , PBGD also cyclizes the usual linear preuroporphyrinogen product to uroporphyrinogen III . PBGD has a unique cofactor , dipyrromethane ( DPM ) , which is covalently attached to a conserved cysteine through a thioether bond . This cofactor acts as a primer for the tetrapolymerization of PBG molecules [8] . The chain elongation takes place through repetition of a sequence of steps: ( 1 ) deamination of the incoming PBG substrate , ( 2 ) nucleophilic attack by the α-carbon atom of the terminal pyrrole ring of the enzyme-bound cofactor on the deaminated substrate and ( 3 ) deprotonation [8] ( Figure 1 ) . Five crystal structures of E . coli PBGD ( EcPBGD ) : 1GTK [9] , 1YPN [10] , 2YPN [11] , 1AH5 [12] , 1PDA [8]; two of hPBGD: 3ECR [13] , 3EQ1 [5]; and most recently , one of Arabidopsis thaliana PBGD ( AtPBGD ) : 4HTG [14] are available in the protein data bank ( PDB ) [15] . The structure of EcPBGD ( Figure 2A ) consists of 3 domains of α/β class . Domain 1 ( 1–99 , 200–217 ) and domain 2 ( 105–193 ) share a topology similar to that of transferrins and periplasmic binding proteins [8] , while domain 3 ( 222–313 ) differs having a three-stranded antiparallel β-sheet with one of its faces covered by three α-helices . The domains are connected by 3 hinge regions ( 100–104 , 194–199 , 218–221 ) . The domain 3 interacts with domain 1 , domain 2 and the inter-domain hinge regions primarily through polar interactions , though it also has a hydrophobic interface with domains 1 and 2 [8] . DPM is linked by a thioether bond to C242 , on a flexible cofactor turn ( residues 240–243 ) and lies in a cleft between domains 1 and 2 [16] . The coordinates for most of the residues in the flexible loop region ( 40–63 ) , also known as ‘active site loop’ present in domain 1 , were missing in all the available EcPBGD and hPBGD crystal structures [5] , [16] , [13] as they could not be determined . However , these were determined in the recent crystal structure of AtPBGD , 4HTG [14] . Structural and biochemical studies have indicated a single catalytic site in EcPBGD [8] . Mutational studies have suggested that several arginines ( Figure 2B ) , conserved in PBGD across species , may be involved in the catalysis [17] . Mutations R11H , R149H , R155H , R176H and R232H have a detrimental effect on the activity of the enzyme [17]; while R131L and R132L affect the ability of protein to bind to DPM cofactor as the interaction of these arginines with the acetate ( -Ac ) and propionate ( -Pr ) side groups of the cofactor are lost [18] , thus making the protein catalytically inactive . Also the mutations K55Q and K59Q affect the catalytic activity of the enzyme [19] . D84 has been suggested to play a key role in stabilizing the positive charges on the pyrrole rings during catalysis of chain elongation . D84E mutation shows a reduction in the enzyme's activity , whereas D84A and D84N mutations make the enzyme inactive [20] . Biochemical studies on hPBGD indicate that D99 , R149 , R167 and R173 ( D84 , R131 , R149 and R155 respectively in EcPBGD ) are involved in cofactor assembly and chain elongation [21]; D99 has also been suggested as a critical catalytic residue [13] . A homologous residue , D95 , has been similarly implicated in AtPBGD [14] . Domain movement about the hinge regions has been predicted as facilitating the sequential entry of PBG molecules . Mutational studies on hPBGD , H120P , showed that the hinge residue , H120 , is critical for the activity of the enzyme and its replacement by a proline leads to its inactivation [13] . Plasmodium falciparum PBGD ( PfPBGD ) is a larger protein than its E . coli and human homologues; whose structure is yet to be determined experimentally . PfPBGD has a leucine ( L116 ) in place of an otherwise conserved lysine ( K55 in EcPBGD ) . Nagaraj et al . , have shown that L116K has higher activity than the wild type PfPBGD , producing both uroporphyrinogen I ( non-enzymatic product ) and uroporphyrinogen III , the product of URO3S , the next enzyme in the pathway [22] . P . falciparum is known to synthesize heme de novo , despite acquiring heme from the host red cell hemoglobin in the intraerythrocytic stage . Inhibition of its heme biosynthetic pathway leads to the death of the parasite [23] , [24] , emphasizing the importance of the enzyme for the parasite . Louie et al . , [16] have hypothesized two mechanisms for accommodating the elongating polypyrrole chain: ( 1 ) the sliding active site model in which the elongating chain is accommodated in the cavity within the protein and the domains adjust themselves to juxtapose the binding site and catalytic site residues near the terminal pyrrole ring; and ( 2 ) the moving chain model , in which the elongating chain is progressively pulled past the catalytic site placing the penultimate and terminal rings in the substrate binding site . Roberts et al . , [14] hypothesized that the rotation of the bond linking the cofactor and conserved cysteine would cause the A and B rings to vacate their position for incoming pyrrole rings to bind at the same catalytic site , a process similar to the moving chain model . Experimental studies have helped to gain insights on the role of catalytically important residues of porphobilinogen deaminase . All the hypotheses that have been proposed on the catalytic mechanism of PBGD were based on the structures of the DPM stage alone , as the structures of subsequent catalytic stages of PBGD are unknown . Consequently , the structural , conformational and domain dynamics of the various catalytic stages of PBGD and the role of individual amino acids in these processes are also not known . Hence , we have carried out extensive MD simulations to investigate the mechanism of the pyrrole chain elongation , its accommodation within the binding site , the concomitant domain motions and the role of the active site residues . Ligand entry and exit paths have always been of interest to structural biologists . We have studied the probable exit paths for the tetrapyrrole product ( HMB ) of PBGD , formed by hydrolysis of the hexapyrrole , using Steered Molecular Dynamics ( SMD ) [25] . We have also investigated relaxation of the protein after the exit of HMB . EcPBGD , the best characterized of all known PBGDs till date , has been used as the model system for this study .
The process of pyrrole chain elongation was studied by simulating PBGD at each of the four stages during the tetrapolymerization of PBG ( after addition of each PBG unit ) to understand the concomitant structural changes in the protein . The RMSD of the protein backbone at each stage of chain elongation relative to the EcPBGD reference structure is shown in Figure 3 . The RMSD showed increase from DPM to P5M stage indicating structural changes upon addition of each porphobilinogen unit; the change in RMSD , however , is negligible going from P5M to P6M stage . In Figure 4B , the color code of the HeatMap explains the residue-wise RMSD contribution . The active site loop residues ( 42–60 ) and the domain 2 region contribute significantly to the observed structural deviations . High RMSD is observed for the active site loop from DPM stage and for domain 2 from P4M stage onwards . The RMSF plot in Figure 4A , showed that , in DPM and P4M stages , the fluctuation in the active site loop region is high , while that in the domain 2 is low . The opposite is observed in the P3M stage , where the fluctuation is high in parts of domain 2 and low for the active site loop . Fluctuations of ∼2 Å were observed in the P5M and P6M stages , which are small compared to that in other stages . These observations indicate that in order to accommodate the growing pyrrole chain , either the active site loop or domain 2 readjust to widen the active site cleft . SASA and Rgyr values ( Figure 4C ) are also in accordance with the above observations indicating that after the P4M stage , the entire pyrrole chain gets accommodated within the expanded active site with minimal structural changes in the protein . After studying the dynamics of PBGD during tetrapolymerization process , the hexapyrrole was cleaved forming the tetrapyrrole product , HMB , leaving DPM cofactor attached to PBGD ( HMB stage ) . The HMB stage was simulated to study the exit mechanism of the product , HMB from the protein . During the simulation it was observed that the C ring of HMB slightly moved towards the opening between domain 1 and domain 2 and the F ring moved towards the space formed by the loop and domain 2 ( Figure 10 ) . As it would require longer simulation time for the product to exit from the PBGD , an external force was used to overcome the energy barriers . CAVER was used to detect channels for the exit of product ( Figure S2 ) . Based on the result from the HMB stage and CAVER , SMD was performed to study the exit of HMB from PBGD . Several trial runs , with varying pulling rate , as well as the final SMD runs were tried along directions of C1 , F1 and F2 ( Figure 11A ) . The magnitude of SMD expulsion forces is comparable to the unbinding forces of other protein-ligand systems [27] . In SMD along C1 path , the E , C and D rings of HMB interact with R11 , Y22 and Q198 respectively until about 11 ns . An increase in the pull force is observed around 13 ns when these interactions start breaking ( Figure 11C ) . The loss of interactions of the C ring of HMB moiety with R176 , and the side chains of the E ring with K83 , D84 , V85 and Q19 cause the fluctuations in force profile till the product exits around 26 ns . In SMD along F1 path , the pull force peaks are observed around 11 ns and 15 ns ( Figure 11C ) . Loss of electrostatic interactions of the E ring of HMB with S81 , D84 and V85 and the D ring of HMB with Q198 occur during the peak at 11ns as the product starts to exit . The peak around 15 ns corresponds to the loss of interactions of the C ring of HMB with N151 and R176 and the E ring with R11 , Q19 and K83 . SMD through F2 path has the lowest force profile ( Figure 11C ) ; force peaks are observed around 8ns , 15ns and 20ns . The first peak corresponds to the alignment of the F ring of tetrapyrrole chain perpendicular to the α11 helix in domain 1 , while the second peak , around 15 ns , corresponds to the loss of interaction of the C ring with R176 . The last peak around 20 ns corresponds to the interactions of the C ring with W18 , Y22 , K175 , the D and C ring with Q19 , the E ring with R11 and the E , D and C rings with K83 as they start breaking one after another , till the exit of HMB moiety from the protein at around 32 ns . R11 , Q19 , K83 , R176 are involved in each of the exit paths considered ( Table 2 ) , of which R11 , Q19 and R176 have been suggested to be involved in the catalytic mechanism of the enzyme . Atleast one of these residues ( R11 , Q19 and R176 ) interacts with HMB till its exit from PBGD ( Figure 11D ) , indicating their potential role in the exit of the product . Based on the SMD analysis , the favorable path for the product exit is through the space between domain 1 , domain 2 and the active site loop ( Figure 11B , Video S3 ) . The active site cavity of PBGD enlarges to accommodate the tetrapyrrole product HMB , as seen from SASA and radius of gyration data of the protein ( Figure 4C ) . After suggesting possible path for the product exit , further investigation on protein relaxation was carried , to study if PBGD regains its initial conformation for the next catalytic cycle .
Molecular dynamics study of the enzyme porphobilinogen deaminase helped in gaining important insights about its structural changes as it catalyzes the formation of the product 1-hydroxymethylbilane using four units of porphobilinogen . The study of the chain elongation process revealed the importance of the active site loop and the domain movements in accommodating the polypyrrole chain . The domains 1 and 2 move apart and the cofactor turn moves towards domain 2 to accommodate the growing pyrrole chain in the active site cleft . Conserved residues D50 , K55 and R149 modulate the dynamics of the active site loop , while R11 , D84 and R176 play a role in the catalytic mechanism corroborating previous biochemical studies [2] , [18] . Steered molecular dynamics employed to study the exit of the product , HMB , helped to propose the most probable exit path . Based on expulsion force profile and minimal structural changes , the proposed path for the exit of HMB is through the space formed between domain 1 , domain 2 and the active site loop . Active site residues R11 , Q19 and R176 , reported as catalytically important , are also involved in the exit of the product . The distended PBGD relaxes gradually upon exit of the product to its initial stage structure to resume its catalytic cycle . The questions of how the substrate PBG diffuses into the active-site and the process by which it is covalently linked to the terminal ring of the cofactor to synthesize the tetrapyrrole product are yet to be answered .
The EcPBGD structure , 2YPN [11] , was used to study the protein dynamics through the stages of chain elongation , exit of product and subsequent relaxation . Modeller 9v8 [32] was used to loop-model the missing residues , 43–59 , of the active site loop region . The different stages of pyrrole chain elongation ( Fig . 1 ) that were studied in this work are: DPM ( PBGD with the DPM cofactor ) , P3M ( PBGD after the first catalytic addition of PBG to DPM , i . e . , with P3M moiety ) , P4M ( PBGD with a tetrapyrrole ( P4M ) moiety ) , P5M ( PBGD with a pentapyrrole ( P5M ) moiety ) and P6M ( PBGD with a hexapyrrole ( P6M ) moiety ) . The starting structure of the protein for each stage was prepared by docking and covalently attaching a moiety of methylene pyrrolenine ( MePy ) , the substrate intermediate , to the polypyrrole cofactor in the active site cavity of the protein followed by its energy minimization . Explicit solvent molecular dynamics simulations of the different stages of PBGD were performed using Gromacs 4 . 5 . 5 [33] with G53a6 united-atoms force field [34] . Force field parameters for the covalently attached cofactor , DPM and the subsequent chain extensions , P3M , P4M , P5M and P6M , were obtained from ATB server [35] . The systems were solvated in an octahedron box with a 9 Å layer of SPC/E water model [36]; protein charges were neutralized by adding sodium ions . The systems were then energy minimized using steepest descent method till convergence was reached or for 7000 cycles . NVT and NPT position restrained equilibrations were done for 200 ps and 1 ns respectively with V-rescale temperature coupling [37] and Parrinello-Rahman pressure coupling [38] for the protein and non-protein parts separately . The temperature was gradually raised from 0 K to 300 K at 3 K/ps . Bond lengths were constrained using LINCS algorithm [39] . Periodic boundary conditions were employed to minimize edge effects and the electrostatic computations were done using Particle Mesh Ewald [40] , [41] with interpolation order of 4 , tolerance of 1e-5 and fourier spacing of 1 . 6 Å . The DPM and P3M stages were simulated for 35 ns each . The addition of an extra pyrrole ring to P3M resulted in the domains 1 and 2 moving far apart . In order to prevent this , harmonic distance restraints were applied to the centers of mass of the domains 1 and 2 for 15 ns , followed by unrestrained dynamics of 35 ns in each of the subsequent stages ( P4M , P5M and P6M stages ) . The unrestrained simulation trajectories were used for analysis . After the P6M stage , the hexapyrrole was cleaved at the bridging carbon atom between B and C rings of the moiety , and an -OH group was attached to the linker carbon atom forming the tetrapyrrole product , 1-hydroxymethylbilane , leaving DPM cofactor attached to PBGD ( HMB stage ) . This set up was simulated for 60 ns . The possible channels for the exit of HMB from PBGD were identified using a pymol plugin CAVER [42] . The structure of PBGD after the HMB stage was used as input for CAVER; center of mass of HMB coordinates were taken as starting point for detecting channels . CAVER predicted three possible exit channels , two ( F1 & F2 channel ) near the F ring and the third ( C1 channel ) close to the C ring of HMB ( Figure 13 ) . The PBGD structure from the HMB stage was used as the starting structure to carry out the SMD simulations . Trial SMD runs at variable pulling rates ( 50 Å ns−1 , 5 Å ns−1 ) were done to pull HMB from PBGD in each of the 3 directions . The center of mass of protein is used as the reference group and the pull force is applied to the center of mass of either the C ring of HMB moiety in the C1 direction or the F ring of HMB moiety in the F1 and F2 directions at a constant rate of 1 Å ns−1 . A force constant of 1000 kJ mol−1 nm−2 was used for the pulling experiments . The ligand ( HMB ) was then removed after simulating the HMB stage for 60 ns . The system in the absence of HMB was simulated for 150 ns to observe the protein relaxation process ( no-HMB stage ) . Trajectories were analyzed using VMD 1 . 9 . 1 [43] and Gromacs 4 . 5 . 5 . VMD was used to calculate the hydrogen bond interactions and the root mean square deviations ( RMSDs ) of the backbone atoms of the protein for all the stages of pyrrole chain elongation . The loop modeled and energy minimized 2YPN structure was used as the reference for RMSD calculations . DisRg , a VMD plugin was used for calculation of the radius of gyration ( Rgyr ) . Gromacs was used to calculate the root mean square fluctuations ( RMSFs ) of the Cα atoms and the solvent accessible surface area ( SASA ) of the entire protein . Combined essential dynamics [44] and dynamic cross-correlation matrix ( DCCM ) analysis [45] were performed on the concatenated trajectory of all the stages of pyrrole chain elongation , fitted to the same reference structure , to analyze the cumulative effect of domain motions . Principal component analysis ( PCA ) was performed using NMWiz , a VMD plugin [46] . Interactions among residues and with the cofactor were studied by calculating the minimum distance between possible donor and acceptor atoms of the respective residues and each ring of the polypyrrole chain . A residue is considered to be interacting with the cofactor if its distance with any polar atom of pyrrole ring is less than 3 . 5 Å . Based on this criterion , persistence of such interactions in the trajectory were calculated . Distance between domains 1 and 2 was computed using the center of mass of interface residues from domain 1 ( residues 14 to 19 ) and domain 2 ( residues 150 to 152 and 173 to 177 ) , to track the domain movements . Volume of the active site cavity along each trajectory was calculated using POVME [47] . | Heme is the prosthetic group at the core of the oxygen carrier metalloprotein hemoglobin . Heme consists of a tetrapyrrole called porphyrin bound to an iron ion . It is synthesized by the heme biosynthetic pathway , which is common to all eukaryotes and most prokaryotes . Porphobilinogen deaminase , an enzyme in the heme biosynthetic pathway , catalyzes the formation of a linear tetrapyrrole product , 1-hydroxymethylbilane , from four units of porphobilinogen . In this study we carried out molecular dynamics simulations to understand the structural changes that the enzyme undergoes while catalyzing this reaction . There are three segments to the study: 1 ) understanding the changes in the enzyme when the porphobilinogen units get attached to the dipyrromethane cofactor , thereby forming a polypyrrole chain; 2 ) exit of the product from the active site of the enzyme via steered molecular dynamics; and 3 ) the relaxation of the enzyme to the initial stage to resume its catalytic cycle . Molecular dynamics simulations of the protein through the different stages of pyrrole chain elongation gives insight into the motions of domains , active site loop and role of conserved active site residues in facilitating the accommodation of the polypyrrole chain . In addition to this , we propose a possible exit path for the product and demonstrate the relaxation of the enzyme after the exit of the product to resume the catalytic cycle . | [
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"analysis"
] | 2014 | Structural Insights into E. coli Porphobilinogen Deaminase during Synthesis and Exit of 1-Hydroxymethylbilane |
Complement forms an important arm of innate immunity against invasive meningococcal infections . Binding of the alternative complement pathway inhibitor factor H ( fH ) to fH-binding protein ( fHbp ) is one mechanism meningococci employ to limit complement activation on the bacterial surface . fHbp is a leading vaccine candidate against group B Neisseria meningitidis . Novel mechanisms that meningococci employ to bind fH could undermine the efficacy of fHbp-based vaccines . We observed that fHbp deletion mutants of some meningococcal strains showed residual fH binding suggesting the presence of a second receptor for fH . Ligand overlay immunoblotting using membrane fractions from one such strain showed that fH bound to a ∼17 kD protein , identified by MALDI-TOF analysis as Neisserial surface protein A ( NspA ) , a meningococcal vaccine candidate whose function has not been defined . Deleting nspA , in the background of fHbp deletion mutants , abrogated fH binding and mAbs against NspA blocked fH binding , confirming NspA as a fH binding molecule on intact bacteria . NspA expression levels vary among strains and expression correlated with the level of fH binding; over-expressing NspA enhanced fH binding to bacteria . Progressive truncation of the heptose ( Hep ) I chain of lipooligosaccharide ( LOS ) , or sialylation of lacto-N-neotetraose LOS both increased fH binding to NspA-expressing meningococci , while expression of capsule reduced fH binding to the strains tested . Similar to fHbp , binding of NspA to fH was human-specific and occurred through fH domains 6–7 . Consistent with its ability to bind fH , deleting NspA increased C3 deposition and resulted in increased complement-dependent killing . Collectively , these data identify a key complement evasion mechanism with important implications for ongoing efforts to develop meningococcal vaccines that employ fHbp as one of its components .
The complement system forms an important arm of innate immune defenses against Neisseria meningitidis . The presence of antibody-dependent complement-mediated serum bactericidal activity predicts protection against invasive disease [1] . Individuals deficient in components of the alternative or terminal complement pathways are predisposed to recurrent episodes of meningococcal infection [2] , [3] , [4] , [5] . In order to survive in its human host , the meningococcus must evade killing by complement ( either direct lysis by the terminal pathway or complement-dependent opsonophagocytosis ) . Capsular polysaccharide expression is probably the most important determinant of meningococcal virulence . Expression of capsular polysaccharide renders the organism more serum resistant [6] , [7] , although the molecular basis for capsule-mediated serum resistance remains undefined . In addition , scavenging host complement inhibitors by meningococcal membrane proteins constitutes an important mechanism of subverting complement attack . Opc has recently been shown to bind to vitronectin [8] and contribute to serum resistance [9] . Porin ( Por ) A ( PorA ) binds to C4b-binding protein , although binding is best observed under hypo-osmolar conditions [10] . The molecule that has received much attention in recent literature is factor H-binding protein ( fHbp; also known as LP2086 [11] or Genome-derived Neisserial Antigen ( GNA ) 1870 [12] ) that binds to the alternative pathway inhibitor , factor H ( fH ) [13] , [14] . FH acts as a cofactor in the factor I-mediated cleavage of C3b to the hemolytically inactive molecule iC3b [15] , prevents the association of factor B with C3b thereby retarding the formation of the alternative pathway C3 convertase ( C3b , Bb ) and irreversibly dissociates the alternative pathway C3 convertase once it is formed [16] , [17] . Based on its amino acid sequence , fHbp has been classified into 3 variants [12] , or into 2 subfamilies [11] , or more recently , into seven modular groups [18] , [19] . Despite the fairly extensive fHbp sequence variation among strains , representative strains from each variant family bind to fH [13] . The co-crystal structure of variant 1 ( subfamily B ) fHbp with a fragment of fH revealed an extensive interaction surface of ∼2 , 860 Å2 [14] . fHbp is currently being evaluated as protein vaccine candidate against group B meningococcal disease and has shown promise in Phase III clinical trials [20] . In light of the use of fHbp as a vaccine , it is important to define alternative means of complement evasion that the meningococcus may employ , in particular scavenging fH . fHbp expression levels vary markedly across strains . Additional mechanisms to bind to host fH could undermine the efficacy of fHbp-based vaccines . In this report we have characterized Neisserial surface protein A ( NspA ) as a second acceptor molecule for fH on meningococci and have established its role in enhancing meningococcal serum resistance . It is noteworthy that NspA has received attention as a possible group B meningococcal vaccine; identification of a novel function for this protein highlights the potential utility of microbial fH binding molecules as vaccine antigens .
Human fH binds to the meningococcal surface molecule , fHbp and meningococcal strains , such as H44/76 , do not show any detectable binding of fH by flow cytometry following deletion of fHbp ( [13] and Figure 1A ) . However , we observed a small but reproducible , albeit statistically insignificant , binding of fH to fHbp mutants of some meningococcal strains , such as A2594 , BZ198 and Z2087 by flow cytometry ( grey shaded histograms , Figure 1A ) relative to control histograms ( Figure 1A , histograms depicted by broken lines ) . The amount of fH that bound to the fHbp deletion mutants of these strains was reduced compared to their wild-type fHbp expressing parents ( histograms depicted by solid lines , Figure 1A ) . By contrast , fH binding to the fHbp deletion mutant of strain H44/76 was below the level of detection by FACS . These data indicate that some strains of meningococci may express a second molecule that binds to human fH . Capsule expression in N . meningitidis is subject to phase variation [21] . Down-regulation of capsule expression occurs during certain stages of pathogenesis , for example , while traversing the epithelial barrier [22] . Further , constitutively unencapsulated strains are commonly found as carriage isolates [23] , [24] , [25] and may contribute to the development of naturally acquired immunity . We have previously demonstrated that expression of capsule in group B meningococcal strain H44/76 reduces binding of the complement regulatory binding protein , C4b-binding protein ( C4BP ) by about 50% [10] . To determine if the expression of capsular polysaccharide similarly affects binding of fH to meningococci that lack fHbp expression , we assessed binding of fH to meningococcal strains in which capsule production had been abrogated . Deleting capsule from the fHbp mutants of strains A2594 , Z2087 , BZ198 and H44/76 revealed that isogenic capsule negative ( Cap− ) fHbp mutants bound more fH than their corresponding capsule expressing ( Cap+ ) counterparts ( Figure 1B ) . Consistent with previous observations [13] , [26] , deletion of capsule from meningococcal strains that expressed fHbp did not significantly alter binding of fH to meningococci ( data not shown ) . While the fHbp mutants of A2594 , Z2087 and BZ198 showed a marked increase in fH binding with loss of capsule , only a minimal increase in fH binding was seen in unencapsulated ( Cap− ) fHbp mutant of H44/76 , suggesting that the second acceptor for fH was expressed in variable amounts across strains . These data show that meningococcal strains possess a molecule distinct from fHbp that serves as a ligand for human fH and that binding of fH to this molecule is inhibited , to some extent , by capsule expression . LPS length can affect binding of complement inhibitors such as fH to bacteria [27] . In Neisseria , many of the genes involved in synthesis of lipooligosaccharide ( LOS ) are subject to reversible phase variation and a consequence is that the length of glycan extensions from HepI varies [28] . Previous work in our laboratory has shown that altering the length of glycan extensions from HepI affects binding of the complement inhibitor , C4BP , to gonococci [29] . To determine if HepI glycan extensions similarly affect binding of fH to its second ligand on meningococci , we studied the effects of truncating the glycan residues from HepI on fH binding to meningococci that lack fHbp . Wildtype strain A2594 expresses a lacto-N-neotetraose ( LNT ) extension from HepI and the wildtype LOS is not modified with sialic acid ( LNT LOS sia-/Figure 2A , blue ) . Mutants that express a lactose extending from heptose I ( lgtA mutants/L8 LOS/Figure 2A , green ) or no saccharides off HepI ( lgtF mutants/unsubstituted HepI LOS/Figure 2A , red ) were created in the background of strains A2594 encapsulated ( Cap+ ) and A2594 Cap− . As seen in Figure 2B , truncation of the HepI chain of LOS results in a progressive increase of fH binding to both Cap+ ( left panel ) and Cap− ( right panel ) meningococci . For a given LOS phenotype , the Cap+ mutant bound less fH than the corresponding Cap− mutant , confirming the observation above ( Figure 1B ) that capsule expression negatively impacted fH binding to the second receptor . In both Cap+ and Cap− backgrounds , the trend of increasing fH binding as the length of LOS HepI chain length decreases was statistically significant ( Supplementary Table S1; p-value for trend test = 0 . 007 ) . The inhibitory influence of capsule on fH binding to the second meningococcal fH receptor decreased as HepI LOS chain length decreased . For example , the differences in fH binding between the Cap+ and Cap− isogenic mutants were least apparent when the HepI of LOS was unsubstituted ( red graphs in Figure 2B ) . In N . gonorrhoeae the modification of LNT LOS with sialic acid dramatically enhances the binding of fH [30] , probably by increasing the access of fH to porin [31] . However , LOS sialylation has not been reported to enhance binding of fH to meningococci [26] , [31] . Meningococcal strains that belong to groups B , C , W-135 and Y can endogenously sialylate their LNT LOS [32] . Group A strains do not have the capacity to synthesize 5′-cytidinemonophospho-N-acetylneuraminic acid ( CMP-NANA; the donor molecule for sialic acid ) and thus cannot endogenously sialylate their LNT LOS [33] , [34] . However , they can scavenge CMP-NANA from the host to sialylate their LNT LOS . To determine if LOS sialylation affects binding of fH to fHbp negative meningococci we analyzed fH binding to fHbp− and Cap− mutants of group A strains A2594 and Z2087 . The use of group A strains , that cannot endogenously sialylate their LOS , permitted us to study the effects of increasing amounts of LOS sialylation on fH binding by varying the amount of CMP-NANA added to growth media . As seen in Figure 3A , growth of both strains in CMP-NANA-containing media increased fH binding . Z2087 Cap− fHbp− was then grown in increasing amounts of CMP-NANA ( Figure 3B ) ; fH binding increased as CMP-NANA concentrations in the growth media were increased and maximal fH binding was achieved at 5 µg/ml of CMP-NANA . Taken together , the data presented thus far indicate that binding of fH to the putative meningococcal second fH receptor is enhanced by truncation of HepI glycan extensions , sialylation of LNT LOS or loss of capsule expression . Our studies indicate the presence of a second meningococcal receptor for human fH , distinct from the previously described fHbp . A Far Western ligand immuno-blotting assay was used to identify the putative second receptor molecule ( s ) present on fHbp deletion mutants of N . meningitidis . Membrane proteins prepared from strains A2594 ( binds fH when its fHbp is deleted ) and H44/76 ( fHbp deletion mutant does not bind detectable amounts of fH by FACS ) were separated on a 4–12% Bis-Tris gel and transferred to a PVDF membrane . Proteins that bound to fH were identified by probing the membrane with purified human fH and detecting bound fH with an anti-fH Ab ( Figure 4A , right ) . An fHbp deletion mutant of each strain was used as a control . We focused on fH-reactive bands that were present in A2594 and A2594 fHbp− , but were either absent or expressed in reduced amounts on H44/76 and H44/76 fHbp− . A prominent fH-binding band of ∼17 kD was apparent in A2594 and A2594 fHbp− . This band was detected , but with lower intensity , in H44/76 and its fHbp deletion mutant ( Figure 4A , right ) . This ∼17 kD band was not considered in our previous study where strain H44/76 was employed to identify fHbp as a fH binding molecule [13] because strain H44/76 expresses very low levels of this protein; we focused on the more prominent 29 kD fH-reactive band ( fHbp ) that was subsequently validated as the fH ligand on intact bacteria . A Coomassie blue stained gel showing the total membrane protein profile of each strain is shown for reference ( Figure 4A , left ) . To determine the identity of the ∼17 kD fH binding molecule , the region corresponding to the location of the ∼17 kD band was excised from a parallel Coomassie stained gel ( indicated by the asterisk , Coomassie blue stained gel , Figure 4A ) and this sample was subject to in-gel trypsin digestion and MALDI-TOF analysis followed by peptide mass fingerprinting that was compared with the Neisseria proteome . The protein band was defined as Neisserial surface protein A ( NspA ) using the Peptide Mass Fingerprint program for MS data and the MS/MS Ion Search program for CID data . The peptide ions covered 43% of the total protein sequence and no other statistically significant matches were identified . The data suggest that NspA could bind to human fH . One caveat of a Far Western assay is that proteins presented in non-native conformations may interact in artificial ways with the ligand , in this case fH , and lead to the detection of “false positive” interactions . The data presented below indicate that NspA is likely the only additional fH ligand present in these strains and additional fH reactive bands present on the Far Western blot ( Figure 4A , right ) were not analyzed by MALDI-TOF . Consistent with our previous observations [13] , PorA and PorB also bound to fH on the western blot ( Figure 4A , right ) . Purified H44/76 PorB3 binds to human fH by ELISA [35] , but neither PorB3 nor PorA bind to fH in the context of intact H44/76 bacteria [13] , [26] , and we therefore did not anticipate these meningococcal porins to serve as ligands for fH on whole bacteria . The putative surface exposed loops of PorA and PorB show considerable variation across strains [36] , [37] , [38] , [39] and thus it remained possible that the porin molecule ( s ) of A2594 , but not H44/76 , served as a ligand for fH . Deleting PorA or PorB3 from the background of A2594 Cap− did not diminish fH binding compared to the respective isogenic Por sufficient parent strains ( right and left graphs of Figure 4B , respectively ) . This suggests that neither Por molecule contributed to fH binding to intact A2594 organisms and that the interaction of these proteins with fH in the Far Western assay is a “false positive” . NspA shares structural similarities with the Neisserial opacity proteins ( Opa ) and we sought to determine if Opa might also bind fH . fH binding to an unencapsulated Z2087 strain that expressed Opa and its isogenic Opa negative mutant was indistinguishable ( Supplementary data Figure S1 ) , indicating that the Opa proteins were not ligands for fH . Several lines of evidence were used to independently verify that NspA is a ligand for fH on live , intact meningococci . FH comprises 20 short consensus repeat ( SCR ) domains arranged as a single chain [43] . Recently , the cocrystal complex of variant 1 fHbp with fH SCRs 6–7 showed an extensive area of interaction of fHbp with fH SCR 6 and minor points of contact with SCR 7 [14] . Site-directed mutagenesis studies also localized the fHbp binding domain in fH to SCR 6 [44] . To determine the fH SCRs involved in binding to NspA , we utilized fusion proteins that contain contiguous fH SCRs fused at their C-terminus to the Fc portion of IgG2a [44] , [45] . The Fc fragment served as a ‘tag’ for symmetric detection of all fusion proteins . The ability of five fH/Fc fusion constructs ( SCR 1–5/Fc , SCR 1–7/Fc , SCR 6–10/Fc , SCR 11–15/Fc and SCR 16–20/Fc ) to bind to meningococcal strain A2594 Cap− L8 LOS and its isogenic fHbp− , NspA− and fHbp− NspA− double negative mutants , was examined by flow cytometry . Only those fH/Fc proteins that contained SCRs 6 and 7 ( SCR 1–7/Fc , and SCR 6–10/Fc ) bound to the NspA expressing strains that lacked fHbp . This result indicates that like fHbp , NspA binds to SCR 6 and/or 7 . As expected , the SCR 6/7 containing constructs bound to fHbp expressing strains while none of the fH SCR/Fc constructs bound to mutants lacking both fHbp and NspA . Factor H-like molecule 1 ( FHL-1 ) comprises fH SCRs 1–7 plus four unique additional C-terminal amino acids ( SFTL ) [46] . FHL-1 also bound to Cap− fHbp− A2594 ( Supplementary Figure S4 ) , supporting the conclusion that SCRs 6 and/or 7 play a role in binding of fH to NspA . This finding is consistent with the NspA binding site residing in fH SCRs 6 and/or 7 . Together , these data suggest that SCR 6 and/or SCR 7 are important for binding of fH to NspA . Although less likely , these data do not unequivocally exclude a role for SCRs 8 , 9 and 10 in binding of fH to NspA; studies to precisely localize the NspA binding region in fH are underway . N . meningitidis and N . gonorrhoeae are exclusively human pathogens and the ability of these pathogens to evade complement-mediated killing in a species-specific fashion may contribute to the narrow host range of infection [45] , [47] , [48] . We have shown previously that gonococci bind specifically to human C4BP ( and in some instances , chimpanzee C4BP ) [48] and human fH [45] . Likewise , binding of fH to meningococcal fHbp is specific for human fH [47] . To determine if fH binding to NspA is also species specific we examined binding of fH from different primate species to N . meningitidis strain A2594 Cap− L8 LOS and its isogenic fHbp− , NspA− and fHbp− NspA− double-negative mutants by Western blotting ( Figure 9A ) . Strains were incubated with 10% heat-inactivated human or primate sera to assess direct binding of fH to bacteria . Heat inactivation destroys heat labile complement components while leaving fH intact; inactivation of complement is necessary to prevent detection of complement C3b-mediated binding of fH to meningococci . Bound fH was detected by Western blot using polyclonal goat anti- human fH Abs . This Ab reacts with fH in the primate sera tested ( Figure 9B ) and as previously reported , detection of rhesus fH was slightly weaker [45] . Human fH bound well to all strains that expressed NspA ( Figure 9A ) , but only weakly to strains that expressed fHbp but lacked NspA , which is consistent with the fH binding data present in Figure 6B . Very weak binding of chimpanzee fH to strains expressing NspA was also noted ( Figure 9A ) . None of the strains tested bound rhesus fH when incubated with heat-inactivated rhesus sera ( Figure 9A ) . The fHbp− NspA− strain showed barely detectable binding to human fH , and as expected , did not bind fH from the other primate species tested ( Figure 9A ) . fH functions to down-regulate the alternative pathway of complement and bacteria that bind to fH would be expected to be more resistant to the bactericidal action of serum than those that do not bind to fH . To determine the relative roles of fHbp and NspA in serum resistance we examined strains BZ198 Cap+ and A2594 Cap− each expressing L8 LOS and their isogenic mutants that lacked either fHbp or NspA or both for their ability to resist killing by normal human serum . The concentration of serum used was determined based on the survival of each parent strain in serum ( Supplementary Figure S5 ) . Loss of NspA expression in both instances resulted in greater sensitivity to complement-dependent killing ( Figure 10A ) . It is noteworthy that in these high NspA-expressing strains , deleting fHbp did not negatively impact survival . Deleting fHbp from the high fHbp-expressing strain H44/76 , which expresses low levels of NspA , results in decreased serum resistance [13] , [42] . fH limits C3 deposition by virtue of its ability to act as a cofactor in the factor I-mediated cleavage of C3b [15] and irreversibly dissociate alternative pathway C3 convertases ( decay-accelerating activity ) [16] , [17] . As expected , mutant strains that lacked NspA bound more C3 than their NspA-sufficient ‘parent’ strains . The median fluorescence of C3 binding was ∼5-fold more with A2594 Cap−/L8 LOS/NspA− and ∼2 . 5-fold more with BZ198 Cap+/L8 LOS compared to their respective isogenic parent strains ( Figure 10B ) . fH binding ( Figure 5B ) mirrored survival of bacteria in serum ( Figure 10A ) confirming that complement regulation by NspA occurred at the level of C3 deposition . Similarly , C3 deposition on BZ198 Cap+/LNT sia+/NspA− was ∼1 . 5-fold higher than on BZ198 Cap+/LNT sia+ ( data not shown ) . Meningococcal strain A2594 Cap− L8 LOS lacking both fHbp and NspA was complemented , in trans , with NspAA2594 to verify that the loss in fH binding and concomitant decrease in serum resistance were not due to secondary changes . As expected , complementation with NspAA2594 resulted in expression of NspA as judged by both western blot ( data not shown ) and flow cytometry ( Figure 11A ) . Restoration of NspA expression also restored the ability of fHbp− NspA− double mutants to bind fH ( Figure 11A ) . The ability of the complemented strains to resist killing by NHS was assessed in a serum bactericidal assay . As shown above ( Figure 10A ) , A2594 Cap− L8 LOS lacking both fHbp and NspA was more sensitive to serum killing than the parent strain expressing both of these proteins . Complementation with NspA , alone , restored serum resistance to the mutant strain lacking both fH ligands , albeit to levels less than that of the parent strain ( Figure 11B ) . All three strains were completely ( 100% ) killed in 6 . 6% NHS ( data not shown ) . Overall , these data indicate that the lack of fH binding and decreased serum resistance observed in strains lacking NspA is because of lack of NspA expression and not the result of secondary changes in these isogenic strains .
Several pathogens , including bacteria , fungi , parasites and viruses bind to fH , which inhibits complement activation on their surface ( reviewed in [49] , [50] ) . This work has characterized NspA as a ligand for human fH and has shown that NspA plays a role in conferring serum resistance to meningococci even in the absence of expression of the previously characterized fH-binding meningococcal molecule , fHbp . NspA interacts with fH SCRs 6 and/or 7 and like fHbp , preferentially binds to human fH . It is interesting that all naturally occurring meningococcal strains reported thus far express both fHbp [12] , [51] and NspA [52] , suggesting an important role for these proteins in meningococcal pathogenesis . Prior to this study the function of NspA had not been defined . The factors that influence fH binding to NspA on intact bacteria have been characterized in this study , which provides insights into the pathophysiological conditions or niches where NspA-mediated fH binding may assume an important role . Meningococcal strains that are isolated from the nasopharynx are often unencapsulated and/or express L8 LOS [53]; high binding of fH to NspA under these conditions could point to a key role for NspA in survival of meningococci during nasopharyngeal colonization ( a prerequisite of invasive disease ) and in survival of carrier strains . The positive effects of NspA on bacterial survival are also seen in encapsulated strains that are high NspA expressers such as BZ198 when they express L8 LOS ( Figure 10A ) . Meningococcal isolates often express more than one LOS species because many of the genes involved in LOS biosynthesis , including lgtA , are phase variable [28] . Thus , for example , a strain could express a combination of LNT and L8 LOS species [54] , [55] . It is not clear how LOS sialylation , which represents elongation beyond the LNT structure , enhances fH binding to NspA . One possibility is that LOS and NspA lie in close proximity and expression of the unsialylated LNT hinders fH from binding to NspA; sialylation may alter the conformation of LOS thereby better exposing the fH binding region of NspA . Another possibility is that LOS sialic acid itself may act as part of the docking site for fH . Nevertheless , LOS sialylation is not essential for fH binding to NspA on intact organisms . Sialylation of N . gonorrhoeae LNT LOS also enhances fH binding , but the interaction in that instance requires the concomitant presence of the gonococcal PorB molecule [56] . LPS glycan extensions can negatively impact binding of complement inhibitors to gram-negative bacteria . As an example , expression of O-antigenic repeats on the LPS of Y . enterocolitica can block binding of fH to the Ail protein [27] . Neisseriae lack O-antigenic repeats , yet subtle changes in the core LOS structure can have profound impacts on the binding of complement inhibitors and serum resistance . The presence of the proximal Glc off HepI appears to be necessary for optimal C4b-binding protein ( C4BP ) binding to porin ( Por ) B . 1B ( Por1B ) -expressing gonococci [29] . NspA forms an eight-stranded anti-parallel β-barrel and has four putative surface exposed loops . A conformational epitope that includes NspA loop 3 appears to be important for binding of mAbs Me-7 [57] and 14C7 [40] both of which inhibit fH binding to NspA on meningococci . It is therefore possible that NspA loop 3 plays a role in the interaction with fH , although steric hindrance by the surface-bound mAb could account for the ability of the mAbs to block fH binding . It would be of interest to determine whether NspA plays a role in fH binding to gonococci because of the extensive ( ∼95% ) sequence similarity between gonococcal and meningococcal NspA . Sera from humans may contain naturally-occurring antibodies that are directed against LNT-expressing LOS and is bactericidal against group B meningococci [58] . Phase variation of lgtA that results in L8 LOS expression could subvert killing by these naturally occurring anti-LNT antibodies . However , truncation of the HepI chain of LOS could have a negative effect on serum resistance because of increased accessibility of the 3-phosphoethanolamine ( PEA ) residue on HepII to C4b [59]; C4b amide-linked to PEA can lead to downstream complement activation that may result in bacterial killing . By virtue of enhanced fH binding , high NspA expressers may be able to dampen excessive complement activation that is initiated by C4b when LOS is truncated . It is noteworthy that loss of NspA ( leaving fHbp intact ) from a high NspA expressing strain such as A2594 ( intermediate fHbp expression levels ) resulted in increased C3 deposition on bacteria , while loss of fHbp ( leaving NspA intact ) in that strain did not enhance C3 deposition ( Figure 10B ) . Strain BZ198 also expresses high levels of NspA and intermediate levels of fHbp; loss of NspA resulted in greater enhancement of C3 deposition relative to that seen when fHbp was deleted ( Figure 10B ) . We have shown previously that loss of fHbp in high-fHbp expressing strains such as H44/76 ( expresses low levels of NspA ) also increases C3 deposition [13] , [42] . The relative abilities of the two ligands to regulate C3 deposition on different strains may reflect heterogeneity in their expression levels . In addition , variables such as the amount of capsule expression and the diversity of HepI LOS extensions could affect the amount of fH binding to NspA and thereby its ability to regulate C3 deposition . The relative roles of fHbp and NspA in regulating complement activation in the context of expression of the different capsular groups and varying LOS structures is a complex subject that merits further study . However , it is evident from the current study and from previous work [13] , [42] , [60] , [61] that both molecules contribute to the ability of meningococci to resist killing by normal human serum . fHbp has shown considerable promise as a vaccine candidate [20] . A vaccine that has fHbp as a component could lead to selection of meningococcal strains that either do not express , or express very low amounts of fHbp . Under such circumstances , high NspA expressers may have a survival advantage . Our data suggest that including NspA as part of a vaccine strategy that targets fH-binding proteins on N . meningitidis could , in theory , overcome this potential obstacle . Indeed , NspA has been intensively investigated as a vaccine candidate against group B meningococci [62] , [63] . Both mAbs against NspA ( including Me-7 and 14C7 ) and polyclonal Abs against NspA ( raised by immunization of mice with meningococcal outer membrane vesicles that contained native NspA ) were bactericidal and protected against experimental murine infection [52] , [64] . Although recombinant NspA expressed in E . coli and purified from inclusion bodies elicited a good antibody response in humans , these antibodies were not bactericidal [62] . Recombinant NspA does not have the same conformation as NspA present in the meningococcal outer membrane [40] , [64] , suggesting that protective antibodies may be directed against conformational epitopes . Another intriguing possibility , in light of our observations that binding of fH to NspA is restricted to humans , is that human fH may bind to NspA in the vaccine formulation , which could have attenuated the antibody response to surface-exposed ( and fH binding ) NspA epitopes that otherwise would have elicited a more productive bactericidal antibody response; this possibility has been raised previously with regard to the use of fH binding proteins as vaccines [14] , [65] . In summary , we have identified an important complement-evasion function for NspA , an antigen that has been studied for its potential as a group B meningococcal vaccine candidate . In addition to the implications for fHbp-based vaccines that are currently being developed , these findings set the stage for further studies to characterize NspA-fH interactions that could boost efforts to develop better meningococcal vaccines .
This study was approved by the Committee for the Protection of Human Subjects in Research at the University of Massachusetts Medical School . All subjects who donated blood for this study provided written informed consent . The relevant phenotypes of the mutants created in N . meningitidis are listed in Table 1 . The characteristics meningococcal strains used in this study are listed in Supplementary Table S2 . Bacteria were routinely grown on chocolate agar plates supplemented with IsoVitaleX equivalent at 37°C in an atmosphere enriched with 5% CO2 . GC plates supplemented with IsoVitaleX equivalent were used for antibiotic selection . Antibiotics where used at the following concentrations when indicated; 100 µg/ml kanamycin , 7 µg/ml chloramphenicol , 5 µg/ml erythromycin , 50 µg/ml spectinomycin and 5 µg/ml tetracycline . Escherichia coli strains ( Invitrogen , Carlsbad , CA ) were routinely cultured in Luria Bertani ( LB ) broth or on LB agar . Antibiotic were used as needed at the following concentrations: 50 µg/ml kanamycin , 150 µg/ml ampicillin , 50 µg/ml chloramphenicol , 400 µg/ml erythromycin , 100 µg/ml spectinomycin and 12 . 5 µg/ml tetracycline . Construction of lgtA [59] , lgtF [59] , mynB [56] , siaD [59] , lst [66] and fHbp [13] deletion mutants have all been described previously . Loss of capsule expression was verified by either colony hybridization or flow cytometry with the appropriate serogroup specific anti-capsule Ab . Anti-group A mAb JW-A-1 ( IgG2a ) , anti-group C mAb KS-C-1 ( IgG3 ) , anti-group W-135 mAb JW-W1b ( IgG2b ) and anti-group Y mAb JW-Y2a ( IgM ) were provided by Dr . Dan M . Granoff ( Childrens Hospital Oakland research Institute , Oakland , CA ) , while anti-group B mAb 2-2-B ( IgM ) was obtained from the National Institute for Biological Standards and Control ( Potters Bar , Hertfordshire , U . K ) . In addition , inactivation of siaD ( mynB in the case of group A ) was verified by PCR . For serogroups A , C , Y and W-135 the confirmatory PCR was the amplification of a fragment corresponding to the predicted size of siaD ( or mynB for group A ) plus the resistance marker ( ∼2 . 5kb for serogroup A using primers NT2 5′-ATGATGGTAATGGGAAAAGAGT-3′ and NT4 5′-ATACTTAATAACAGAAAATGGCG-3′; ∼2 . 9 kb for serogroup C using primers BF 5′-AGCGTCAACGAATATGAAACATTAT-3′ and CR 5′-CTGCTTAACTTTATTAAGGGCATTG-3′ and ∼2 . 8 kb from serogroups W-135 and Y strains using primers W1618 5′-ATTCCCCATGAACTACATCAGAATA-3′ and W2766 5′-TAATGCAAACTCAATTGCAAAACTA-3′ ) coupled with the absence of a wildtype gene . In serogroup B strains siaD is inactivated with the 8 . 9 kb Tn1725 and a lack of amplification of the wild type siaD , in the presence of a positive control reaction , was used to demonstrate the lack of the wild type siaD . All Cap− serogroup B strains were verified by lack of reactivity to the anti-group B capsule mAb 2-2-B . LOS structure was verified in all strains and mutants by silver staining of protease K digested bacterial lysates that had been separated on 12% Bis-Tris gels ( Invitrogen , Carlsbad , CA ) using MES buffer ( Invitrogen , Carlsbad , CA ) as described previously [67] . In addition insertions in lgtA or lgtE were verified by PCR . Mutant derivatives of strain A2594 that lacked PorA or PorB3 expression were constructed using DNA extracted from PorA and PorB3 deletion mutants in strain H44/76 ( porA::kan and porB3::erm , respectively ) that were provided by Dr . Peter Van der Ley ( Laboratory of Vaccine Research , Netherlands Vaccine Institute , Bilthoven , The Netherlands ) . NspA deletion mutants were constructed as follows . A 1 . 3 kb fragment of DNA containing nspA was amplified from N . meningitidis strain A2594 using the primers nspA_F114 ( 5′-CTCTTTAGGTTCTGCCAAAGGCTTC-3′ ) and nspA_R1122 ( 5′-ATGTTGTGAAGTGGGAAAGTGTTGC-3′ ) and the amplicon was cloned into pCR2 . 1-TOPO ( Invitrogen , Carlsbad , CA ) . The resulting plasmid was digested with HincII , deleting an internal 130 bp fragment of nspA , and ligated to a blunt spectinomycin resistance cassette containing aadA . Linearized plasmid DNA was used to transform N . meningitidis strains as previously described [68] . PCR was used to confirm the nspA::spc genotype and Western blot using anti-NspA mAb Me-7 were performed to demonstrated loss of NspA . A derivative of the E . coli-Neisseria shuttle vector pFP12 was used to complement the nspA::spc mutations in trans . A 1 , 131 bp fragment , containing nspA with its native promoter and terminator was amplified from chromosomal DNA prepared from strain A2594 using the primers NspA-R1213 StuI 5′-GACAGGCCTGTTTTGGACATTTCGGATTCCTC-3′ and NspA-F102 SphI 5′-GACGCATGCCACTATATAAGCGCAAACAAATCG-3′ . The amplified DNA was digested with StuI and SphI and cloned into identically digested pFP12-GNA1870 [69] . The resulting construct was then digested with ScaI to allow for the insertion of a blunt ( BsrBI ) TetM cassette . The resulting plasmid construct , pFP12 NspAA2594Tet , was confirmed by DNA sequencing and by Western blot analysis of E . coli cell lysates using ME-7 . A2594 Cap− L8 ( CmR , KanR ) and its fHbp− ( ermR ) and NspA− ( spcR ) and fHbp− NspA− double mutants were transformed with pFP12 NspAA2594Tet as described above . Tetracycline resistant transformants were screened by PCR and Western blot with Me-7 . In addition the DNA sequence of the complementing nspA was verified . Expression of NspA was up-regulated in some strains that naturally express low levels of NspA by replacing the nspA promoter with the promoter of porA . In brief , an approximately 200 bp fragment of DNA containing the promoter region of porA was amplified by PCR from genomic DNA isolated from group B strain M986 using the following primers: 5′-CTCATCGATGGGCAAACACCCGATACG-3′ ( introducing site ClaI ) and 5′-CTCACGCGTGAGGTCTGCGCTTGAATTGTG-3′ ( introducing site MluI ) . This fragment was ligated into a 700 bp region upstream of nspA amplified by PCR from Z1092 genomic DNA using primers 5′-CATAAGCTTCGTAGCGGTATCCGGCTGC-3′ and 5′-CGCTGCCGAAGATTTGCCGGCAAATCTTCGGCAGCG-3′ . This , in turn , was ligated into the EcoRI and HindIII sites of the cloning vector pGEM3zf ( - ) ( Promega Corporation , Madison , Wisconsin ) . An erythromycin resistance cassette , ermB , was inserted upstream of the porA promoter within the fragment upstream of nspA . N . meningitidis strain Z1092 was transformed by adding plasmid DNA in 10 mM MgCl2 to colonies of Z1092 and incubating at 37°C enriched with 5% CO2 for 5 hours prior to plating onto chocolate agar with 5 µg/ml or erythromycin . The level of NspA expression in erythromycin-resistant colonies was analyzed by SDS-PAGE and Western blotting using murine antisera raised against His-tagged-NspA . All analyzed transformants expressed approximately 5-times the level of NspA when compared with the wild-type strain ( data not shown ) . DNA extracted from Z1092 overexpressing NspA was used to transform strain Y2220 and Y2220/L8 LOS; colonies resistant to erythromycin were screened for increased NspA production compared to the parent strain by Western blotting . E . coli BL21 ( DE3 ) pGMS 1 . 0 harboring a functional copy of nspA and the preparation of microvesicles that contain NspA have been described previously [40] . For simplicity , the capsule and LOS phenotype of each mutant has been designated as follows: encapsulated strains , Cap+; unencapsulated mutants , Cap−; sialylated lacto-N-neotetraose LOS , LNT sia+; unsialylated lacto-N-neotetraose LOS , LNT; LOS with lactose extension off HepI ( lgtA mutants ) , L8 LOS and LOS with no HepI saccharide extensions ( lgtF mutants ) , HepI unsubstituted . Serum collected from a healthy human volunteer without a history of meningococcal disease and who had not received any meningococcal vaccines ( normal human serum; NHS ) was aliquoted and stored at −80°C till used . Hemolytic activity of the serum was confirmed using the Total Haemolytic Complement assay ( Binding Site , Birmingham , U . K ) . Chimpanzee , baboon and rhesus sera were purchased from Bioreclamation ( Bioreclamation , Hicksville , NY ) . Complement activity in the sera was destroyed by heating at 56°C for 30 minutes . The serum used did not contain any fHbp- or NspA-specific antibodies as revealed by western blots of whole bacterial lysates that were probed with serum . Flow cytometry to detect bound fH was performed as described previously [44] . Briefly , bacteria grown overnight on chocolate agar plates were washed with Hanks Balanced Salt Solution ( HBSS ) containing 1mM Ca2+ and 1 mM Mg2+ ( HBSS++ ) and suspended to a final concentration of 3×108 cells/ml; 108 organisms were centrifuged and incubated with fH purified from human plasma ( Complement Technology , Inc . ; concentration specified for each experiment ) . Bound fH was detected using either affinity-isolated sheep anti-human fH ( Lifespan Biosciences ) or an anti-fH mAb ( Quidel , catalog no . A254 ( mAb 90× ) ) , as available . While the polyclonal antibody provided higher sensitivity , relative differences in fH binding among strains using the two reagents were similar . FITC conjugated anti-sheep IgG or anti-mouse IgG ( Sigma ) were used as secondary antibodies . All reaction mixtures were carried out in HBSS++/1% BSA in a final volume of 50 µl . Flow cytometry was performed using a FACSCalibur instrument ( Becton Dickinson ) and data analysis was performed using the FlowJo data analysis software package ( www . TreeStar . com ) . FH / murine Fc fusion constructs that contain contiguous fH SCR domains ( SCRs 1–5 , 1–7 , 6–10 , 11–15 , or 16–20 ) fused to the N-terminus of the Fc fragment of murine IgG2a ( fH/Fc fusion proteins ) have been described in detail previously [45] . To detect binding of recombinant fH/Fc fusion proteins , bacteria were incubated with concentrated tissue culture supernatant containing 0 . 5 µg of recombinant fH/Fc protein ( as determined by ELISA ) in a final reaction volume of 100 µl for 30 min at 37°C . After washing , FITC-labeled goat anti-mouse IgG ( Sigma-Aldrich ) diluted 1∶100 in 1% BSA/HBSS++ was used to detect bacteria-bound fH/Fc fusion proteins . Recombinant Factor H-like protein-1 ( FHL-1 ) was generated as previously described [44] . Following incubation of bacteria with 0 . 5 µg purified FHL-1 , bound FHL-1 was detected using monoclonal ( mAb ) 90× ( specific for SCR1; detects both full-length fH and FHL-1 ) and FITC-conjugated anti-mouse IgG ( Sigma ) as previously described [44] . In some experiments , mAbs against NspA ( mAb Me-7; IgG2a [57] and 14C7 [40] ) were used to block fH binding to intact bacteria; mAb P1 . 9 against outer membrane protein PorA of strain A2594 ( National Institute of Biological Standards and Control ) was used as a control . Bacteria were incubated with tissue culture supernatants containing mAbs Me-7 or P1 . 9 ( the concentration of mAb in supernatants was estimated by western blotting where serial dilutions of the supernatants were compared against purified mouse IgG standards of the same subclass ) or purified mAb 14C7 for 15 min at 37°C followed by addition of purified fH . The reaction mixture was incubated for an additional 15 min and bound fH was detected using sheep anti-human fH as described above . C3 deposition on bacteria that were incubated with normal human serum ( concentration specified for each experiment ) was measured using FITC-conjugated sheep anti-human C3 ( Biodesign/Meridian Life Science , Inc . ) as described previously [13] . Membranes were prepared from N . meningitidis strains A2594 and H44/76 and their fHbp deletion mutants as previously described [13] , [70] . Briefly , bacteria harvested from five plates after overnight culture on chocolate agar were suspended in normal saline . Bacteria were washed , suspended in 5 ml of PBS containing 10 mM EDTA , and incubated at 60°C for 30 min . The bacterial suspensions were sheared by sequential passage through progressively smaller-gauge needles ( 18- through 25-gauge ) . The resultant suspension was centrifuged at 5000×g for 10 min at 4°C to separate any intact cells and debris . The supernatant was collected and ultracentrifuged at 80 , 000×g for 90 min at 4°C to yield a pellet that was enriched in outer membranes . Far Western blotting was used to assess fH binding to membrane preparations as described previously [13] . Membrane proteins were separated on a 4–12% Bis-Tris gel ( Invitrogen Life Technologies ) using MOPS running buffer . Proteins were transferred to polyvinylidene difluoride membranes ( Millipore ) and blocked with PBS-1% dry milk for 30 min at room temperature . Blocked membranes were incubated overnight at 4°C with fH ( 1 µg/ml in PBS-0 . 05% Tween 20 ) . fH-binding proteins were detected using affinity-isolated sheep anti-human fH ( 1 µg/ml in PBS-0 . 05% Tween 20 ) and disclosed using anti-sheep IgG-alkaline phosphatase . Western blotting was also used to assess NspA and fHbp expression . To detect NspA , membranes were probed with tissue culture supernatants that contained anti-NspA mAb Me-7 followed by anti-mouse IgG alkaline phosphatase . To detect fHbp ( variant 1 , 2 and 3 ) , membranes were probed with rabbit polyclonal anti-serum that recognized variants 1 , 2 and 3 fHbp diluted 1∶1000 in TBS 0 . 02% Tween 20 followed by anti-rabbit IgG alkaline phosphatase . To ensure equal loading across lanes , membranes were incised horizontally at the level of the ∼40–50 kD marker prior to the blocking step and stained with Coomassie blue ( Imperial Protein Stain kit , Pierce ) . Binding of human , chimpanzee and rhesus macaque fH to neisserial strains was measured by Western blotting as described previously [45] . Briefly , 108 bacteria were suspended in HBSS2+ and incubated for 30 min at 37°C with 10% ( v/v ) heat-inactivated NHS or heat-inactivated chimpanzee or rhesus serum in a final reaction volume of 100 µl . Bacteria were washed three times in HBSS2+ , the bacterial pellets were lysed with lithium dodecyl sulfate sample buffer ( Invitrogen , Carlsbad CA ) and liberated fH was detected after electrophoresis and transfer to PVDF using goat polyclonal anti-human fH ( Bethyl Laboratories , Montgomery , TX ) that also recognizes chimpanzee and rhesus fH [45] , followed by alkaline phosphatase-conjugated anti-goat IgG ( Sigma ) . Human and the non-human primate sera alone served as positive controls . Outer membrane proteins were separated by electrophoresis as described above and stained with colloidal Coomassie brilliant blue ( Sigma-Aldrich ) . The band corresponding to the ∼17 kDa band that bound fH was excised , washed extensively and then digested “in gel” with trypsin as described elsewhere [71] . Digested peptides were further purified via micro Zip Tipping . Briefly , samples dried down to a 10 µl volume were acidified with 1–2 µl of 1% TFA and then loaded on a Zip Tipμ-C18 ( Millipore , Corp ) that had been pre-equilibrated with 0 . 1% TFA . After washing with twice with 10 µl aliquots of 0 . 1% , TFA samples were deposited directly onto the MALDI sample target using 1 µl of Matrix solution ( 15 mg/ml of 2 , 5-dihydroxybenzoic Acid ( MassPrep DHB , Waters Corp . ) in 50∶50 acetonitrile: 0 . 1% TFA ) . Samples were allowed to air dry prior to insertion into the mass spectrometer . Analysis was performed on a Kratos Axima QIT ( Shimadzu Instruments ) matrix-assisted-laser desorption/ionization ( MALDI ) mass spectrometer . Peptides were analyzed in positive ion mode in mid-mass range ( 700–3000 Da ) . The instrument was externally calibrated with Angiotensin II ( 1046 . 54 Da ) , P14R ( 1533 . 86 Da ) and ACTH ( 18–39 ) ( 2465 . 20 Da ) . Precursors were selected based on signal intensity at a mass resolution width of 250 for CID fragmentation using Argon as the collision gas . Database searches were performed in house with Mascot ( Matrix Sciences , Ltd . ) using the Peptide Mass Fingerprint program for MS data and the MS/MS Ion Search program for CID data . All identifications were confirmed or established with CID ( MS/MS ) data . Susceptibility of meningococci to complement mediated killing was determined using a serum bactericidal assay as described previously [44] , [72] . The optimal concentration of serum was determined empirically for each strain ( Supplementary Figure S5 ) . Bacteria from an overnight culture on chocolate agar plates were inoculated onto fresh chocolate agar and allowed to grow for ∼6 h at 37°C in 5% CO2 . Normal human serum was obtained from a healthy human volunteer and stored at −70°C till used in bactericidal assays . Briefly , 2000 CFUs of meningococci were incubated with serum ( concentrations specified for each experiment ) in a final reaction volume of 150 µl . Aliquots of 25 µl were plated in duplicate at the start of the assay ( t0 ) and after incubating the reaction mixture at 37°C for 30 min ( t30 ) . Survival was calculated as the number of viable colonies at t30 relative to baseline colony counts at t0 . Each experiment was repeated at least three times . E . coli BL21 ( DE3 ) ( Invitrogen , Carlsbad , CA ) harboring recombinant NspA on plasmid pGMS 1 . 0 and E . coli BL21 ( DE3 ) transformed with pBluescript II SK+ ( Stratagene , La Jolla , CA ) were used to prepare microvesicles as previously described [40] . An ELISA was used to detect fH binding to NspA containing vesicles . Microtiter wells were coated with either NspA-producing vesicles or with control vesicles each at a concentration of 10 µg/ml in PBS overnight at 22°C . Nonspecific biding sites were blocked with PBS/2 . 5% BSA for 2 h at 37°C . To demonstrate the ability of anti-NspA mAb 14C7 to block fH binding to NspA-containing vesicles , select wells were incubated with mAb 14C7 ( 10 µg/ml ) in PBS/0 . 05% Tween 20 for 1 h at 37°C; the remaining wells were incubated with PBS/Tween alone . fH ( concentrations ranging from 0 to 10 µg/ml ) was then added to wells for 1 h at 37°C , and bound fH was detected using polyclonal sheep anti-human fH followed by anti-sheep IgG conjugated with alkaline phosphatase , each for 1 h at 37°C . Cuzick's nonparametric test for trend across ordered groups [73] was used to determine if there was a trend between the binding of fH and the length of glycan extensions from the HepI chain of LOS . Median fluorescence values from three independent experiments were used in the analysis . Strains expressing LNT LOS , L8 LOS and unsubstituted LOS were ordered decreasingly by the length of the HepI glycans extensions and scored as 5 , 3 and 1 , respectively . The measurement of binding was divided by the value of the control for normalizing . The analysis was done separately for strains A2594 Cap+ and A2594 Cap− . The results showed a statistically significant trend between fH binding and decreasing length of HepI glycan extensions for both Cap+ and Cap− strains ( Table S2; results are the same for A2594 Cap+ and A2594 Cap− , p = 0 . 007 ) . For bactericidal assays the average survival was calculated from at least three independent experiments and error bars represent the standard deviation . A t-test was used to determine significance . | Neisseria meningitidis is an important cause of bacterial meningitis and sepsis worldwide . The complement system is a family of proteins that is critical for innate immune defenses against this pathogen . In order to successfully colonize humans and cause disease , the meningococcus must escape killing by the complement system . In this study we show that meningococci can use one of its surface proteins called Neisserial surface protein A ( NspA ) to bind to a host complement inhibitory protein called factor H ( fH ) . NspA is a protein vaccine candidate against group B meningococcal disease . Binding of fH limits complement activation on the bacterial surface and enhances the ability of the meningococcus to resist complement-dependent killing . Capsular polysaccharide expression decreases fH binding to NspA , while truncation of the core glycan chain of lipooligosaccharide increases fH binding to meningococcal NspA . Loss of NspA results in enhanced complement activation on the bacterial surface and increased complement-dependent killing of meningococci . Our findings have disclosed a novel function for NspA and sheds further light on how this pathogen evades killing by the complement system . | [
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] | 2010 | The Meningococcal Vaccine Candidate Neisserial Surface Protein A (NspA) Binds to Factor H and Enhances Meningococcal Resistance to Complement |
Cryptosporidium spp . infections are the most frequent parasitic cause of diarrhea in humans and cattle . However , asymptomatic cases are less often documented than symptomatic cases or cases with experimentally infected animals . Cryptosporidium ( C . ) hominis infection accounts for the majority of pediatric cases in several countries , while C . parvum is a major cause of diarrhea in neonatal calves . In cattle Cryptosporidium spp . infection can be caused by C . parvum , C . bovis , C . andersoni and C . ryanae , and recently , reports of cattle cases of C . hominis cryptosporidiosis cases suggest that the presence of C . hominis in calves was previously underestimated . From February to November 2015 , Cryptosporidium spp . infected calves were detected in 29/44 randomly included farms from 5 geographic regions of France . C . hominis and C . parvum were found in 12/44 and 26/44 farms , respectively with higher C . hominis prevalence in the western region . In 9 farms , both C . parvum and C . hominis were detected . Eighty-six of 412 ( 73/342 asymptomatic and 13/70 symptomatic ) one to nine-week-old calves shed C . hominis or C . parvum oocysts ( 15 and 71 calves , respectively ) , with no mixed infection detected . The predominant C . hominis IbA9G3 genotype was present in all regions , and more frequent in the western region . An incompletely characterized Ib , and the IbA13G3 , IbA9G2 and IbA14G2 genotypes were present only in the western region . For C . parvum , the most frequent genotype was IIaA16G3R1 with no geographic clustering . Most C . hominis infected calves were asymptomatic , with some exceptions of IbA9G2 and IbA9G3 isolates , while C . parvum IIaA16G3R1 was associated with symptoms . Present results indicate for the first time that in several geographic regions of France , C . hominis was present in about one fifth of both asymptomatic and symptomatic infected calves , with isolated genotypes likely associated with human infection . Further investigations are aimed at documenting direct or indirect transmissions between livestock and humans .
Cryptosporidium spp . are Apicomplexa which include parasite species causing asymptomatic to severe gastrointestinal infections in a wide range of vertebrate hosts , and exhibiting varying degrees of host adaptation [1] . Previously , information on cryptosporidial host restriction of natural cryptosporidial infection was usually obtained from animal and human cases for which clinical symptoms , ages and immune statuses were recorded . However , evidence of asymptomatic sustained or transitory infection , and the role of additional parameters such as parasite detection methods , host’s genetic background , co-infection and environmental factors such as climate , seasons and socioeconomic status were less documented [2] . For some cryptosporidial host specialisations , information is presently limited to experimentally infected immunocompetent or immunosuppressed laboratory animals [3] . In humans , cryptosporidiosis is presently identified as the most frequent zoonotic cause of parasitic diarrhea , especially severe in immunocompromised individuals and infants in both developed and developing countries [4–7] . In addition to 8 others sporadically observed species , C . hominis , once considered to be restricted to humans , and C . parvum , of which some isolate genotypes also infect ruminants , account for more than 90% of reported human cases worldwide [8–10] . There is equal or higher prevalence of C . hominis than C . parvum in humans in many parts of the world except in Europe where C . parvum largely prevails , likely reflecting the ratios of human to animal sources of anthroponotic C . hominis and anthropozoonotic C . parvum contamination , respectively [11–15] . In cattle , the main symptom of cryptosporidiosis is watery and profuse acute diarrhea which can be associated with dehydration , anorexia , and impaired growth [16] . It was previously established that cattle can be infected by at least 4 Cryptosporidium species , i . e . C . parvum , C . bovis , C . andersoni , and C ryanae [17 , 18] . In France , C . parvum , C . bovis and C . andersoni predominate in newborn and older calves , respectively , C . parvum infection is identified as a major cause of diarrhea in newborn calves of less than one month old , with economically significant morbidity and mortality . However , detailed epidemiology on the occurrence of viable oocysts from normal feces of asymptomatic calves is unknown [19 , 20] . Recently , a limited number of observations reported of cattle cryptosporidiosis due to C . hominis have been reported in Australasia , Asia , Africa and Europe , suggesting that the presence of C . hominis in calves was previously underestimated in studies on diarrheic and adult animals [21–27] . The aim of this work was to document the prevalence of Cryptosporidium spp oocysts in calves from five different geographic regions of Metropolitan France . Farms were randomly included in the study , the clinical status of each animal was recorded , and the presence of calves with Cryptosporidium spp . oocysts in feces was investigated . Isolates were genetically characterized for their synzootic and zoonotic potentials .
From February to November 2015 , 412 calves aged from 1 to 9 weeks were selected in 44 farms from a national list of farms under regular veterinarian survey ( 16 veterinary offices , from 1 to 4 farm ( s ) per office ) . Farms were randomly selected , and within farms , calves aged from 1 to 9 weeks were randomly selected . Selected farms were situated in 14 “départements” ( an administrative sub-region ) distributed in 5 geographic regions of Metropolitan France: western ( Côtes d'Armor , Ille-et-Vilaine , Morbihan ) , central western ( Vendée , Deux Sèvres , Mayenne ) , northeastern ( Pas-de- Calais , Moselle ) , southwestern ( Landes , Pyrénées atlantiques , Tarn , Hautes-Pyrénées ) , and central ( Puy-de-Dôme , Allier ) . For each calf , the clinical status was evaluated and recorded by all veterinarians at the time of sampling as follows: presence or absence of digestive symptoms such as diarrhea and abdominal bloating and/or respiratory symptoms , and evaluation of the general condition as follows: "Normal general condition: shiny hair coat , regular appetite; impaired general condition: delayed growth , dull hair coat , capricious appetite; Poor general condition: dull hair coat , capricious appetite , marked growth retardation . " From each farm ( housing from 11 to 50 calves ) , feces samples were obtained by veterinarians from 5–10 calves by rectal stimulation . Most farms were dairy farms and breeds consisted of Salers , Holstein , Charolais , Montbeliard , Blonde d'Aquitaine , Parthenaise , Limousine and the Belgian Blue Breed ( BBB ) . In all farms , calves were kept in semi-intensive farming systems and separated from their dams . The presence of Cryptosporidium spp . oocysts was microscopically determined by the same experienced clinical parasitologists using Bailenger type feces concentration method [28] and Heine staining [29] . The presence of other intestinal parasites ( Giardia , Strongyloides , and coccidia ) detected in some of the calves using various methodologies was not considered in the present study . All samples were subjected to molecular analysis for speciation and genotyping of speciation positive samples . Before DNA isolation , feces were subjected to a pre-treatment with a mechanical lysis in Lysing Matrix A Tubes ( garnet matrix and ¼ ceramic spheres ) ( Qiagen , CA , USA ) with the Fastprep-24 device and transferred into 2 ml Eppendorf tube prior to thermal shock lysis ( 6 freeze-thaw cycles ) . Samples were placed in an ultrasonic bath for sonication ( 3x20 sec bursts ) . In accordance with the manufacturer's instructions , a modified QIAamp Stool Mini Kit ( Qiagen , CA , USA ) was used to isolate DNA from the pre-treated samples . All centrifugation steps were performed at RT ( 20–25°C ) , at 14 . 000 rpm . Eight hundred μL/tube of ASL buffer was added , and tubes were heated at 99°C for 15 min . For speciation , a 18S rRNA gene sequence was amplified using a nested PCR and restriction digestion of the secondary product with SspI ( NEB , MA , USA ) and VspI ( NEB , MA , USA ) was performed [30] . Briefly , for the primary PCR step , a PCR product ( about 1 , 325 bp long ) was amplified by using primers 5-TTCTAGAGCTAATACATGCG-3 and 5-CCCTAATCCTTCGAAACAGGA-3 . For the secondary PCR step , by using 5 μl of the primary PCR product and primers 5-GGAAGGGTTGTATTTATTAGATAAAG-3’ and 5-AAGGAGTAAGGAACAACCTCCA-3 a PCR product ( 819 to 825 bp long , depending on the species ) was amplified . Each PCR mixture ( total volume , 50 μl ) contained 5 μl of 10X DreamTaq Buffer , each deoxynucleoside triphosphate at a concentration of 0 . 2mM , each primer at a concentration of 100 nM , 2 . 5 U of DreamTaq polymerase , and 5μL of DNA template . Then , 1 . 25μL of DMSO ( 100% ) was added to the mixture . A total of 40 cycles , each consisting of 94°C for 45 s , 55°C for 45 s , and 72°C for 1 min , were performed . An initial hot start at 94°C for 3 min and a final extension step at 72°C for 7 min were also included . Each amplification run included a negative control ( PCR water ) and two positive controls ( genomic DNA from C . parvum oocysts purchased from Waterborne Inc . , and C . hominis genomic DNA from fecal specimen collected at Rouen University Hospital ) . Products were visualized in 2% agarose gels using ethidium bromide staining and identification was confirmed by sequencing . Positive samples were further genotyped by DNA sequencing of the gp60 gene amplified by a nested PCR following the protocol described by Sulaiman et al . [31] ( 2005 ) . All Amplification experiments were repeated at least thrice to check reproducibility . Purified PCR products were sequenced in both directions on an ABI 3500 sequencer analyzer ( Applied Biosystems , CA , USA ) by using the secondary PCR primers and the BigDye Terminator v3 . 1 Cycle Sequencing Kit ( Applied Biosystems , CA , USA ) . The obtained sequences were inspected using the 4 peaks software ( https://nucleobytes . com/4peaks/index . html ) , edited with the BioEdit sequence alignment editor ( version 7 . 2 . 5 ) , and analyzed for DNA database search and comparisons using the BLAST server ( www . ncbi . nlm . nih . gov/BLAST ) . Genotypes were named using the established gp60 genotype nomenclature [31] . Statistical analyses were performed using the chi-square ( χ2 ) test or Fisher’s exact test as appropriate using the Number Cruncher Statistical System ( NCSS ) , version 2000 to determine the association between the prevalence of Cryptosporidium infection vs regions , and sampling periods . A p value <0 . 05 was considered statistically significant . Before carrying out this work , informed written authorization to perform and anonymously publish the present epidemiological study was obtained from all cattle owners and veterinarians . Clinical examination of calves and stool harvest were part of routine breeding and veterinary procedures , without any invasive , traumatic or specific containment method . Such procedures are not qualified as animal experimentation involving vertebrate according to French laws , and no specific ethical clearing was required .
As shown in Table 1 , infected calves were detected in 29/44 farms from all geographic regions and “départements” except Puy-de-Dôme and Mayenne , with no inter-regional difference in the ratios of the number of infected farms to the number of included farms . C . hominis and C . parvum were found in calves from 12/44 and 26/44 farms , respectively . In 9/44 farms , both C . parvum and C . hominis infected calves were found with no mixed infection in any animal . No mixed infection of C . hominis and C . parvum was noted in any animal . Eighty-six of 412 included calves exhibited Cryptosporidium spp . oocysts in feces , of which 15 and 71 had C . hominis and C . parvum infection respectively . There were no inter-regional differences in the ratio of the number of infected calves to the number of included calves ( p = 0 . 839 ) . In the western region , the ratio of C . hominis infected farms ( 8/13 ) was higher than in all other geographic regions ( p = 0 . 018 ) , while no interregional difference was found for C . parvum ( p = 0 . 122 ) . Infections were found in calves from 2 weeks to 7 weeks of age ( 3 to 7 weeks and 2 to 7 weeks for C . hominis and C . parvum cases , respectively ) . As shown in Table 2 , C . hominis IbA9G3 genotype isolates were predominant and present in all geographic regions . The incompletely characterized Ib and the IbA13G3 , Ib A9G2 and IbA14G2 genotypes were only represented in the western region . For C . parvum , IIaA16G3R1 genotype was the most frequent with no geographic clustering ( p = 0 . 574 ) , and the limited number of isolates exhibiting other genotypes precluded further investigation on their geographic representation ( Table 3 ) . No C . hominis infected calves exhibited diarrhea during the week before or at the time of stool sampling ( Table 4 ) . Two infected calves , one with genotype IbA9G2 and one with genotype IbA9G3 presented with general state alteration and non-diarrheal digestive symptoms . Ten calves infected with the most frequent C . parvum IIaA16G3R1 genotype presented with digestive symptoms , respiratory symptoms , or both , and 12 exhibited an impaired or poor general state . The limited number of observations , precluded from investigating further associations between symptoms and genotypes . The ratios of the number of infected calves to the number of sampled calves observed during the August-September ( 50/216 ) and October-November ( 28/106 ) periods were higher than those during February to March ( 3/45 ) and April to June ( 5/42 ) periods ( p = 0 . 010 ) with no difference in the respective C . hominis and C . parvum representations ( the corresponding values for C . hominis were 9/50 , 4/28 , 2/3 , and 0/5 , respectively , p>0 . 05 ) . No seasonal effect on Cryptosporidium infection prevalence , however , could be unambiguously established , taking into account that due to the yearly calving cycle , most calves were sampled during summer and autumn , and that no feces sample was obtained in January , July and December . The age distribution of C . parvum infected calves is similar to that of C . hominis infected calves ( Fig 1 )
Present results indicate for the first time that in several geographic regions of France , C . hominis was present in about one fifth of both asymptomatic and symptomatic calves , and exhibited genotypes likely linked to human infection . Cattle have been considered to be a primary reservoir for Cryptosporidium spp . and to play a role in transmitting zoonotic C . parvum organisms to humans [44 , 45] . Results of the present study suggest that calves in France also frequently harbor C . hominis isolates which might be cause of human infections . Further investigations are aimed at determining whether the source of cattle infections was other livestock or humans , and whether the transmission was direct or indirect . | Symptomatic infection by the Apicomplexan Cryptosporidium spp . is presently considered the most frequent parasitic cause of acute diarrhea in both humans ( especially severe in immunocompromised individuals and infants in both developed and developing countries ) and cattle ( calves ) , while asymptomatic infections are less often documented . Cryptosporidium ( C . ) hominis once considered to be restricted to humans accounts for the majority of pediatric cases in several countries . C . parvum can also infect cattle as well as C . bovis , C . andersoni , and C . ryanae . Recently , cattle C . hominis cryptosporidiosis has been reported , suggesting that the presence of C . hominis in calves was previously underestimated . The aim of this work was to characterize Cryptosporidium spp . infection in both asymptomatic and symptomatic dairy and beef calves from Metropolitan France . From February to November 2015 , C . parvum or C . hominis infected calves were detected in farms from 5 geographic regions of France . Surprisingly , C . hominis was present in about one fifth of Cryptosporidium spp . infected calves , and exhibited genotypes which were previously reported in human and nonhuman primate . Further investigations are aimed at documenting direct or indirect C . hominis transmissions between and among livestock and humans . | [
"Abstract",
"Introduction",
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] | [
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"apico... | 2018 | Common occurrence of Cryptosporidium hominis in asymptomatic and symptomatic calves in France |
The Gene Ontology ( GO ) is a collaborative effort that provides structured vocabularies for annotating the molecular function , biological role , and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms . Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism ( s ) they are annotating . Here we introduce the Reference Genome project , which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms . The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project , and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms . In addition , the project has several important incidental benefits , such as increasing annotation consistency across genome databases , and providing important improvements to the GO's logical structure and biological content .
The functional annotation of gene products , both proteins and RNAs , is a major endeavor that requires a judicious mix of manual analysis and computational tools . The manual aspect of this annotation task is carried out by curators , from the Latin cure: to look after and preserve . A curator in this context is a Ph . D . trained professional life scientist whose task is to meaningfully integrate published , and in some cases unpublished , biological data into a database [1] , [2] . The GO was developed within the community of the Model Organism Databases ( MODs ) , whose goal is to annotate the genomes of organisms having important impact on biomedical research [3] , [4] . The GO consists of over 26 , 000 terms arranged in three “branches”: molecular function , biological process , and cellular component . Terms are related to each other by well-defined relationships , particularly by a subsumption relationship ( is_a ) , a partitive relationship ( part_of ) and relationships which denote biological regulation ( regulates ) . GO is one of the most widely used tools for functional annotation , particularly in the analysis of data from high throughput experiments . GO terms are manually associated with gene products by curators using two general methods: extracting annotations based on published experimental data; and inferring annotations based on homology with related gene products for which experimental data is available . Automated methods that are based on either sequence similarity or domain composition are also used to make annotations without curator intervention . These different methods of assigning GO terms to gene products are distinguished by the use of different GO evidence codes [5] . The comprehensive annotation of a genome entails assigning functions to all gene products , including those that have not yet been experimentally characterized . The annotations based on experimental data provide a solid , dependable substrate for downstream analyses to infer the functions of related gene products . High-quality manual annotation by experts is an absolute prerequisite for seeding this system and , other than the major MOD projects and large sequence databse projects ( such as UniProt and Reactome ) , very few research communities have the resources or trained GO curators to perform this labor-intensive task . Therefore , the functional annotation of non-manually curated genomes typically relies on automated methods that provide the core information for the transfer of annotations from related genes for which experimentally supported annotations are available . The GO Reference Genome project is committed to providing comprehensive GO annotations for the human genome , as well as that of eleven important model organisms: Arabidopsis thaliana , Caenorhabditis elegans , Danio rerio , Dictyostelium discoideum , Drosophila melanogaster , Escherichia coli , Gallus gallus , Mus musculus , Rattus norvegicus , Saccharomyces cerevisiae , and Schizosaccharomyces pombe . Collectively those twelve species are referred to as the “GO Reference Genomes” . Each model organism has its own advantages for studying different aspects of gene function , ranging from basic metabolic reactions to cellular processes , development , physiology , behavior , and disease . The organisms selected to provide this gold-standard reference set have the following characteristics: they represent a wide range of the phylogenetic spectrum; they are the basis of a significant body of scientific literature; a reasonably sized community of researchers study the organism; and the organism is an important experimental system for the study of human disease , or for economically important activities such as agriculture . Importantly , all of these organisms are supported by an established database that includes GO curators who have the expertise to annotate gene products in these genomes according to shared , rigorous standards set by the groups participating in the Reference Genome project ( see below ) . Although the development of the GO has been a collaborative effort since its inception , each participating group has previously worked independently in assigning GO annotations . Thus , prior to this project , specific protocols for annotation varied greatly between the different databases . Variation in annotation results from different curator decisions as to which data is appropriate to annotate and which GO terms to employ . [6] , [7] . Other discrepancies in annotations come from the use of different methods to perform “automated annotations” ( primarily based on comparisons of homologous genes ) by each of the different groups . Those two factors contribute to the inconsistencies observed among propagated annotations [8]–[11] . To address this issue , it was decided that the groups would simultaneously curate a number of homologous genes to provide an opportunity for improving the accuracy and consistency of the annotations made by the different groups . This strategy has the additional benefit of improving the ontology , since several curators working simultaneously with particular nodes of the GO structure can collaboratively identify omissions , ambiguities or logical inconsistencies in the GO and work towards their resolution with the ontology editors . We expect these reference annotations to have two important applications . First , they will increase the quality of the annotations provided by the GO Consortium , with a focus on providing precise annotations for each gene and the broadest possible coverage of each genome . Second , the gold-standard annotation set will greatly accelerate the annotation of new genomes where extensive experimental data on gene function or the resources and expertise to perform the annotations are unavailable .
One major advantage of annotating several genomes concurrently is the ability to carry out parallel annotations on homologous genes . Annotating several genes in a single step improves annotation efficiency . Moreover , it improves breadth of annotations by allowing easy access to known function of related genes . Finally , concurrent annotation of gene families across different databases promotes annotation consistency .
Gene products selected for concurrent annotation in the course of the Reference Genome project have improved the breadth and depth of annotation coverage . As of November 2008 , we have annotated approximately 4 , 000 gene products . These genes have a higher percentage of annotations derived from published experimental research . Moreover , the annotation of these genes is significantly more detailed relative to when we started this project . Initially , 34% of the 4 , 000 genes had annotations supported by experimental data . Now , there are 71% , a 2-fold increase; while a randomly selected sample with the same number of genes , has only 52% , a 1 . 5-fold increase . We might expect the Reference Genome project to yield annotations to more specific terms . Given some specificity metric for a term , we can calculate the average specificity of terms used in annotations for Reference Genome genes and compare these against the average specificity of annotations as a whole , and observe whether there has been an overall increase in specificity . Unfortunately , there is no single perfect measure of specificity . The depth of a term in the graph structure is often a poor proxy , as this is open to ontology structure bias . In this paper we use the Shannon Information Content ( IC ) as a proxy for specificity of a term . The IC of a term reflects the frequency of annotations to that term ( or to descendants of that term ) , with frequently used terms yielding a lower score than infrequently used terms . The IC is calculated as follows:where p ( t ) is the probability of a gene being annotated at or below t . For example , 2 . 75% of genes in the GO database are currently annotated to ‘transmembrane receptor activity’ , so this yields an IC of 5 . 18 . In contrast , the more specific term GABA-B receptor activity is used for only 0 . 01% of genes , so this yields a higher IC of 13 . 29 . Because annotations are propagated up the graph , the IC score must increase monotonically according to the depth in the graph – no term can have a higher IC than its descendants . But unlike the depth of the term , the IC is less open to ontology structure bias , as it is based on annotation frequency . However , the IC is subject to annotation or literature bias – if the annotated literature corpus happens to include lots of papers on transmembrane receptors , then the increased frequency of annotations will result in a lower IC . The IC is also subject to change as the annotation database changes . However , as the IC is based on the frequency rather than total number of annotations , we do not expect the IC to change radically with the annotation of new genes . We might expect a slight decrease in the IC of a term over time as annotation breadth increases , and with it the frequency of term usage . We can measure the increase in IC on a gene set over time by measuring the average IC of the terms used to annotate the genes in that set before and after reference genome curation . Genes can have multiple annotations in each of the three branches of the GO; here we take the maximum IC within each branch . We then calculate the average of this maximum IC for all genes in a set to get a measure of the annotation specificity for that set . We compared this number for two sets of genes: the group of all annotated genes for all 12 gene reference genome species ( which corresponds to approximately 200 , 000 genes ) , and the subset of this set corresponding to those genes that have been selected for thorough annotation . We then averaged the maximum IC values for both sets of genes before being selected for annotation by the Reference Genome project ( July 2006 ) and again with the most recent set of annotations ( December 2008 ) . The results , shown in Table 1 , are broken down by branch . For non-reference genome genes , the maximal IC has remained relatively constant or has decreased slightly . This small decrease is expected , as annotation gaps are filled in . We measured the improvement in average maximum IC of the set of reference genome annotated genes versus the baseline . As we might expect , there is an overall improvement in specificity of annotations , with annotations to biological process improving the most: the information content of the genes selected for thorough annotation has increased by about 2 for cellular component and molecular function , and by 2 . 44 for biological process . Since the improvement is logarithmic , an increase in 1 . 0 means that on average a typical gene gets annotated with a new term that is used with half the frequency of the previous most informative term . Another measure of the depth and breadth of GO annotations is what range of the ontology graph they cover . The graph coverage of a gene is the size of the set of terms used to annotate a gene , plus all ancestors of that term . In July 2006 , the average graph coverage per reference genome gene in a reference species was 34 . 7 , versus an average of 22 . 9 over all genes in all 12 species . In December 2008 this increased to 64 . 0 versus 27 . 0 . This shows that the coverage of genes selected for the reference set is proportionally higher , 1 . 84 versus 1 . 18 . The collaborative annotation of a group of similar gene products has also proven to be useful for the development of GO itself . For example , as a direct consequence of the Reference Genome project , 223 ontology changes or term modifications were made ( corresponding to slightly more than 10% of the total ontology change requests during this period ) . Examples of requested new terms include “regulation of NAD ( P ) H oxidase activity” , “DNA 5′-adenosine monophosphate hydrolase activity” , “neurofilament bundle assembly” , and “quinolinate metabolic process” . We have also enhanced the ontology by adding synonyms ( for example , “Y-form DNA binding” is now a synonym of “forked DNA binding” ) , improving definitions , and correcting inconsistencies . Examples of terms where definitions and inconsistencies have been corrected include “electron transport” ( replaced by two terms: “electron transport chain” and “oxidation reduction” ) , and “secretory pathway” ( replaced by two terms: “exocytosis” and “vesicle-mediated transport” ) . GO annotations may be viewed using AmiGO , the GOC browser ( http://amigo . geneontology . org/ ) [21] . In the latest release of AmiGO a number of new displays are available that are specifically designed for public browsing of data from the Reference Genome project . For each homolog set there is a link to a “Comparison Graph” that allows the user to easily visualize the common functions for each member in gene family as well as those particular to certain organisms or groups of organisms as shown in Figure 3 .
Access to all GOC software and data is free and without constraints of any kind . An overview of the project as well as links to all resources described below can be found at http://geneontology . org/GO . refgenome . shtml . Annotations made by the databases participating in the Reference Genome project are available from the GOC website in gene_association file format ( http://geneontology . org/GO . current . annotations . shtml ) . The protein sequence datasets are available for the community as a standardized resource from http://geneontology . org/gp2protein/ , and as FASTA sequence files here: ftp://ftp . pantherdb . org/genome/pthr7 . 0 . These sets provide a representative protein sequence for each protein-coding gene in each genome , cross-referenced to UniProt whenever possible , but augmented with RefSeq and Ensembl protein identifiers as well . The exact queries used to gather statistics for the annotation improvement reports can be found at: http://geneontology . org/GO . database . schema-with-views . shtml . | Biological research is increasingly dependent on the availability of well-structured representations of biological data with detailed , accurate descriptions provided by the curators of the data repositories . The Reference Genome project's goal is to provide comprehensive functional annotation for the genomes of human as well as eleven organisms that are important models in biomedical research . To achieve this , we have developed an approach that superposes experimentally-based annotations onto the leaves of phylogenetic trees and then we manually annotate the function of the common ancestors , predicated on the assumption that the ancestors possessed the experimentally determined functions that are held in common at these leaves , and that these functions are likely to be conserved in all other descendents of each family . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/bio-ontology",
"computational",
"biology/genomics"
] | 2009 | The Gene Ontology's Reference Genome Project: A Unified Framework for Functional Annotation across Species |
Nucleoside analogs used in antiretroviral treatment have been associated with mitochondrial toxicity . The polymerase-γ hypothesis states that this toxicity stems from the analogs' inhibition of the mitochondrial DNA polymerase ( polymerase-γ ) leading to mitochondrial DNA ( mtDNA ) depletion . We have constructed a computational model of the interaction of polymerase-γ with activated nucleoside and nucleotide analog drugs , based on experimentally measured reaction rates and base excision rates , together with the mtDNA genome size , the human mtDNA sequence , and mitochondrial dNTP concentrations . The model predicts an approximately 1000-fold difference in the activated drug concentration required for a 50% probability of mtDNA strand termination between the activated di-deoxy analogs d4T , ddC , and ddI ( activated to ddA ) and the activated forms of the analogs 3TC , TDF , AZT , FTC , and ABC . These predictions are supported by experimental and clinical data showing significantly greater mtDNA depletion in cell culture and patient samples caused by the di-deoxy analog drugs . For zidovudine ( AZT ) we calculated a very low mtDNA replication termination probability , in contrast to its reported mitochondrial toxicity in vitro and clinically . Therefore AZT mitochondrial toxicity is likely due to a mechanism that does not involve strand termination of mtDNA replication .
Polymerase-γ ( pol–γ ) is the only polymerase responsible for mitochondrial DNA replication . While pol-γ is not believed to directly regulate mtDNA levels , pathogenic mutations in the gene POLG do affect the stability of mtDNA and cause mtDNA depletion [5] . Polymorphisms found in the POLG gene in the human population may cause a natural variability in the activity of this complex enzyme and may conceivably play a role in patient variability in NRTI drug toxicities . In a study conducted by Martin et al . [6] the approved NRTIs were shown to inhibit various host DNA polymerases . After the HIV Reverse Transcriptase , the highest affinity of the NRTIs was for polymerase-γ . This , along with the fact that many of the NRTI side-effects resemble symptoms of mitochondrial genetic disorders , implicated interaction with polymerase-γ and subsequent depletion of mtDNA as a potential cause of NRTI toxicity giving rise to the polymerase-γ hypothesis [7] . Indeed , experiments have demonstrated decreased mtDNA amounts in various tissue types of NRTI-treated HIV positive patients [8]–[11] . In addition , mtDNA depletion was observed in parallel with cell death , mitochondrial morphological changes , and increased lactate production in liver , heart , neuron , skeletal muscle , adipose , and blood cell cultures after incubation with different NRTIs [12]–[20] . The possible polymerase-γ dependent toxicity mechanisms that comprise the polymerase-γ hypothesis are ( i ) direct inhibition of polymerase-γ by NRTI-triphosphate without incorporation into the mtDNA , ( ii ) chain termination of mtDNA replication following incorporation of the NRTI triphosphate , and ( iii ) incorporation of the analog triphosphate into mtDNA without chain-termination allowing the NRTI to continue as a point mutation in mtDNA [21] . However , there also exists a substantial body of data that are not consistent with toxicity mechanisms resulting in depletion of mtDNA . Martin et al . [6] showed no association between inhibition of polymerase-γ by NRTIs and mtDNA depletion . Mitochondrial dysfunction has been observed in vitro in mouse muscle , white adipose , brain , liver , and heart tissue [22] , hepatoma cell lines [20] as well as CD4 cells [19] after incubation with NRTIs although no significant decrease in mtDNA amount was observed . Particularly , incubation of liver and skeletal muscle cells with ddC , ddI , d4T , and AZT show a higher rate of lactate production in the presence of AZT , but the least amount of mtDNA depletion [15] , [18] . In clinical settings mtDNA depletion has been seen in parallel with normal cytochrome c oxidase activity , a sign of correct mitochondrial function [23] , and was not associated with lipoatrophy [24] ( although that study measured mtDNA depletion in blood samples , not fat cells ) . Taken together , these findings indicate a weak relationship between mtDNA copy number and nucleoside analog toxicity . This warrants a deeper look at the data concerning the interaction of different NRTIs with polymerase-γ . To this end , we have simulated the DNA replication process of mitochondria . Using enzyme kinetics data gathered from Johnson et al . [25] , Feng et al . [26] , and Hanes et al . [27] , [28] we have carried out a series of simulations of mtDNA replication in the presence of various nucleoside analogs that interact with polymerase-γ ( Table 2 ) . These simulations bridge the gap between the basic enzyme kinetics data and the probability of failure of the mtDNA replication process .
Thirteen analogs were used in the simulations ( Table 1 ) . These included eight drugs of the NRTI class currently approved for human treatment- stavudine ( d4T ) , lamivudine ( 3TC ( − ) ) , zidovudine ( AZT ) , zalcitabine ( ddC ) , didanosine ( ddI ) ( whose active form is dideoxyadenosine ( ddA ) triphosphate ) , abacavir ( ABC ) , emtricitabine ( FTC ( − ) ) and tenofovir ( TDF ) [1] , and one anti-herpes drug , acyclovir ( ACV ) . In addition we modeled the effects of the natural enantiomers of FTC ( + ) and 3TC ( + ) that have been used to explore a possible role of stereochemistry in the efficacy of strand termination [29] , and ddI in its non-activated form . Since this study focuses on strand termination , we have not included FIAU , an anti-hepatitis B drug that tragically resulted in the deaths of five patients in phase 2 trials and whose toxicity is believed to be due to errors in mtDNA replication [30] , [31] , though not necessarily through strand termination [25] , [31] . Our computational model of the mitochondrial DNA replication process is based on the Stochastic Simulation Algorithm [32] , [33] , a well-known Monte Carlo simulation method for chemical reactions . The model is based on four reactions; DNA polymerase activity , exonuclease activity , disassociation of the polymerase from the DNA , and reassociation of the polymerase with the DNA molecule ( Figure 1 ) . In the DNA polymerase reaction pol-γ adds one nucleotide to the new DNA strand . This nucleotide may be the correct or incorrect ( point mutation ) base indicated by the template strand . In this model this includes the incorporation of nucleoside analog triphosphates . In the exonuclease reaction pol-γ removes one nucleotide from the new DNA strand . This includes the removal of nucleoside analogs from the DNA strand . The exonuclease reaction is an error correction mechanism , as the rate for removal of incorrectly incorporated nucleotides is typically faster than that of correctly incorporated nucleotides . In the disassociation reaction the polymerase separates from the DNA molecule . In the reassociation reaction the polymerase re-attaches to the DNA molecule after disassociation . At each position on the replicating mtDNA strand , pol-γ will randomly undergo one of the first three reactions ( polymerase , exonuclease , or dissociation ) . Which reaction pol-γ undergoes is determined by the probability of each reaction , calculated using the reactions rates and Michaelis-Menten kinetics . For the two scenarios of a correctly inserted and incorrectly inserted previous nucleotide we have separate sets of kinetic parameters for each of the pol–γ reactions [34]–[36] . These studies have reported an increase in exonuclease and disassociation rates , but a decrease in incorporation rates by pol-γ following an incorrect incorporation . This is included in the simulation model by using two sets of enzyme kinetics parameters , one set for reactions following a correct incorporation and another set for reactions following an incorrect incorporation . Kinetic parameters for the natural nucleotide ( dNTP ) interaction with pol-γ are available in Text S1 . As data regarding the reassociation reaction rate are not available our model assumes that after a disassociation event occurs the reassociation reaction follows , except in the special case discussed immediately below . Since the rate for the reassociation reaction is not available , the time required for that reaction is not calculated in this model . This approximation is not important to our results reported here which focus on strand termination probabilities . Upon incorporation of an analog into the new DNA strand the next polymerase reaction is blocked . The exonuclease reaction can still occur , removing the analog molecule . However , if a disassociation reaction occurs before the analog can be removed , we assume that reassociation of the DNA polymerase is also blocked and the mtDNA replication event is disrupted resulting in strand termination ( Figure 1 ) . There has been some speculation that the drugs , in particular AZT , may be inserted into a replicating mtDNA strand without causing strand termination . In this model we take the conservative assumption that all NRTIs that are inserted in the mtDNA strand and not subsequently removed cause strand termination . Parameters included in the model for incorporation of each analog by pol- γ were the concentration necessary for binding of 50% of available pol-γ ( Km ) , the rate of polymerization ( kpol ) , , and the rate of excision ( Vexo ) of each analog by pol-γ . The parameters kpol and Km were estimated from the maximum rate of incorporation by pol-γ ( kcat ) and the dissociation constant from pol-γ ( Kd ) , respectively , obtained under pre-steady state conditions [25]–[27] . A recent publication shows that pyrophosphate release from AZT is uniquely slow during polymerization and that kinetics measured during steady-state conditions give a more accurate kpol estimation [27] . These measurements were carried out on AZT due to the fact that under pre-steady state conditions a decrease in incorporation rate was observed with increased AZT concentration indicating reversible binding . This pattern was not seen with any of the other analogs studied ( d4T , 3TC ( − ) , AZT , ddC , ddI , ddA , ABC , TDF , and 3TC ( + ) ) and for this reason reanalysis of the enzyme kinetics for those drugs was not performed in that experiment . Given this continuing evolution in our understanding of the AZT kinetics we carried out two simulations for AZT insertion using the two available published sets of parameters determined under steady-state conditions in the 2007 paper by Hanes and Johnson [27] and pre-steady state conditions published in the 2001 paper by Johnson et al . [25] . We distinguished the results using these two parameter sets as AZT2001 and AZT2007 . These parameter values , as well as those for the other analogs , are given in Table 2 . The polymerization reaction rates are functions of the dNTP concentrations . For this calculation we consider three sets of dNTP concentrations , representing high , medium and low concentration conditions . Mitochondrial dNTP levels were estimated following the observations of Rampazzo et al and Ferraro et al [37] , [38] ( Table 3 ) . The units of picomole of mitochondrial dNTP per mg of mitochondria or picomoles per 106 cells were converted to µM by using an assumed mitochondrial volume of 0 . 2 femtoliters and density measurements from Pollak and Munn [39] . It should be noted that these density measurements considered mitochondria as discrete entities not taking in to account any change in mitochondrial size due to organelle fission and fusion processes . We use these values only as estimates , in order to define the three categories of dNTP concentrations given below . Vertebrate mitochondrial DNA has a highly asymmetric G content . The low-G strand is labeled the light strand , with the complement strand called the heavy strand . Sets of simulations were carried out separately for the light strand sequence ( NCBI , gi 17981852 ) and the heavy strand sequence . On each template three separate simulation sets were carried out using the high , medium , or low natural nucleotide concentrations described above and varying the concentration of activated triphosphorylated analog . Each simulation was repeated 1000 times . The number of simulated mtDNA strand replications ending in a strand termination event ( caused by a nucleoside analog incorporation ) was recorded . The concentrations of the four dNTP pools and the activated analog were held constant throughout each simulated replication .
By measuring the probability of strand termination in the simulation as a function of the activated drug concentration , dose response curves for each drug were calculated . Figure 2 shows the dose response curves obtained for the strand termination probability of each clinically approved analog as a function of the analog mitochondrial concentrations . The concentration at which these dose response curves passed 50% defined the IC50 values for each activated drug ( Table 4 ) . In our model , replication was terminated once an analog was inserted and failed to be removed by exonuclease activity . Based on these simulated IC50 values the list of analogs in the order of decreasing probability of mtDNA strand termination on the light strand was: ddC = ddA = d4T>FTC ( + ) >3TC ( + ) >ACV>ddI>3TC ( − ) = TDF> = AZT2001>>FTC ( − ) >ABC = AZT2007 in which “>>” indicates a 10 fold difference or more , “>” indicates a 2 to 10 fold difference and “ = ” indicates a less than 2 fold difference . Note that ddA is the activated form of ddI . Of this list only d4T , ddI , ddC , 3TC ( − ) , TDF , AZT , ABC , ACV and FTC ( − ) are approved for therapeutic use . The IC50 list showed that the “di-deoxy drugs” , meaning ddC , d4T , and ddA , had the highest probability of causing mtDNA strand termination during replication while FTC ( − ) , ABC and AZT2007 showed the least . Of those drugs approved for HIV treatment there was an observed difference of more than 800 fold between the di-deoxy drugs ( ddC , ddA , and d4T ) and other approved drugs ( 3TC , TDF , AZT , ABC , and FTC ( − ) ) in the activated drug concentration necessary for 50% probability of mtDNA strand termination . The only difference seen in the simulation of heavy strand replication ( Text S1 ) was that acyclovir had a slightly higher probability of termination than 3TC ( + ) and ABC had approximately equal probability of termination as FTC ( − ) . Since there was little difference in the results for the two strands of the mtDNA molecule , we concentrated on results from the light strand . For the readers' convenience in interpreting these IC50 values , reported ranges of intracellular concentrations [42]–[48] for activated nucleoside analog drugs measured in peripheral blood mononuclear cells in patients are given in Table 5 . Where necessary , values were converted to units of µM using the conversion of Kewn et al [45] . However it should be kept in mind that these concentrations are intracellular values , not the concentration values in the mitochondria which may be different . For most analogs , the simulated dose response curve increases to 100% probability of strand termination if the analog concentration is raised high enough . AZT2007 , ABC and FTC ( − ) behaved differently from the other analogs in that they reached the point of saturation below 100% probability of strand termination ( Figure 2 ) , and in some cases the strand termination probability saturated below 50% , meaning that no IC50 values could be defined in those cases ( the blank entries in Table 4 ) . These three analogs interact so poorly with pol-γ that over the finite length of the mtDNA sequence ( approximately 16 , 600 base pairs ) these analogs have too few chances to incorporate into the growing mtDNA strand for the probability of strand termination to approach 100% , even with very large concentrations of the activated drug in the mitochondrion . When the recently revised steady-state derived parameters for kcat and Km for AZT [27] were used in the simulation , AZT2007 did not reach a 50% probability of strand termination in the presence of normal to high dTTP levels , instead saturating at a 23% probability . This grouped AZT2007 with ABC as having the least probability of causing termination of the replicating mtDNA . A common measurement for the relative likelihood of strand termination by each analog is the specificity constant [25] determined by the ratio kcat/Km for the incorporation of an analog by pol-γ . This is a common measurement used for predicting the discrimination of analogs by pol-γ over the natural nucleotide substrate [25]–[28] . The drawback in taking this measurement of direct interaction with pol-γ as a predictor for the actual incorporation into the replicating mtDNA strand is that the specificity constant does not consider exonuclease activity , mitochondrial dNTP levels , nor strand length , all of which can affect the probability that an analog will be incorporated . All of these factors of the system are integrated into our computational model and the resulting IC50 values . Previous studies [25]–[28] provide a list of increasing specificity constants of: ddC>ddA>d4T>>ACV>3TC ( − ) >TDF>AZT>>ABC = FTC ( − ) . The order of this list agreed quite well with our list given above based on simulated mtDNA strand termination . This agreement validates the use of kcat/Km values as an appropriate proxy for the relative probability of incorporation of these NRTIs by pol-γ . A quantitative comparison between the specificity constant and our calculated IC50 for strand termination is given in Figure 3 . The very low exonuclease reaction rate for each analog is the primary reason why the specificity constant serves as a reasonable prediction of mtDNA strand termination . The exonuclease reaction rates used in this study were taken from Johnson et al . [25] . Low excision rates for NRTIs have also been documented in the case of ddC [49] and using yeast mtDNA polymerase with ddC and AZT [50] . However , 3TC has a non-negligible measured exonuclease rates ( Table 2 ) [25] . Whenever an analog is inserted into the DNA strand , our pol-γ model assumed that only the exonuclease and pol-γ dissociation reactions can occur . Based on this model , in Table 6 we give the predicted probability Pexo of the analog removalwhere Vexo is the rate of exonuclease reaction for the analog and Vdis is the rate of disassociation of the polymerase . To test these predictions , we carried out a set of simulations with the analog exonuclease reactions removed . The ratio of the IC50 value in the full model to the IC50 value in the exonuclease deficient model was in very good agreement with the 1-Pexo values ( Table 6 ) . As predicted , only the two 3TC forms showed significant effects from the removal of the analog exonuclease reaction . Even in these cases the effect of the exonuclease reaction only shifted the IC50 value by a factor of 2 or less . The current therapy for HIV infections involves a combination of nucleoside analog drugs , along with another class of drug such as a protease inhibitor . It has been reported that combining nucleoside analogs increases toxicity [51] , [52] . The pol-γ model is a series of reactions occurring as the DNA polymerase moves along the template strand . At each position on the DNA strand different nucleoside analogs would be able to be incorporated into the DNA strand . For example , AZT triphosphate molecules would only have a reasonable rate of incorporation opposite an A on the template strand , while 3TC triphosphate molecules would only have a reasonable rate of incorporation opposite a G on the template . Considering this , it is unlikely that there could be a combined effect of two analogs of different nucleosides on strand termination through the pol-γ interaction alone . To test this , we modeled the effects of two analogs , AZT and 3TC , separately and in combination ( Figure 4 ) . The combination of AZT and 3TC has been shown to have enhanced toxicity [51] , [52] , though neither of these two studies found any significant mtDNA depletion associated with this toxicity . If we define PAZT as the probability of strand termination from a given concentration of AZT triphosphate and P3TC as the probability from a given 3TC triphosphate concentration , then the combination of the two drugs should result in a strand termination probability of This equation assumes there is no interaction between the two nucleoside analog drugs ( this is known as the Webb fractional effect [53] ) . Note that PAZT+3TC is here defined as one minus the probability that neither AZT nor 3TC independently cause strand termination . A set of 1000 simulations was repeated 10 times , using the medium dNTP concentrations defined in Table 3 , and mean and standard deviations for the probabilities PAZT , P3TC and PAZT+3TC were measured . The results for PAZT+3TC were consistent with the probability expected assuming no interaction between the two drugs ( Figure 4 ) . This indicates that any synergistic effects of multiple NRTIs on mitochondrial toxicity are not consequences of direct strand termination . Alternative explanations for synergistic effects may include competitive inhibition of deoxynucleotide phosphorylation , which is outside the limits of this computational model .
The di-deoxy NRTIs ( ddC , ddA , and d4T ) showed the greatest risk of strand termination in our simulations , indicated by their predicted low IC50 values . This agrees with previous studies showing they are the NRTIs most associated with mtDNA depletion in vitro [14]–[16] , [18] , [19] , [55] and mitochondria-related toxicities clinically placing them as alternative drugs in the federal guidelines [1] . In both muscle and subcutaneous fat biopsies of HIV+ patients , mtDNA levels were significantly lower in those on di-deoxy drug regimens as opposed to those on non-di-deoxy NRTI regimens [8] , [11] . Even though the toxic side effects of di-deoxy drugs are well known and the in vitro effects on tissue mtDNA levels of these drugs are in agreement with our simulation results , the very low IC50 values for these drugs of approximately 3×10−4 µM warrant discussion . The low IC50 value for ddC is in agreement with findings that this drug is not readily metabolized in the cell to its active form [16] , [47] , implying that the concentrations of the activated drug in the cell may be quite small . There is evidence , however , that d4T and ddI are activated to a significant degree as the concentration of their triphosphorylated forms in patient peripheral blood mononuclear cells are above the predicted IC50 values by approximately 100-fold [43] , [44] ( Table 5 , note that the experimentally measured value is the activated drug concentration in the cytoplasm , not in the mitochondria ) . Given that d4T and ddI are still recommended drugs for HAART [1] they are obviously tolerable to a large number of patients who do not experience the serious side-effects of lactic acidosis and neuropathy . One plausible explanation for this tolerance in the face of the striking affinity of these drugs for pol-γ is that there exists a significant barrier to di-deoxy drug entry into the mitochondrion or drug activation within the mitochondrion allowing activated di-deoxy drug mitochondrial concentrations to remain low in the majority of patients treated with these drugs . We know of no reports of measured levels of the triphosphate form of these di-deoxy drugs within mitochondria . The experimental data indicates that AZT interacts poorly with pol-γ as shown by the high Km and low kcat values ( Table 2 ) for this drug . An explanation for the slow rate of incorporation of AZT was recently published [27] . AZT demonstrates unusually slow pyrophosphate release upon incorporation by pol-γ rendering polymerization readily reversible even upon binding to the template∶primer molecule . In the cases of natural nucleotides this subreaction is fast enough to be considered negligible indicating that pre-steady state kpol values are a good approximation for kcat . Yet , in the case of AZT , this slow pyrophosphate release rate has a significant effect on kcat so that a measurement of kpol during steady-state conditions is more appropriate for estimating kcat . The kpol and Kd determined in the recent Hanes and Johnson study [27] with steady-state conditions , that theoretically take the slow pyrophosphate release rate in to account , indicate a kcat 100-fold lower than that determined from the kpol calculated under pre-steady state conditions [25] . We carried out the simulation for both sets of parameters separately and in both cases AZT shows a poor probability of mtDNA strand termination ( Figure 2 ) . The IC50 values generated by this study show that AZT should not be toxic through mtDNA strand termination as it has a higher IC50 value than 3TC ( − ) and TDF , neither of which are associated with mitochondrial toxicity . The low probability of strand termination by AZT is supported by the fact that although AZT has consistently been associated with positive markers for mitochondrial toxicity , substantial evidence exists that the extent of AZT-induced mitochondrial toxicity is disproportional to the amount of mtDNA depletion it causes . The analogs ddC , d4T and ddI ( activated to ddA ) cause significantly more mtDNA depletion and decreased protein subunit expression of various electron transfer chain proteins with essential subunits encoded in the mtDNA , as would be expected from their increased interaction with pol-γ compared to other analogs ( Figure 2 ) . Yet AZT still manages to demonstrate a cytotoxicity that is equal to or greater than ddA , ddC , and d4T at comparable concentrations in various studies . In human liver and cardiac muscle cells incubation with AZT lead to cytotoxicity and increased lactate levels with no sign of mtDNA depletion [18] , [56] , [57] . Similar results are seen in blood cells and adipose cells [19] , [58] , [59] . Szabados et al . [60] showed significant toxic effects on cardiac muscle cells including increased ROS , abnormal mitochondrial structure , and decreased ATP/ADP ratio after two weeks of exposure of cells in medium with no effects on mtDNA levels . In fact , AZT is actually associated with slight increase in mtDNA levels in cell culture [61] , [62] , PBMCs [9] , and liver tissue samples [11] . Our model , however , does not address subreactions that influence pol-γ binding of the analog , meaning our results cannot disprove the possibility that AZT toxicity is due to deactivating pol-γ either through irreversible binding or induction of a conformational change in the enzyme . However , the high Kd determined by both Hanes and Johnson and Johnson et al . [25] , [27] , along with the cited studies showing toxicity independent of mtDNA depletion , make this an improbable mode of toxicity . It is our conclusion that based on the measured kinetic coefficients of AZT with pol-γ that AZT toxicity is not dependent upon mtDNA strand termination . Indeed , various pol-γ independent hypotheses have been proposed for AZT mitochondrial toxicity . These include inhibition of the enzymes of the mitochondrial salvage pathway causing nucleotide pool imbalances [63] , binding to ADP-ATP translocator [64] , and direct inhibition of components of the electron transport chain [65] . Tenofovir is associated with renal dysfunction without significant mtDNA depletion [66] , [67] . In a retrospective study of HIV positive patients taking TDF and those not taking TDF , no significant differences in mtDNA levels of kidney biopsies were observed [68] . Similarly , in human renal proximal tubule cells [69] , TDF was not associated with cytotoxicity , mtDNA depletion , or COII mRNA depletion . In our simulations mitochondrial TDF triphosphate IC50 values were in the range 0 . 2 to 1 . 2 µM , depending on the natural dNTP levels . Since these concentrations are not unusually high , our conclusion is that Tenofovir might be able to cause some moderate mtDNA depletion , depending on how well the activated drug is concentrated within the mitochondrion . A number of hypothesis with supporting evidence have been proposed for NRTI toxicity experienced during HAART . Possible pol-γ mediated pathways include the direct inhibition of pol-γ by NRTI-triphosphate without incorporation of the analog; chain termination by incorporation of NRTI triphosphate into mtDNA; and incorporation without chain termination of the analog-triphosphate allowing it to remain as a point mutation in mtDNA . Our model only addresses the case of chain termination . There is not enough data on the subreactions that comprise analog binding to pol-γ for this model to explore the possibility that some analogs cause toxicity through inhibition of the pol-γ enzyme directly , either by irreversible binding or induction of conformational change , as opposed to strand termination . The specificity constant , kcat/Km , [25] is commonly used as an approximate indicator of mitochondrial toxicity through strand termination of mtDNA . Before this model , this has been a bit of a leap as the specificity constant does not take genome length , exonuclease activity , nor dNTP concentration into account , and no direct predictions or measurements of strand termination probabilities have previously been given . We fill this gap in our understanding by providing a model that includes all of these factors and that predicts strand termination probabilities . The consistence between our simulation model results and the qualitative ordered list of NRTI drug toxicity based on the specificity constant is a validation of the model results . However , the simulation model goes far beyond the specificity constant by predicting IC50 values and quantitative dose-response curves ( Figure 2 ) for these drugs . Furthermore , the specific definition of strand termination used in this model raises the hypothesis that dissociation of polymerase-γ after an NRTI is incorporated into the mtDNA strand is a critical step in strand termination . In this particular model we chose to define strand termination as the dissociation of polymerase-γ after the incorporation of an NRTI , under the assumption that re-association of the polymerase after the NRTI could not occur . Of course , it is possible that in-vivo there may be other currently unknown factors which may alter the polymerase-γ dissociation kinetics ( or any other kinetics for that matter ) from the measured values . If our assumption that pol-γ re-association is blocked after NRTI incorporation was changed , and re-association of the polymerase was to be allowed , then more exonuclease events of the NRTIs would occur . However , it is not clear to us then what the definition of “strand termination” would be since the exonuclease activity would eventually remove all incorporated NRTIs given enough time . Johnson et al [25] used that assumption , where all NRTIs incorporated into the mtDNA were eventually removed by exonuclease activity , to define a toxicity index based on a calculation of the amount of additional time required for these NRTI exonuclease events . Based on this definition of a toxicity index , Johnson et al [25] also defined an ordered list of NRTI drug toxicity which was similar to our list and similar to the lists based on the specificity constants . An important use of any computational model is to raise questions for further experimental study . This simulation raises the following questions . Is strand termination defined by the dissociation of the polymerase after insertion of an activated NRTI ? If not , what is the proper definition of strand termination ? NRTI toxicity appears perplexingly specific to cell type [1] and the mechanism for this tissue specificity is currently unclear . As natural nucleotide concentrations within the mitochondrion can differ greatly across cell types , we sought to observe how incorporation of NRTIs may differ in the presence of varying mitochondrial dNTP levels , which we broke down into three sample categories; high dNTP levels , medium dNTP levels and low dNTP levels ( Table 3 ) . Although the IC50 values for strand termination measured in this simulation did depend on the concentrations of the natural nucleotide triphosphates , the relative ordering of the nucleoside analogs by IC50 was the same for all three dNTP conditions ( Table 4 ) . The simulations made with low dNTP concentrations , representing post-mitotic tissues , did have lower IC50 values , consistent with a greater sensitivity of these tissues to damage by nucleoside analog drugs . However , these results would not explain why some tissues are susceptible to toxicity from a particular analog . We have limited this simulation model to the activity of the mitochondrial DNA polymerase acting on the tri-phosphate form of the four natural deoxyribonucleosides and the tri-phosphate form of the drugs . Since the tissue dependence of the toxicity of the drugs was not reproduced in this model , this implies that the source of this tissue dependence lies outside the bounds of this particular model . This includes the possible interference of the various phosphate states of the drugs with the metabolism within the mitochondria that produces the natural deoxyribonucleoside tri-phosphates , potentially altering the relative levels of the four natural dNTPs . Although mitochondrial toxicity from NRTIs is common , the more severe forms of this toxicity are certainly not universal . Current research is revealing that the gene for polymerase-γ is the site of a large number of mutations and polymorphisms that alter its enzyme kinetics and function [70]–[73] . The natural variability in this crucial gene may be an important source of the individual variation in the susceptibility of patients to this toxicity , and perhaps to the phenotypic variation which occurs . Although the interaction of nucleoside analogs with polymerase-γ has been recognized for almost 15 years now [6] , we still know surprisingly little about the levels of activated drugs within mitochondria [63] or about the transport mechanism by which these drugs enter the mitochondrion [74] , [75] . | While HIV/AIDS therapy is very successful at controlling HIV infection , the therapy must continue for the remainder of the patient's life . Approximately one-fourth of these patients suffer from serious drug toxicity problems . It is generally believed that the toxicity of these drugs is caused by damage to mitochondria , the “power plants” of every cell . But we do not know exactly how this damage occurs . The most common explanation is that these drugs damage mitochondria in the same way that they control the virus , by interfering with DNA replication . We tested that idea by analyzing data for the interaction of several AIDS drugs with the mitochondrial DNA polymerase , the protein responsible for copying mitochondrial DNA . By using a detailed simulation of the mitochondrial DNA polymerase , we show that some of these drugs do interact well enough with the mitochondrial DNA polymerase to lead to toxic effects . However , many of these drugs , including the commonly used drug AZT , had very little toxic effect in this simulation although AZT often causes toxicity in patients . This indicates that the toxicity of AZT occurs through some other process and not through the direct interruption of mitochondrial DNA replication . | [
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] | 2009 | An Analysis of Enzyme Kinetics Data for Mitochondrial DNA Strand Termination by Nucleoside Reverse Transcription Inhibitors |
Bacteria and archaea are characterized by an amazing metabolic diversity , which allows them to persist in diverse and often extreme habitats . Apart from oxygenic photosynthesis and oxidative phosphorylation , well-studied processes from chloroplasts and mitochondria of plants and animals , prokaryotes utilize various chemo- or lithotrophic modes , such as anoxygenic photosynthesis , iron oxidation and reduction , sulfate reduction , and methanogenesis . Most bioenergetic pathways have a similar general structure , with an electron transport chain composed of protein complexes acting as electron donors and acceptors , as well as a central cytochrome complex , mobile electron carriers , and an ATP synthase . While each pathway has been studied in considerable detail in isolation , not much is known about their relative evolutionary relationships . Wanting to address how this metabolic diversity evolved , we mapped the distribution of nine bioenergetic modes on a phylogenetic tree based on 16S rRNA sequences from 272 species representing the full diversity of prokaryotic lineages . This highlights the patchy distribution of many pathways across different lineages , and suggests either up to 26 independent origins or 17 horizontal gene transfer events . Next , we used comparative genomics and phylogenetic analysis of all subunits of the F0F1 ATP synthase , common to most bacterial lineages regardless of their bioenergetic mode . Our results indicate an ancient origin of this protein complex , and no clustering based on bioenergetic mode , which suggests that no special modifications are needed for the ATP synthase to work with different electron transport chains . Moreover , examination of the ATP synthase genetic locus indicates various gene rearrangements in the different bacterial lineages , ancient duplications of atpI and of the beta subunit of the F0 subcomplex , as well as more recent stochastic lineage-specific and species-specific duplications of all subunits . We discuss the implications of the overall pattern of conservation and flexibility of the F0F1 ATP synthase genetic locus .
Bacteria and archaea use diverse bioenergetic electron transport chains to generate ATP . Apart from photosynthesis and aerobic respiration , many other bacterial and archaeal bioenergetic pathways have been characterized in considerable biochemical detail ( e . g . [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] ) . However , the origins of the diversity of bioenergetic pathways , and their evolutionary relationships , have so far received relatively little attention . Did each pathway evolve independently or did they all evolve from a common ancestral metabolic mode ? As in organismal evolution , it is likely that there were some novel innovations and that parts of pre-existing pathways were co-opted to evolve into new pathways . Molecular evolutionary studies of shared proteins amongst prokaryotes , coupled to data from the geological record , indicate that the vast majority of extant bioenergetic pathways evolved within the first billion years from the origin of life on earth [13] , [14] and have since been mostly characterized by stasis [15] . Interestingly , when 16S rRNA phylogenetic analysis is carried out for a variety of prokaryotes , organisms that utilize different bioenergetic pathways don't group into clear monophyletic groups , i . e . closely related organisms can utilize quite distinct bioenergetic strategies [16] , [17] . This may be due to horizontal gene transfer [18] , and highlights the challenge of deciphering the evolution of these pathways . While most previous studies have focused on comparison of the organisms that harbour the bioenergetic machinery , direct comparisons of the proteins that compose the bioenergetic machinery has been more limited . Most bioenergetic pathways use an electron transport chain ( ETC ) to generate a proton gradient across the membrane , and the energy released by the flow of electrons to compensate for this gradient is then used by the ATP synthase to generate ATP . The electron transport chains of disparate pathways have a similar general structure , being composed of protein complexes acting as electron donors and acceptors , with a central cytochrome bc-type complex and mobile electron carriers between them . Three scenarios are envisaged for the early evolution of energetic flexibility in the bacteria and the archaea: ( i ) each bioenergetic pathway evolved independently , ( ii ) all bioenergetic pathways evolved from a “simpler” ancestral metabolism , ( iii ) some new metabolic capabilities evolved by the modification of pre-existing pathways . The third scenario is the most likely , and has been highlighted through detailed analysis of the bioenergetic protein complexes , e . g . for oxygenic and anoxygenic photosynthesis [19] , [20] , [21] . The unprecedented availability of genomic data enables us to address evolutionary questions relating to the events that led to the emergence of this metabolic diversity early in the evolution of life on Earth . Although various studies have looked at the evolution of ATP synthases across the bacteria and the archaea ( e . g . [22] , [23] , [24] ) , these have mostly addressed the relative relationships between the F-V- and A-type ATPases , and no study has looked at organisms spanning the full bioenergetic diversity of bacteria . We chose to examine the F0F1 ATP synthase complex , common to nine bioenergetic modes , and sampled a large variety of species across all major lineages to establish their homology and evolutionary relationships . We first asked whether the evolution of the ATP synthase complexes in these species agrees with the 16S rRNA phylogeny , i . e . whether they cluster according to the type of ETC , or based on taxonomic groups . This enables us to check for horizontal gene transfer events concerning the ATP synthase , as well as for putative specific modifications in the ATP synthase subunits associated with each bioenergetic mode . We also examined the structure of the F0F1 ATP synthase genetic locus , and report a variety of both ancient and recent gene duplications and rearrangements .
In this study , we focused on nine pathways most of which have been well characterized at the biochemical level , and for which enough sequence information is available to enable assessment of the diversity within each group as well as inter-group relationships: Species , whose complete genomes are available , were chosen to represent all major lineages of bacteria and archaea , and all the above bioenergetic modes . Information about the metabolism ( bioenergetic mode ) of each species was collected from the species description at the NCBI BioProject database , as well as from the Integrated Microbial Genomes database . Full details of the 198 bacteria and 74 archaea species selected are given in Table S1 , while the number of species from each lineage , and each bioenergetic mode is shown in Table 1 . As has been observed in previous analyses [16] , [17] , [18] , certain bioenergetic modes can be shared by quite distinct taxonomic groups . Indeed , as demonstrated by 16S rRNA phylogenetic analysis of the organisms examined here ( Figure 1 ) , species which utilize the same bioenergetic modes do not always segregate in monophyletic groups . Inferring the origin of each bioenergetic mode is therefore confounded by their patchy distribution among the prokaryotes . Oxygenic photosynthesis is the only bioenergetic mode which is unique to a lineage ( the cyanobacteria ) . Oxidative phosphorylation ( respiration ) is shared by the greatest variety of lineages , and as such , can be considered as an ancient mode of generating energy in both the bacteria and the archaea , while methanogenesis is found in seven lineages within the euryarchaea , and as such can be considered ancient to this group However , anoxygenic photosynthesis , sulfur reduction , sulfate reduction , sulfur oxidation , iron reduction and iron oxidation are found in more than one lineage , which are not closely related . The presence of the same pathway in these distinct lineages , can come about by one of three processes: either ( a ) all bioenergetic modes were found in the common ancestor of these lineages , and some have been lost from some lineages , or ( b ) bioenergetic modes were acquired by distinct lineages by horizontal gene transfer ( HGT ) , or ( c ) some electron transport chains originated multiple times independently in different lineages . The most parsimonious explanation is probably HGT , since , based on the phylogenetic tree of Figure 1 , and as summarized at the bottom of Table 1 , the distribution of bioenergetic pathways can be explained by up to 26 independent origins or , alternatively , 17 horizontal gene transfer events . Four HGT events can be inferred for iron oxidation , three HGT events can be inferred for anoxygenic photosynthesis , sulfate reduction , sulfur oxidation and iron reduction , and one HGT event can explain the distribution of sulfur reduction ( Table 1 ) . These inferences are based on minimal assumptions of lineage groupings ( e . g . for the alpha– beta- gamma- and delta-proteobacteria ) as the branching order of prokaryotic lineages is still largely unresolved [25] , [26] , [27] , [28] , [29]; the lineage-groupings seen in a more recent and better-resolved bacterial phylogeny [30] still do not change these numbers . Moreover , while iron reduction , and anoxygenic photosynthesis are specific to the bacteria , the other modes ( sulfate reduction , sulfur reduction , sulfur oxidation , and iron oxidation ) are found in both bacteria and archaea . Notably , certain lineages seem more prone to bioenergetic diversity than others . For example , five bioenergetic modes are seen within the gamma-proteobacteria and the firmicutes; four bioenergetic modes are found within the alpha-proteobacteria , three bioenergetic modes are found within the beta- the delta- and the epsilon-proteobacteria , the aquificae , and the sulfolobales; two bioenergetic modes are found within the deinococci , the acidobacteria , the actinobacteria , the thermoproteales , the desulfurococcales , and the thermoplasmata , while sulfate reduction and iron oxidation are both seen in the archaeoglobi . However , this may be influenced by how many complete genomes are available per lineage , and how well this represents the true diversity in each lineage [31] . This picture may thus change in the future , as more diverse organisms are sequenced . As the ATP synthase complex is common to all the electron transport chains of the studied bioenergetic modes , we chose to study the evolution of this complex in the different lineages . To examine whether the ATP synthase complex which is associated with the different bioenergetic modes was also subject to HGT , we performed phylogenetic analysis of all the protein subunits of the F0F1 ATP synthase , as this is shared by most of the bacterial lineages . However , archaea and certain bacterial species/lineages lack ATPF0F1 altogether , and have ATPV instead: Clostridium tetani and Thermoanaerobacter sp . X513 ( clostridia ) , Chlamydia trachomatis and Chlamydophila pneumoniae ( chlamydiae ) , Deinococcus radiodurans , Thermus scotoductus and Thermus thermophilus ( deinococci ) , Fibrobacter succinogenes ( fibrobacteres ) , Borrelia burgdorferi , Spirochaeta thermophila and Treponema pallidum ( spirochaetaceae ) , Aminobacterium colombiense and Thermanaerovibrio acidaminovorans ( synergistetes ) , Candidatus Phytoplasma mali ( mollicutes ) . As most subunits of the V-type and the F-type ATPases are not homologous [24] , we chose to focus solely on the F0F1 ATP synthase . Gene sequences were identified using KEGG orthology annotations , both by searching the KEGG orthology tables , and by manual searches in IMG ( for the species not included in KEGG ) . The bacterial F0F1 ATP synthase complex is composed of the F0 subcomplex , which is embedded in the membrane , and the F1 subcomplex which protrudes on the side of the membrane towards which the protons exit following the proton gradient . The F0 subcomplex is composed of ATPF0A ( K02108 ) , ATPF0B ( K02109 ) , and ATPF0C ( K02110 ) , while the F1 subcomplex is composed of ATPF1A ( K02111 ) , ATPF1B ( K02112 ) , ATPF1D ( K02113 ) , ATPF1E ( K02114 ) , and ATPF1G ( K02115 ) . The genes encoding these subunits are usually arranged consecutively in a conserved genetic locus , which also includes another subunit , ATPI ( K02116 ) and sometimes atpR . K02116 is interchangeably associated with two pfam domains , which makes orthologous gene assignments problematic: for consistency in the text below , ATPI sequences containing the pfam03899-ATP_synthI domain will be called “sI” , and ATPI sequences containing the pfam09527-ATPase_gene1 will be called “I”: atpR sequences containing the pfam12966-atpR domain will be called “R” . For each subunit , the corresponding protein sequences were downloaded from KEGG for all species and , after multiple alignment , phylogenetic analysis was performed using Bayesian and maximum likelihood methods . The phylogenetic analysis for ATPF0A and ATPF1A are shown in Figures 2 and 3 , respectively , while the rest of the trees are in Figures S1 , S2 , S3 , S4 , S5 , S6 , S7 . Overall , for all subunits , species segregate based on taxonomic groups with good bootstrap support , as in the 16S tree , and not based on bioenergetic mode . If the current patchy distribution of bioenergetic modes ( Figure 1 ) is due to HGT , we might expect the ATP synthase sequences from different organisms which utilize the same pathway to group together ( as we used different colours for the different bioenergetic modes for species names on the tree , we would essentially expect to see organisms grouping based on colour ) . This is not what we observe , suggesting that there is no evidence of HGT of the ATP synthase despite the use of different bioenergetic modes between closely related species . Nevertheless , in certain species , a duplication of the whole ATPF0F1 locus is seen ( Table 2 ) , and the majority of those duplications correspond to the so-called N-ATPase , which appears to have been acquired via horizontal gene transfer , as has been reported previously [32] . The N-ATPase genetic locus is characterized by the absence of the ATPF1D gene and the presence of the atpR gene ( Figure 4 ) as well as a long ( >100aa ) C-terminal extension in ATPF0B ( Dataset S1 ) . For the set of organisms studied here , the N-ATPase is found in certain species of planctomycetes ( Rhodopirellula baltica ) , verrucomicrobia ( Methylacidiphilum infernorum ) , chlorobi ( Chlorobaculum parvum , Chlorobaculum tepidum ( partial ) , Pelodictyon luteolum , Prosthecochloris aestuarii ) , cyanobacteria ( Acaryochloris marina , Cyanothece sp . ATCC 51142 , Synechococcus sp . PCC 7002 ) , alpha-proteobacteria ( Azospirillum sp . B510 , Dinoroseobacter shibae , Rhodopseudomonas palustris , Rhodospirillum centenum/Rhodocista centenaria ) , beta-proteobacteria ( Rhodoferax ferrireducens ) , gamma-proteobacteria ( Nitrosococcus halophilus - double N-ATPase , one locus split , both missing atpR ) , delta-proteobacteria ( Desulfobacterium autotrophicum , Desulfobulbus propionicus , Desulfomicrobium baculatum , Desulfovibrio salexigens , Desulfuromonas acetoxidans , Pelobacter carbinolicus ) and methanomicrobia ( Methanosarcina acetivorans , Methanosarcina barkeri ) . The sequences corresponding to the N-ATPase form a highly supported monophyletic group; the trees ( apart from ATPF1D ) were therefore rooted at this N-ATPase clade . Phylogenetic reconstruction of all subunits confidently separates the major bacterial taxonomic lineages , but the trees only give limited support for the branching order ( Figures 2–3 , S1 , S2 , S3 , S4 , S5 , S6 ) . The differences between trees , with respect to the resolution of the branching order of different lineages , are probably due to the sequence length of the proteins analyzed; longer subunits retain more information and tend to give better-resolved phylogenetic trees , than shorter sequences [33] . The most clear-cut grouping is that of the beta- and gamma-proteobacteria , which is seen in all trees , and has significant bootstrap support in all but the ATPF1D and ATPF1E trees . Significant bootstrap support for the beta- and gamma-proteobacteria grouping is also seen in the 16S phylogenetic analysis ( Figure 1 ) , which also suggests groupings of the chlorobi and the bacteroidetes , and of the fusobacteria and tenericutes . The phylogenetic link between the chlorobi and the bacteroidetes is also seen in the trees for ATPF0A ( Figure 2 ) , ATPF0C ( Figure S2 ) , ATPF1A ( Figure 3 ) and ATFP1B ( Figure S3 ) . In the ATPF0C analysis this group also includes the planctomycetes as well as the spirochaete Leptospira interrogans and the gemmatimonadete Gemmatimonas aurantiaca ( Leptospira interrogans also groups with the planctomycetes in the ATPF1A phylogeny ) . The ATPF0A phylogeny also has reasonable support for grouping the chlorobi , bacteroidetes and planctomycetes , together with the actinobacteria and the alpha-proteobacteria ( this group also includes the spirochaete Leptospira interrogans and the gemmatimonadete Gemmatimonas aurantiaca , as well as Candidatus Nitrospira defluvii which groups with the alpha-proteobacteria; Candidatus Nitrospira defluvii also groups with the alpha-proteobacteria in the ATPF0C analysis ) . A group containing the actinobacteria and the planctomycetes ( as well as the spirochaete Leptospira interrogans and the gemmatimonadete Gemmatimonas aurantiaca ) is supported by the ATPF1G tree . Strong support is provided by the ATPF0A phylogeny for a group containing the verrucomicrobia and chloroflexi; the phylogenetic reconstruction of ATPF1G ( Figure S6 ) also has reasonable support for a group containing the verrucomicrobia , chloroflexi , and the beta-gamma-proteobacteria . Finally , reasonable support is provided in the ATPF0A tree for the grouping of dictyoglomi and cyanobacteria , and for a group containing the fusobacteria , firmicutes , tenericutes , thermotogae , and beta-gamma-proteobacteria . In the ATPF0C analysis , the dictyglomi cluster with the N-ATPase with good statistical support ( Figure S2 ) . Although the phylogenetic analysis is based on trimmed sequences , i . e . only the unambiguous homologous regions were retained for phylogenetic analysis by manually inspecting and masking/trimming the sequences , some notable insertions/deletions were noted in the multiple alignments . For example , the chlorobi and the bacteroidetes are both missing the C-terminal half of ATPF1E , and share an internal 10–15aa insertion in ATPF1A . A different internal 10–15aa insertion in ATPF1A is shared between the beta- and gamma-proteobacteria . Actinobacteria have a ∼75aa insertion near the N-terminus of ATPF1D , and cyanobacteria have an internal 20aa insertion in ATPF1G . The N-ATPase ATPF1A in Azospirillum sp . B510 has a long ( ∼100aa ) N-terminal extension plus a ∼150aa insertion near the N-terminus , while the N-ATPase ATPF1G in Cyanothece sp . ATCC 51142 has a 50aa N-terminal extension ( Dataset S1 ) . The elucidation of the role of these signature sequences would require further study based on experimental or structural analysis . Given the ancient origin of the ATP synthase complex , the syntenic genetic location of the genes was checked in all lineages , to identify common gene order transversions , gene duplications , and possible horizontal gene transfer events ( Figure 4 ) . The N-ATPase , which has been suggested to be an early-diverging branch of membrane ATPases [32] has the following gene order: IB-IE-I-R-0A-0C-0B-IA-IG . Bacteroides fragilis also has a similar gene locus organization , except that it lacks atpR . The subunits are arranged in consecutive order ( i . e . the locus is not split ) in the dictyoglomi , planctomycetes , firmicutes , thermotogae , chloroflexi , actinobacteria , tenericutes , verrucomicrobia , fusobacteria and the beta- and gamma-proteobacteria . Except for the proteobacteria and the verrucomicrobia , these lineages have been suggested to be near the base of the bacterial clade , either based on phylogenetic analysis [25] , [31] or based on the analysis of signature sequences [26] , [30] . By inference , the most likely ancient gene order for the ATPF0F1 locus is: I-sI-0A-0C-0B-ID-IA-IG-IB-IE , although some lineages lack I or sI or both ( e . g . fusobacteria , chloroflexi , verrucomicrobia ) . The locus has been split ( indicated by semi-colons in Figure 4 ) at the junction between IG and IB in the chlorobi , bacteroidetes , cyanobacteria , aquificae and Beggiatoa , with further splits between IB and IE in aquificae and Beggiatoa . A further split is seen between ID and IA in the chlorobi and between IA and IG in aquificae and Beggiatoa . A split between 0B and ID is seen in nitrospirae and the alpha-proteobacteria , while a split between 0C and 0B is seen in aquificae , acidobacteria , deferribacteres , and delta- and epsilon-proteobacteria . A split between 0A and 0C has occurred in the epsilon-proteobacteria . Finally a split between I and OA is seen in aquificae . Therefore , although there are three “blocks” of genes which are usually conserved , in terms of gene order ( one containing I ( -sI ) -0A-0C ( -0B′ ) -0B , another containing ID-IA-IG , and another with IB-IE ) , in principle , gene-order transversion can and has happened all along the genetic locus . The phylogenetic analysis and the gene locus information were used to examine the most likely origin of duplicated genes , i . e . whether they arose as gene duplications within a particular species , or via horizontal gene transfer ( Table 2 ) . In the delta-proteobacterium Pelobacter carbinolicus , there are two duplications of the whole ATPF0F1 locus , one corresponds to the N-ATPase , and the other is a species-specific duplication ( Figures 2–3 , S1 , S2 , S3 , S4 , S5 , S6 ) . A duplicated ATPF0F1 full locus , which does not correspond to the N-ATPase was also found in the gamma-proteobacterium Photobacterium profundum; this appears as a species-specific duplication in the ATPF1A , ATPF1B and ATPF1E trees ( Figures 3 , S3 , S5 ) , while in the rest of the trees , one copy groups with Vibrio cholerae and the other elsewhere within the gamma clade ( Figure 2 , S1 , S2 , S4 , S6 ) . This possibly hints at HGT from another closely related species , but the placement within the gamma clade is not consistent and could thus simply be due to high sequence divergence of the second copy in P . profundum for some of the subunits . There are also certain in-locus gene duplications , where the duplicated genes are still found adjacent to each other on the genetic locus , as well as ectopic duplications ( outside the main ATPF0F1 locus ) probably resulting from recombinations/transversions ( summarized in Table 2 ) . The most commonly in-locus duplicated genes are ATPF0B and ATPI , discussed in more detail in the next section . The delta-proteobacterium Desulfococcus oleovorans has a fully duplicated ectopic ATPF0 complement; in the ATPF0B phylogeny ( Figure S1 ) both copies group within the delta-proteobacteria suggesting that this could be a species-specific duplication where one copy has diversified . However , the duplicated ATPF0A ( Figure 2 ) and ATPF0C ( Figure S2 ) subunits group with the thermotogae with good bootstrap support , hinting at a possible HGT event; assuming a common origin for all three subunits in the duplicated locus , this suggestion of HGT from Thermotogae requires further study ( phylogenetic analysis of only the deltaproteobacteria and thermotogae sequences did not resolve this issue as it gives the same results as above for the duplicated subunits , data not shown ) . The actinobacterium Saccharopolyspora erythraea has a duplicated ectopic ATPF0A , which looks like a species-specific duplication ( Figure 2 ) . The zeta-proteobacterium Mariprofundus ferrooxydans has a duplicated ectopic ATPF0B; it is unclear if this is the result of HGT , as the sequence groups with planctomycetes , but not with high bootstrap support ( Figure S1 ) . The firmicute Alkaliphilus metalliredigens has a species-specific in-locus duplication of ATPF0C ( Figure S2 ) which is characterized by a long ( ∼100aa ) N-terminal extension ( Dataset S1 ) . Ectopic duplications of ATPF1A and ATPF1B are seen in Mycoplasma agalactiae and Ureaplasma parvum ( tenericutes ) as has been reported recently [34]; this duplication likely happened before the split between the two species ( Figure 3 , S3 ) ; one of the ATPF1A copies in U . parvum has a long ( ∼250aa ) C-terminal extension ( Dataset S1 ) . Ureaplasma parvum also has duplicated ATPF1D in-locus; the evolutionary history of this duplication cannot be clearly inferred from the phylogenetic analysis , although it appears to be species-specific in the PhyML and RaxML trees , but is not statistically supported by high bootstrap values . ATPF1E is duplicated ectopically in Mariprofundus ferrooxydans ( zeta-proteobacteria ) , Thiobacillus denitrificans ( beta-proteobacteria ) , and the gamma-proteobacteria Acidithiobacillus caldus , Acidithiobacillus ferrivorans , and Acidithiobacillus ferrooxidans ( two extra copies ) , as well as in-locus in the delta-proteobacteria Desulfovibrio magneticus and Desulfovibrio sp . FW1012B . The duplication in M . ferrooxydans is species-specific , while the other duplications are lineage-specific , i . e . the duplication either occurred before the split from other closely-related species or represents HGT from other closely-related species ( Figure S5 ) : the duplication in T . denitrificans may represent HGT from other gamma-proteobacteria; a duplication occurred before the split between the gamma-proteobacteria Acidithiobacillus ferrooxidans , Acidithiobacillus ferrivorans and Acidithiobacillus caldus , with a further species-specific duplication in Acidithiobacillus ferrooxidans; another duplication occurred before the split between Desulfovibrio magneticus and Desulfovibrio sp . FW1012B in the delta-proteobacteria . ATPF1G is ectopically duplicated in Aquifex aeolicus ( aquificae; one copy has a 80aa C-terminal extension ) and Acidithiobacillus ferrooxidans ( gamma-proteobacteria; one copy is missing the N-terminal half ) ; the duplication in Aquifex aeolicus represents a very divergent sequence which groups with the dictyoglomi in the MrBayes and PhyML trees ( Figure S6 ) ; the duplication in A . ferrooxidans might be a pseudogene as it is much smaller in size - in the tree it clusters with the N-ATPase genes . ATPF0B is duplicated in-locus in acidobacteria , aquificae , cyanobacteria , deferribacteres , and alpha- delta- and epsilon-proteobacteria . This raises the question of whether ATPF0B has been duplicated independently in separate lineages , or whether the duplication has been passed on , either by direct descent , or by horizontal gene transfer . In the phylogenetic analysis ( Figure S1 ) the ATPF0B′ group in the alpha-proteobacteria appears as a sister group to the alpha-proteobacterial ATPF0B , but with only moderate statistical support ( red asterisk: 0 . 7 posterior probability in MrBayes , 50% , and 46% bootstrap support in PhyML and RaxML , respectively ) . The other ATPF0B's group together ( blue asterisk ) , with good statistical support by MrBayes ( posterior probability: 1 ) but with low support in PhyML and RaxML ( 24% and 26% bootstrap support , respectively ) . The grouping of the alpha-proteobacterial ATPF0B and ATPF0B′ may indicate that this duplication happened more recently than the ATPF0B duplications in the other lineages . However , given the low bootstrap support it remains unclear from the tree whether the ATPF0B/0B′ duplication happened independently in the different lineages where it is observed , or whether it happened only once in the common ancestor of all the lineages where it is observed ( and presumably lost in other lineages , e . g . the beta-gamma-proteobacteria ) ; however , the latter scenario is more plausible based on parsimony considerations . Notable absences are the ATPF1D in N-ATPase , as well as in dictyoglomi ( Dictyoglomus thermophilum and Dictyoglomus turgidum ) , ATPF0C in Wolinella succinogenes ( epsilon-proteobacteria ) , ATPF1B and ATPF1E in the cyanobacterium Microcoleus chthonoplastes , and ATPI missing from many species ( e . g . chloroflexales , verrucomicrobia ) . At least some of these absences may of course be due to incomplete annotation or extreme sequence divergence . ATPI has been the least studied subunit of the F0F1 ATP synthase complex . As mentioned above , ATPI ( K02116 ) is interchangeably associated with two pfam domains ( pfam03899-ATP_synthI and pfam09527-ATPase_gene1 ) , which makes orthologous gene assignments problematic . The bacterial uncI gene encoding a small transmembrane protein which includes the pfam03899 domain , has been demonstrated to have a chaperone role in assisting the assembly of the c-ring of the F0 subcomplex [35] , [36] . By analogy , it has been suggested that the atpR gene of the N-ATPase ( characterized by the presence of the pfam12966 domain ) plays a similar role , in the absence of uncI [32] . Given this suggestion , and the grouping of the atpQ genes ( which include the pfam09527 domain ) into the same KEGG cluster as uncI , along with the fact that all three encode proteins of similar size and , based on their position in the genetic cluster , could be the result of gene duplications , we decided to analyze their evolutionary relationship in more detail . The phylogenetic reconstruction of ATPI ( K02116 ) protein sequences , including “sI” proteins containing the pfam03899-ATP_synthI domain , “I” proteins containing the pfam09527-ATPase_gene1 , and “R” proteins containing the pfam12966-atpR domain ( found in the N-ATPase locus ) is shown in Figure S7 . Overall the three types of proteins look similar in the alignment , although atpR stands out , as do the cyanobacterial sI sequences; the delta-proteobacterium Desulfovibrio piger has a prominent 50aa C-terminal extension ( Dataset S1 ) . Only the PhyML tree is shown , even though the bootstrap support for most branches is not significant . Phylogenetic analysis with the same set of sequences using MrBayes failed to converge on a tree , and the RaxML tree had very bad resolution . The low resolution and low bootstrap support are probably due to the short sequence length and high divergence of these sequences . Nevertheless , the tree does separate a cluster of the “I” proteins ( which contain the pfam09527-ATPase_gene1 domain ) to the left of the dotted grey line , and another cluster containing the “sI” proteins ( pfam03899-ATP_synthI domain ) and “R” proteins ( pfam12966-atpR domain ) to the right of the grey dotted line . Based on the gene locus organization and the protein sizes , the genes encoding the “sI” and “R” proteins look like duplications of the “I” gene , and the tree indeed supports this hypothesis . However , due to the low resolution of the phylogenetic analysis , the issue of the origin and functional homology of atpI , sI , and R would ultimately need to be resolved with structural and functional analysis .
Phylogenetic analysis of 16S rRNA for 272 species chosen to represent all the major prokaryotic lineages and bioenergetic modes indicated that , overall , there is no monophyly of bioenergetic modes ( one notable exception is oxygenic photosynthesis which is confined to the cyanobacteria ) . This analysis also highlighted lineages which include species with vastly different modes of generating energy ( e . g . proteobacteria , firmicutes ) . The scattered distribution of certain bioenergetic modes , such as anoxygenic photosynthesis or iron oxidation , indicates rampant HGT of at least some bioenergetic modes , in agreement with previous analyses [16] , [17] , [18] . All these bioenergetic pathways also include the ATP synthase complex , but phylogenetic analysis of all the ATPF0F1 synthase subunits , common to almost all bacterial lineages , largely agree with the 16s rRNA tree . This indicates that , if different bioenergetic pathways dispersed into different lineages by horizontal gene transfer , this did not involve the ATP synthase complex . Presumably , each species used its pre-existing ATP synthase complex and adapted it to utilize the proton gradient generated by vastly different ETCs . Recent data has shown that large-scale HGT from bacteria transformed the bioenergetic capabilities of the Haloarchaea [37] and yet Haloarchaea retain ATPV , whereas their laterally acquired bioenergetics modes utilize ATPF in the bacteria . This is in agreement with our results , and again indicates flexibility in combining a species' pre-existing ATP synthase with a newly acquired electron transport chain . Given the widespread effect of HGT on prokaryotic evolution [38] , [39] , [40] , it may be that the cost of incorporating a laterally transferred ATP synthase to replace a pre-existing enzyme is too high to overcome [41] . To our knowledge the question of whether specific modifications are needed for the ATP synthase to function with different bioenergetic modes has not been addressed previously , so this current , updated large-scope study allows us to resolve this issue , and suggests that no apparent such modifications exist , at least at the sequence level . A more thorough structural analysis would be needed to examine if certain structural modifications unite the ATP synthases of organisms using each bioenergetic pathway . HGT has happened however , for a variant form of the ATP synthase , previously named N-ATPase , as it includes residues in the c subunit for translocating Na+ [32] . This is found always in addition to the F0F1 ATP synthase , in certain species from different bacterial lineages , as well as in two Methanosarcina species of the archaea . The N-ATPase subunits always cluster independently of their F0F1 counterparts , and although they often group closest to the dictyoglomi , only the ATPF0C phylogeny has significant bootstrap support for a grouping of the dictyoglomi and N-ATPase; therefore , their exact origin cannot be inferred from the tree , and possibly predates the separation between ATPV and ATPF [32] . The N-ATPase locus is characterized by the absence of the ATPF1D subunit , and the presence of the atpR gene ( also see below ) . Interestingly , the two dictyoglomi species studied here ( the only two for which complete genome information is available ) also lack the ATPF1D subunit , which in combination with the close affinity of the dictyoglomi and the N-ATPase in most of the trees , might suggest that the dictyoglomi are the closest relative to the common ancestor of the N-ATPase . In the gamma-proteobacterium Nitrosococcus halophilus , two copies of the N-ATPase are found ( one locus is split in half , both are missing atpR ) , whereas Chlorobaculum tepidum of the chlorobi only has half the locus; the lack of certain subunits may indicate a non-functional degenerate N-ATPase . It is assumed that the N-ATPase confers a selective advantage in high-salt environments [32] . Given the ancient origin of the F0F1 ATPase , the phylogenetic trees can perhaps give clues as to the evolutionary relationships between different bacterial lineages . The branching order of bacterial lineages remains an issue unresolved through phylogenetic analysis [25] , [27] , [28] , [29] , although other methods have also been proposed based on signature sequences of certain crucial proteins [26] , and a more recent analysis based on feature frequency profiles in whole proteome data has produced a well-resolved tree [30] . Some of the F0F1 ATP synthase subunits are relatively long proteins , and relatively slow evolving due to their interactions with the other subunits , so they may retain some of the evolutionary signal that cannot be retrieved from 16S rRNA sequences . There is consistent support for a grouping of the beta- and gamma-proteobacteria , another of the chlorobi and the bacteroidetes , and some support for this group also including the planctomycetes , the actinobacteria , the alpha-proteobacteria and the spirochaete Leptospira interrogans and the gemmatimonadete Gemmatimonas aurantiaca; Candidatus Nitrospira defluvii groups with the alpha-proteobacteria . Some trees also indicate a subgroup containing the verrucomicrobia and the chloroflexi , and possibly also the beta-gamma-proteobacteria . Finally , reasonable support is provided in the ATPF0A tree for the grouping of dictyoglomi and cyanobacteria , and for a subgroup containing the fusobacteria , tenericutes , firmicutes , thermotogae , and beta-gamma-proteobacteria . The groupings of ( i ) the beta-gamma proteobacteria , ( ii ) the chlorobi and bacteroidetes , and ( iii ) the fusobacteria , tenericutes , firmicutes , and thermotogae , are in agreement with the more recent phylogeny [30] . The order of the genes encoding the F0F1 ATP synthase subunits is relatively well conserved overall in most of the species analyzed , although the locus has been split on multiple occasions , and the genes for ATPF1B and ATPF1E are found either upstream ( in the N-ATPase and in Bacteroides fragilis ) or , most commonly , downstream of all the others . Duplications of each of the F0F1 ATP synthase subunits are observed in several species , either within the genetic locus or in distant parts of the genome . The history of these duplications can be traced by looking at the phylogenetic analysis . The most ancient in-locus duplication is likely that of atpI , with the diversification of the downstream copy into “sI” and “R” , but with multiple losses in various lineages of either one or both copies . Another ancient in-locus duplication is that of ATPF0B , which probably occurred in the common ancestor of the acidobacteria , aquificae , cyanobacteria , deferribacteres , delta- epsilon- and alpha-proteobacteria , ( and presumably lost in other lineages , e . g . the beta-gamma-proteobacteria ) . Most of the other duplications have occurred in isolated species , and appear to be species-specific , with no unassailable evidence of HGT . These duplications raise the question of how certain species deal with gene dosage effects , e . g . to co-ordinate the ATP synthase complex structure . As there is no clear evidence of HGT , apart from the N-ATPase clade , most duplications seem to be the result of stochastic events that have not been bred out; presumably this means that at least some of these duplications , e . g . the ATPF0B/0B′ duplication may confer a selective advantage , although this would need to be confirmed experimentally . A recent study of the ATPV complex showed that such paralogous expansions can lead to increased complexity ( and possibly also specificity ) of a multi-subunit molecular machine [42] . Moreover , ATPF0B functions as a dimer , even in species where only one copy exists in the genome , and the two parts of the dimer interact with different parts of the F1 and the F0 subcomplex [43] , [44] . Thus a gene duplication which allows each gene copy to fine-tune specific interactions may indeed be advantageous . Notably , cyanobacterial ATPF0B/0B′ have been successfully inserted into a null E . coli strain ( which lacks its native single ATPF0B ) and form heterodimers which assemble with the rest of the native ATP synthase E . coli subunits to form a functional enzyme [45] . This again points to a flexibility of the ATP synthase in different species , to accommodate changes and duplications . The loss of ATPF1D from N-ATPase and dictyoglomi , as well as ATPI from many species also raises the question of the essentiality of these subunits for the function of the F0F1 ATP synthase . The absence of certain subunits in isolated species ( ATPF0C from Wolinella succinogenes ( epsilon-proteobacteria ) , ATPF1B and ATPF1E from the cyanobacterium Microcoleus chthonoplastes ) may be due to incomplete annotation or extreme sequence divergence , although if they represent true losses , again this raises questions as to the functionality of the ATP synthase in these species . Overall , this analysis highlights the patchy distribution of bioenergetic modes across prokaryotic lineages , which is most likely the result of HGT . However , there is no evidence of HGT for the ATP synthase to accompany the spread of bioenergetic pathways in different lineages . This means that the ATP synthase cannot be used to reconstruct the origin of the diversity of bioenergetic modes in prokaryotes . It also indicates that there are no apparent specific modifications of the F0F1 ATP synthase in order for it to work with different bioenergetic ETCs . The F0F1 ATP synthase genetic locus is overall well conserved , although as demonstrated by multiple splits and duplications , in principle , the system is robust and flexible , as it can deal with a split between any subunits and/or a duplication of any subunit . The elucidation of the way in which certain species deal with these duplications , splits and losses , and the advantage any of these may confer , now requires further study .
Bacteria and archaea species , whose genomes have been completely sequenced and are available at NCBI , were chosen by parsing the NCBI Genome Project database ( http://www . ncbi . nlm . nih . gov/bioproject ) with keywords relating to the relevant metabolisms ( e . g . “anoxygenic phototroph” ) , and the relevant phyla ( e . g . “chlorobi” ) . For autotrophs and chemolithotrophs , all relevant species were examined , but for heterotrophs , only a sample of species was examined so as to cover the full diversity of bacteria and archaea [31] ( http://tolweb . org/tree/ ) and the full bioenergetic diversity per lineage . For lineages with many sequenced genomes , the tree of [31] was used to pick species so as to cover as much phylogenetic diversity as possible with a limited number of species . The set of species selected , represent 131 clusters , with a genome similarity score ( GSS ) threshold of 0 . 5; of those , 24 are in “clusters” which only have one member , and 63 are the sole representatives from their cluster [46] . Information on the metabolic mode of all species was also cross-checked in the IMG database [47] . Each species name was assigned an 8-character abbreviation for better data handling during the phylogenetic analysis , by keeping the first two letters of the first name and the first three letters of the second name , as well as a 2–3 letter ending , denoting the bioenergetic mode . Details of all the 272 organisms analyzed , and of the species names abbreviations are given in Table S1 . 16S rRNA sequences were downloaded pre-aligned from the RDP database [48] . When more than one sequence was available for each species/strain examined , one of the good-quality >1200 bp sequences was selected at random , unless the type sequence was available , in which case that was selected . Importantly , we used data from the same strain for the 16S analysis and the ATP analysis ( see below ) . As bacterial and archaeal sequences are provided as separate pre-aligned files , the program opal was used to align the two sets [49] . Common gaps were removed after manual examination of the whole set of sequences in McClade . The nucleotide substitution model that best fits the data ( GTR+I+G ) was selected using the program ModelGenerator [50] ( http://bioinf . nuim . ie/modelgenerator/ ) . All other analyses were done at the amino acid level . For the ATP synthase subunits , sequence accession numbers were retrieved using the ortholog tables from the KEGG database: KEGG ortholog tables are based on RefSeq annotations , sequence similarity and best-hit searches , as well as tools for operon-like consistency and completeness of pathway modules and complexes; furthermore they are regularly updated ( http://www . kegg . jp/kegg/ko . html ) . In cases where data was missing from the KEGG database , this was supplemented by data from IMG [47] , manual analysis to find the best reciprocal BLAST hits , as well as synteny considerations , since the gene order of the ATP synthase locus is well-conserved overall . The accession numbers of all sequences analyzed , and the corresponding species names abbreviations , are given in Table S1 . Sequences were downloaded from KEGG in fasta format using a custom perl script . Alignments were created using MUSCLE [51] . Only unambiguous homologous regions were retained for phylogenetic analysis by manually inspecting and masking/trimming the sequences in McClade ( the masked alignment are given in Dataset S1 ) . ProtTest [52] was used to estimate the appropriate model of sequence evolution . Phylogenetic analysis was performed by three separate methods . To obtain the Bayesian tree topology and posterior probability values , the program MrBayes version 3 . 1 . 2 was used [53] . Analyses were run for 1–5 million generations , removing all trees before a plateau established by graphical estimation . All calculations were checked for convergence and had a splits frequency of <0 . 1 . Maximum-likelihood ( ML ) analysis was performed using PhyML [54] and RAxML [55] with 100 bootstrap replicates . Nodes with better than 0 . 95 posterior probability and 80% bootstrap support were considered robust , and nodes with better than 0 . 80 posterior probability and 50% bootstrap support are shown . Tree files were processed in Figtree v1 . 4 and Adobe Illustrator to highlight homologous groups , and colour-code species names based on bioenergetic mode . As the genes encoding the different subunits of the ATP synthase are normally clustered in an operon , the genetic locus of the sequences analyzed was examined in the IMG database [47] . Details of the locus organization in each species are given in Table S1 and the data is summarized per lineage in Figure 4 . | Bacteria and archaea are the most primitive forms of life on Earth , invisible to the naked eye and not extremely varied or impressive in their appearance . Nevertheless , they are characterized by an amazing metabolic diversity , especially in the different processes they use to generate energy in the form of ATP . This allows them to persist in diverse and often extreme habitats . Wanting to address how this metabolic diversity evolved , we mapped the distribution of nine bioenergetic modes across all the major lineages of bacteria and archaea . We find a patchy distribution of the different pathways , which suggests either frequent innovations , or gene transfer between unrelated species . We also examined the F-type ATP synthase , a protein complex which is central to all bioenergetic processes , and common to most types of bacteria regardless of how they harness energy from their environment . Our results indicate an ancient origin for this protein complex , and suggest that different species , without necessitating major innovation , used their pre-existing ATP synthase and adapted it to work with different bioenergetic pathways . We also describe gene duplications and rearrangements of the ATP synthase subunits in different lineages , which suggest further flexibility and robustness in the control of ATP synthesis . | [
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"evo... | 2014 | Evolution of the F0F1 ATP Synthase Complex in Light of the Patchy Distribution of Different Bioenergetic Pathways across Prokaryotes |
Leishmania braziliensis is the most prevalent species isolated from patients displaying cutaneous and muco-cutaneous leishmaniasis in South America . However , there are difficulties for studying L . braziliensis pathogenesis or response to chemotherapy in vivo due to the natural resistance of most mouse strains to infection with these parasites . The aim of this work was to develop an experimental set up that could be used to assess drug efficacy against L . braziliensis . The model was tested using miltefosine . A L . braziliensis line , originally isolated from a cutaneous leishmaniasis patient , was passaged repeatedly in laboratory rodents and further genetically manipulated to express luciferase . Once collected from a culture of parasites freshly transformed from amastigotes , 106 wild type or luciferase-expressing stationary phase promastigotes were inoculated subcutaneously in young BALB/c mice or golden hamsters . In both groups , sustained cutaneous lesions developed at the site of inoculation , no spontaneous self- healing being observed 4 months post-inoculation , if left untreated . Compared to the wild type line features , no difference was noted for the luciferase-transgenic line . Infected animals were treated with 5 or 15 mg/kg/day miltefosine orally for 15 days . At the end of treatment , lesions had regressed and parasites were not detected . However , relapses were observed in animals treated with both doses of miltefosine . Here we described experimental settings for a late-healing model of cutaneous leishmaniasis upon inoculation of a luciferase-expressing L . braziliensis line that can be applied to drug development projects . These settings allowed the monitoring of the transient efficacy of a short-term miltefosine administration .
Leishmania spp . are the etiological agents of leishmaniasis , a complex of vector-borne infectious diseases transmitted by sand flies . The disease is widespread in the world in tropical and subtropical regions . Approximately 12 million people are affected worldwide and 1 . 2 million new cases occur each year [1] . Leishmaniasis is responsible for a spectrum of clinical manifestations including visceral , cutaneous , mucosal , disseminated and diffuse cutaneous disease that are determined , at least partially , by the species of the parasite [2] . In South America , Leishmania ( Viannia ) braziliensis is the most prevalent species isolated from patients displaying either cutaneous or mucocutaneous leishmaniasis [3–5] . This species is also associated with disseminated leishmaniasis [5] . In spite of its epidemiological importance , the study of L . braziliensis infections is limited by the difficulty of establishing good animal models of disease . L . braziliensis infects hamsters and induces a chronic cutaneous disease with no spontaneous healing [6 , 7] . However , inbred strains of mice are generally resistant to L . braziliensis or develop a transient and self-healing cutaneous lesion [7–10] . When the parasites are inoculated into partially susceptible strains such as BALB/c mice , a non-ulcerative lesion develops initially and evolves to spontaneous healing in about 30 days [8 , 10] . A mouse model of sustained skin lesions with L . braziliensis would be very useful for studies on the pathogenesis of the disease as well as a tool to develop new control strategies , such as vaccines and drugs . The treatment of leishmaniasis relies on a few drugs , most of them displaying some inadequacy to present requirements . They are expensive , toxic and require prolonged and parenteral administration . Additionally , drug resistance has been reported [11 , 12] . More than a decade ago , miltefosine ( MF ) was described as an oral drug against visceral leishmaniasis ( VL ) and has been widely used in the Indian subcontinent for that application [13–15] . On the other hand , MF efficacy for the treatment of cutaneous leishmaniasis ( CL ) seems to be dependent on the Leishmania species and even on intraspecies heterogeneity . Some clinical studies in South America identified cure rates varying from 53% to 91% in CL patients [16–18] . Two clinical studies performed in Brazil detected cure rates of 71 . 4% and 75% , respectively , in infections due to L . guyanensis [19] and L . braziliensis [20] . However , given the lack of other new treatment alternatives , further understanding of MF’s potentialities and limitations is necessary . We have recently described the use of luciferase as a reporter for drug efficacy evaluation in cutaneous and visceral leishmaniasis models [21 , 22] . In both models , luciferase has proven to be an accurate measure of parasite burden . In this study , we generated a transgenic line of L . braziliensis expressing luciferase that was adapted to induce chronic lesions in BALB/c mice . Using this experimental model , we evaluated the efficacy of MF to reduce lesion size and parasite load .
Amphotericin B sodium deoxycholate and MF were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . Stock solutions of amphotericin B and MF for in vitro experiments were prepared in sterile water ( 10 mM ) . Miltefosine used for the treatment of infected mice was prepared daily in sterile saline ( 0 . 9% NaCl ) from a 6 mg/mL stock solution . The L . braziliensis strain MHOM/BR/94/H3227 was kindly provided by Dr . Maria Jania Teixeira , from Universidade Federal do Ceará . These parasites were isolated from a cutaneous ulcer from a patient in Ceará State , Brazil and the strain was previously typed as L . braziliensis [10] . Promastigotes were grown in medium M199 ( Sigma-Aldrich ) supplemented with 10% heat-inactivated foetal calf serum , 2% urine , 0 . 25% hemin , 12 mM NaHCO3 , 50 U/mL penicillin and 50 μg/mL streptomycin at 25°C . For in vivo experiments , female BALB/c mice ( 4–5 week-old ) were inoculated with 106 stationary-phase promastigotes injected subcutaneously in the right hind footpad . Amastigotes were obtained from infected mice , as described [23] and differentiated back as promastigotes in M199 . Promastigotes were counted in a Neubauer haemocytometer . A construct containing the luc2 gene was previously built [22] taking advantage of the vectors pSSU-int and pSPαHYGα which were kindly provided by Dr . Tony Aebischer ( Robert Koch Institute , Berlin , Germany ) and Dr . Marc Ouellette ( Universite Laval , Quebec , Canada ) . The linear cassette purified upon Pac I and Pme I digestion of the construct described previously , was used to transfect L . braziliensis H3227 promastigotes . Briefly , the cassette contains the luc2 gene followed by a Leishmania 3’ UTR , the hygromycin phosphotransferase gene and fragments of the L . mexicana small subunit ( SSU ) ribosomal DNA ( rDNA ) at the cassette extremities to promote homologous recombination . Transfection was performed as described [24] using 5 μg of linear DNA . Twenty-four hours after transfection , 32 μg/mL hygromycin B was added for selection of mutants . The L . braziliensis transfectant line was kept for three passages in M199 containing hygromycin B and then plated on semi-solid M199 medium/1% agar supplemented with 1 . 2 μg/mL biopterin , 2% urine and 32 μg/mL hygromycin B for cloning [24] . Four independent clones were analysed for integration of the cassette into the rDNA locus by PCR amplification , using primers complementary to sequences inside and outside of the linear cassette . Genomic DNA of these clones was purified as described [25] for confirmation by PCR . Primers used were S1 ( 5’-GATCTGGTTGATTCTGCCAG-3’ ) and S4 ( 5’-GATCCAGCTGCAGGTTCACC-3’ ) [26] that anneal to the SSU rDNA sequence flanking the insertion sites and primers luc2F ( 5’-GCGGGATCCATGGAAGATGCCAAAAACATTAAG-3’ ) , luc2R ( 5’-CACGCGCATACATTCACGGCGTTACACGGCGATCTTGCCGC-3’ ) and luc2i ( 5’-GACCGACTACCAGGGCTTCC-3’ ) that anneal to the luc2 gene contained in the linear cassette ( Fig 1A ) [22] . Susceptibility of L . braziliensis wild type and transgenic lines to amphotericin B and MF was evaluated by a [3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyl tetrazolium bromide] ( MTT , Sigma-Aldrich ) viability test assay as described [27] . For this assay , promastigotes ( 2×106 parasites per well ) were incubated in the presence of increasing concentrations of amphotericin B ( 0 . 037 to 0 . 3 μM ) or MF ( 25 to 200 μM ) for 24 h . MTT cleavage was measured in a microplate reader ( POLARstar Omega , BMG Labtech , Ortenberg , Germany ) with a test wavelength of 595 nm and a reference wavelength of 690 nm . For intracellular amastigotes , bone marrow-derived macrophages ( BMDM ) from BALB/c mice were used as previously described [21] . BMDM were plated on round glass coverslips in 24-well culture dishes , at a density of 3 × 105 cells in 400 μL of RPMI 1640 medium ( Gibco , Invitrogen Corporation ) supplemented with 10% FCS ( Gibco , Invitrogen Corporation ) in a 5% CO2 atmosphere for 16 h at 37°C allowing macrophages to adhere . Macrophages were then infected with stationary-phase promastigotes using ratios varying from 20 to 35 parasites per macrophage for 3 h at 33°C . Non-internalized parasites were removed by washing with warmed PBS , followed by the addition of fresh medium containing increasing concentrations of amphotericin B ( 0 . 025 to 0 . 2 μM ) or MF ( 0 . 5 to 24 μM ) . After 48 h , the cells were fixed in methanol and stained using the panoptic haematological labelling method ( Instant Prov kit , Newprov , Pinhais , PR , Brazil ) . The percentage of infected macrophages was determined by counting 100 cells in three independent experiments . The half-maximal effective concentrations ( EC50 ) were determined from sigmoidal regression of the concentration-response curves . Assays were performed in triplicate and results are expressed as the mean and standard deviation ( SD ) of at least three independent experiments . The activity of luciferase was measured in recombinant L . braziliensis promastigotes at logarithmic and stationary growth phases . Parasites were washed twice with PBS and then serially diluted for the luciferase assay , which was performed using the One Glo Luciferase Assay System ( Promega Corporation ) according to the manufacturer’s instructions and as described previously [22] . Bioluminescence was measured in a microplate reader ( POLARstar Omega , BMG Labtech , Ortenberg , Germany ) . Each point was tested in triplicate in two independent experiments . Male golden hamsters ( 3 to 5 weeks-old ) were obtained from the Instituto de Medicina Tropical , University of São Paulo . Female BALB/c mice ( 3 to 5 weeks-old ) were from the Instituto de Ciências Biomédicas , University of São Paulo . Animals were kept in cages and received unlimited food and water . Given that the wild type ( Lb-WT ) and luciferase transgenic parasites ( Lb-LUC ) were in different passages as promastigote cultures , they were initially used to inoculate highly susceptible hamsters . Initially , 106 stationary phase promastigotes were injected into hamsters’ hind footpads in a volume of 30 μL . Once the infections were established , parasites of both lines were recovered from the inoculated footpads and differentiated back into promastigotes in M199 at 25°C . These promastigotes were then used to inoculate BALB/c mice using the same protocol described above , except for the host . When the lesions developed , parasites were recovered , transformed into promastigotes and used to inoculate mice and hamsters with the results described in the Results section . Disease progression was followed for 11 weeks in hamsters and for 24 weeks in BALB/c mice . BALB/c mice inoculated with Lb-WT or Lb-LUC were submitted to treatment with MF . Treatment was initiated 4 weeks post-inoculation , using two different doses of the drug: 5 or 15 mg/kg/day , in groups of 5 animals . MF was prepared daily from a stock solution of 6 mg/mL and was administered by oral gavage for 15 consecutive days . An untreated group was used as a control for each line studied . Lesion sizes were evaluated once a week by measuring the difference in thickness between the infected and the contralateral uninfected footpad using a caliper ( Mitutoyo Corporation , Kawasaki , Kanagawa , Japan ) . Parasite burden in Lb-LUC inoculated BALB/c mice was determined 6 weeks ( at the end of MF treatment ) , 17 and 23 weeks post-inoculation . The parasite load was quantified by measuring luciferase activity through bioimaging ( IVIS Spectrum , Caliper Life Sciences , Inc . MA/USA ) as described [21] . Briefly , before luminescence detection animals received 75 mg/kg luciferin ( VivoGlo Luciferin , Promega ) intraperitoneally followed by anesthesia in a 2% isoflurane atmosphere ( Cristália ) . Images were collected after 20 minutes using a high-resolution mode with 2 minutes exposure from a fixed-size region of interest . Results were quantified with Living Image software version 4 . 3 . 1 ( Caliper Life Sciences ) and were expressed as photons/second/square centimeter/steradian ( ph/sec/cm2/sr ) . Lb-WT parasites were recovered from infected mice that had been treated with 5 mg/kg/day ( n = 5 ) or with 15 mg/kg/day ( n = 5 ) MF at the 19th and 23th week post-inoculation , respectively . These parasites were differentiated into promastigotes in M199 medium and their susceptibility to MF was determined by the MTT assay as described above . As controls , parasites were also rescued from an untreated infected animal at the 19th week post-infection . Unpaired two-tailed Student’s t tests were used to compare the EC50 determined in vitro on promastigotes and intracellular amastigotes of wild type parasites and luciferase expressing lines and for comparing disease progression . Parasite burden was analysed for statistical significance by One Way ANOVA , followed by the Tukey post-test . All statistical analyses were performed using GraphPad Prism 6 . 0 software ( GraphPad Software , Inc . , La Jolla , CA USA ) . Results were considered significant at P < 0 . 05 . Animal experiments were approved by the Ethics Committee for Animal Experimentation of the Biomedical Sciences Institute ( Protocol: 178/138/02 ) and of the Tropical Medicine Institute ( Protocol: CPE-IMT 2012/145 ) of the University of São Paulo in agreement with the guidelines of the Sociedade Brasileira de Ciência de Animais de Laboratório ( SBCAL ) and of the Conselho Nacional de Controle da Experimentação Animal ( CONCEA ) .
To generate a luciferase expressing L . braziliensis line ( Lb-LUC ) , promastigotes of the H3227 strain were transfected with a linear cassette containing the luc2 gene flanked by SSU rDNA sequences ( Fig 1A ) . After transfection , parasites were selected in the presence of hygromycin B and cloned . Four independent clones were tested for the presence of the luc2 gene by PCR . Amplification of a 1 . 6 kb fragment with oligonucleotides complementary to the 5’ and 3’ ends of the luc2 open reading frame was observed in the four selected clones while the same reaction led to the absence of amplification from wild type parasites ( Fig 1B ) . Additional pairs of primers were used to confirm the correct integration into the ribosomal DNA locus in one of the selected clones ( clone 3 , lane 3 in Fig 1B ) . Primers S1 and S4 [26] , complementary to the rDNA outside the linear cassette , were used in combination with primers luc2R and luc2i , respectively ( Fig 1A ) . These two combinations of primers amplified the expected fragments of 2 . 4 and 6 . 7 kb in the transgenic line but not in wild type parasites ( Fig 1A and 1C ) . Bioluminescence in the transgenic line ( Lb-LUC ) was confirmed after incubation of parasites with luciferin . A linear correlation between the number of promastigotes and the production of light was observed ( Fig 1D ) . Growth curves for transgenic Lb-LUC and wild type ( Lb-WT ) promastigotes were indistinguishable ( S1A Fig ) . Logarithmic and stationary phase promastigotes presented similar light emission ( S1B Fig ) indicating that luciferase expression was stable along the growth curve . The stability of the transgene was also evaluated by removing the drug pressure from promastigote cultures . Luciferase expression was stable with the same levels of light production in parasites kept in the absence of hygromycin B for 25 passages in culture ( S1C Fig ) . No differences were observed in the infectivity of Lb-LUC and Lb-WT to macrophages after 24 or 48 hours evaluated by the morphology of infected cells , percentage of infected cells or number of amastigotes per infected macrophage ( S2A–S2D Fig ) . In addition , a direct correlation was observed between the number of Lb-LUC intracellular amastigotes per macrophage and bioluminescence ( S2E Fig ) . Finally , in vitro susceptibility of Lb-LUC and Lb-WT promastigotes and intracellular amastigotes to amphotericin B and MF showed no significant differences ( Table 1 ) . Hamsters and BALB/c mice were inoculated with 106 Lb-WT or Lb-LUC promastigotes subcutaneously in the footpad . In both animal models , there were no significant differences in the kinetics of lesion development or in lesion size when the two parasite lines were compared ( Fig 2A and 2B ) . In both models , lesions were non-ulcerative ( Fig 2C and 2D ) . In hamsters , lesions were detectable from two weeks post-inoculation , progressed steadily for the next five weeks and then the oedema stabilized till the 11th week post-inoculation ( Fig 2A ) , when animals were euthanized . In BALB/c mice , lesions were clearly detected after two weeks , progressed in size until weeks 9–10 and then decreased in the following weeks ( Fig 2B and S3 Fig ) , but the local oedema remained detectable until 23 weeks post-inoculation ( Fig 2B and S3 Fig ) . Therefore , no spontaneous healing was observed in the animal models studied during the period evaluated . BALB/c mice inoculated with Lb-LUC were examined by in vivo imaging after 6 , 17 and 23 weeks ( Figs 3 and 4 ) . Luminescence was detected from the lesion site in all mice at the 6th week post-inoculation ( Figs 3B and 4 ) . By week 17th , light emission was not detected in 2 out of 5 mice ( Figs 3C and 4 ) . No bioluminescence was detected in untreated animals at the 23th week post-inoculation ( Figs 3D and 4 ) , although measurable oedema was still observed in this group at the 24th week ( Fig 3A ) . The late-healing infection model of L . braziliensis H3227 in BALB/c mice was used to test MF efficacy . Treatment was initiated 4 weeks post-inoculation in animals injected with the transgenic line Lb-LUC ( Fig 3 ) or with the wild type parasites ( S3 Fig ) . Animals were treated with MF 5 or 15 mg/kg/day for 15 consecutive days . Both doses of MF resulted in regression of lesions at the end of the treatment ( 6 weeks post-inoculation ) ( Fig 3A and S4 Fig ) . Luminescence was not detected by bioimaging at the end of the treatment ( Figs 3B and 4 ) . However , 8 weeks post-inoculation a relapse was observed in animals treated with 5 mg/kg/day MF , with lesions progressing in size for the following weeks ( Fig 3A and S3 Fig ) . These findings were confirmed by quantification of bioluminescence 120 days post-inoculation ( 17th week ) , time when animals treated with 5 mg/kg/day presented a higher parasite burden than untreated animals ( Figs 3C and 4 ) . A clinically detectable relapse was also observed in animals treated with 15 mg/kg/day from the 15th week post-inoculation that progressed in the following weeks ( Fig 3A ) . Although no luminescence was detected in these animals at week 17th ( 120 days ) post-inoculation ( Figs 3C and 4 ) , parasites were clearly detected by in vivo imaging at the 23th week post-inoculation ( Figs 3D and 4 ) . Animals inoculated with Lb-WT and treated with both doses of MF presented the same pattern of disease progression , response and relapse to treatment observed with the Lb-LUC transgenic line ( Fig 3A and S3 Fig ) . To investigate whether these relapses were due to MF resistance acquired during the therapy regimen , parasites were recovered from 5 animals treated with MF 5 mg/kg/day ( at the 19th week post-inoculation ) and with 15 mg/kg/day ( at the 23th week post-inoculation ) ( S3 Fig ) . After differentiation to promastigotes , MF susceptibility was determined in parasites isolated from untreated and treated animals and no significant changes in drug susceptibility were found between these parasites ( Table 2 ) .
To the best of our knowledge , this is the first report of a luciferase-expressing L . braziliensis , the causative species of cutaneous and mucocutaneous leishmaniasis in humans and the most prevalent species in South America [2] . The importance of research on L . braziliensis infections cannot be overstated . However , it faces multiple difficulties largely derived from the distinct behaviour of this Leishmania species , in vitro and in vivo . L . braziliensis promastigotes do not grow easily in axenic cultures and amastigotes are scarce in the lesions . The lack of an appropriate animal model mimicking the human infection in all of its potential clinical presentations is perhaps one of the main drawbacks . Most L . braziliensis strains are infective to mice but lead to asymptomatic or short-lived , self-healing disease [28 , 29] . The infection is more sustained in hamsters [6 , 29] , but do not progress to a mucosal or disseminated disease . The development of lesions at the site of inoculation with the H3227 strain of L . braziliensis in BALB/c mice was previously described [10] . However , in the original description , lesions were self-limited and healed 30 days post-inoculation . This strain was kindly given to us by Dr . Maria Jania Teixeira ( Universidade Federal do Ceará , Brazil ) and , before being submitted to transfection , was passaged in mice repeatedly . Most likely as a result of these repeated in vivo passages , the line obtained after transfection , together with the parental wild type parasites were then able to induce a late-healing disease in BALB/c mice , as well as being pathogenic in hamsters . A late-healing animal model of L . braziliensis infection is likely to become very useful as a tool in drug development or in the evaluation of the disease pathogenesis . It is unclear at present whether other strains of L . braziliensis could be adapted to cause late-healing disease in BALB/c mice . We have attempted to do so with three other isolates as well as with the type strain of L . braziliensis M2903 without success but cannot rule out that possibility . It will be interesting to investigate whether mutations on this Leishmania isolate ( H3227 ) are present and could be correlated with the altered phenotype . These parasites were transformed to express the luciferase gene integrated into the ribosomal locus . Luciferase expression was stable throughout the life cycle . As previously reported for other luciferase-expressing Leishmania species [21 , 22 , 30] , this L . braziliensis transgenic line presented the same biological properties in vitro and in vivo of the parental line and was useful for parasite quantification of promastigotes and amastigotes , in vitro and in vivo . Luciferase as a reporter has become instrumental in pre-clinical drug development projects , especially in chronic infections . Our previous results with L . amazonensis [21] and L . chagasi [22] lines expressing luciferase clearly demonstrated that luciferase detection correlates well with parasite burden in vivo . In the case of H3227 parasites , the sensitivity of detection in vivo has proven to be insufficient to detect low parasite burdens , as was the case in Lb-LUC in untreated animals or when relapses were noticed in the groups treated with the higher dose of MF . This limitation in sensitivity should be taken into account in drug efficacy studies . Nevertheless , bioimaging was used successfully to document infection and as a semi-quantitative method for comparing parasite burden between different groups . The model of infection with Lb-LUC in BALB/c mice was put to test using MF , the newest addition to leishmaniasis chemotherapy . The susceptibility of the H3227 strain to MF in vitro can be considered comparable to other L . braziliensis strains [31 , 32] . Accordingly , at the end of MF treatment , Lb-LUC inoculated mice showed regression of lesions and absence of luminescence . However , although the doses used here were higher than the WHO recommended scheme for CL and VL ( 2 . 5 mg/kg/day ) [15] , a clinical relapse was evident in animals treated with 5 mg/kg/day as soon as two weeks after the end of treatment . In these animals , lesions progressed in the following weeks reaching sizes similar to the ones observed in untreated animals . In animals treated with 15 mg/kg/day , oedema in the footpad reappeared 10 weeks after the interruption of treatment . Relapses in VL patients treated with MF do not seem to be due to acquired resistance [33] . Likewise , our data showed similar MF susceptibility in parasites rescued from untreated and treated animals , indicating that L . braziliensis did not acquire resistance to MF during treatment . In VL patients , MF failure may be correlated with low drug exposure due to pharmacokinetic-pharmacodynamics factors [34] . MF has been shown to reach high levels in the skin [15] but poor drug distribution to cutaneous lesions may be another reason for the lower efficacy of the drug against CL when compared with VL . Furthermore , the greater genetic diversity of L . braziliensis compared to L . donovani [35 , 36] may increase the number of elements that contribute to treatment failure . Moreover , other factors , unrelated to drug resistance , should be considered as quiescence and even the presence of the LRV1 virus [37 , 38] . The persistence of viable parasites after treatment with 5 and 15 mg/kg/day MF per 15 days indicated that , even for the highest dose used , clinical response did not reflect sterile cure . In fact , sterile cure does not seem to be required for long-term control of the disease , as has been shown by the presence of parasites in healthy skin in animals [39 , 40] . On the other hand , relapses have been reported in patients treated with MF for all clinical forms of the disease [33 , 41–46] . Therefore , we believe the data presented herein stress the need for long-term follow up of MF treated patients and suggest that higher doses and/or longer treatment regimens should be evaluated in order to avoid relapses . In addition , the combined use of MF with other antileishmanial drugs should be considered as an alternative for the chemotherapy of the disease . | Leishmania braziliensis is the most prevalent species isolated from patients displaying either cutaneous or mucocutaneous leishmaniasis in South America . In this study , we developed a transgenic luciferase-expressing L . braziliensis line . These parasites were passaged in hamsters and mice and then transformed back into promastigotes . Once inoculated subcutaneously in the footpad of young laboratory animals—BALB/c mice or golden hamsters , rapid and sustained footpad thickness increase developed . This experimental model was used to monitor the parasite load fluctuations and the response to miltefosine treatment . Mice were treated orally over a two-week period , starting at week 4 post-inoculation . Though such a regimen was shown to display efficacy , the effect was not sustained and both parasite re-expansion and delayed footpad thickness increase were noticed . | [
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"anima... | 2016 | A Luciferase-Expressing Leishmania braziliensis Line That Leads to Sustained Skin Lesions in BALB/c Mice and Allows Monitoring of Miltefosine Treatment Outcome |
The biotrophic fungal pathogen Blumeria graminis causes the powdery mildew disease of cereals and grasses . We present the first crystal structure of a B . graminis effector of pathogenicity ( CSEP0064/BEC1054 ) , demonstrating it has a ribonuclease ( RNase ) -like fold . This effector is part of a group of RNase-like proteins ( termed RALPHs ) which comprise the largest set of secreted effector candidates within the B . graminis genomes . Their exceptional abundance suggests they play crucial functions during pathogenesis . We show that transgenic expression of RALPH CSEP0064/BEC1054 increases susceptibility to infection in both monocotyledonous and dicotyledonous plants . CSEP0064/BEC1054 interacts in planta with the pathogenesis-related protein PR10 . The effector protein associates with total RNA and weakly with DNA . Methyl jasmonate ( MeJA ) levels modulate susceptibility to aniline-induced host RNA fragmentation . In planta expression of CSEP0064/BEC1054 reduces the formation of this RNA fragment . We propose CSEP0064/BEC1054 is a pseudoenzyme that binds to host ribosomes , thereby inhibiting the action of plant ribosome-inactivating proteins ( RIPs ) that would otherwise lead to host cell death , an unviable interaction and demise of the fungus .
The obligate biotrophic fungus Blumeria graminis causes powdery mildew disease on some small grain cereals and grasses ( Poaceae ) . A high degree of host specificity is displayed by the fungus , with at least eight formae speciales ( f . sp . ) , each infecting a different host genus . Blumeria graminis f . sp . hordei and B . graminis f . sp . tritici colonise barley ( Hordeum vulgare L . ) and wheat ( Triticum aestivum L . ) , respectively , and they can result in large crop losses [1] . In the case of powdery mildews , the only part of the fungus that actually penetrates the plant during infection is the haustorium [2] , a dedicated fungal infection structure thought to absorb plant nutrients . The success of infection is determined by the outcome of a “secretory warfare” between the host and the pathogen , which produces effectors at the haustorial complex [2 , 3] . Genomes of powdery mildew fungi code for several hundred effector-like proteins: 491 Candidate Secreted Effector Proteins ( CSEPs ) were initially identified in the B . graminis f . sp . hordei genome [4] , and it has become clear that there are even more in this f . sp . [5] , while 844 are currently described in the recently updated B . graminis f . sp . tritici genome [6] . These putative effectors have a predicted amino-terminal signal peptide for secretion , lack recognisable transmembrane domains , and have no relevant BLAST hits outside of the powdery mildew family ( Erysiphaceae ) . As for most other pathogen effectors , their mode of action is not yet understood . Previously , two complementary bioinformatic procedures were used to identify CSEPs with predicted RNA-binding or RNase folds: InterProScan combined with Gene Ontology characterisation , and IntFOLD , an integrated structure prediction server . In total , 72 out of the then 491 known B . graminis f . sp . hordei CSEPs were found by these approaches , with 54 predicted by InterProScan and 37 by IntFOLD ( 19 of which were found by both techniques [4] ) . Proteins occurring within CSEP families with structural similarity to RNase and/or RNA-binding activity were termed RNase Like Proteins expressed in Haustoria ( RALPHs ) [7] . Some of the RALPH CSEP families contain members for which the RNase domain is not recognised by the current prediction algorithms . If the latter are included , the RALPHs comprise the biggest subset of effector candidates within the Blumeria graminis f . sp . hordei genome , numbering at least 113 of the previously analysed 491 CSEPs . Their abundance , and their proliferation within a genome that otherwise has lost numerous genes [8] , suggests that they play a prominent role during infection [4] . This notion is further supported by the fact that some RALPH effectors are recognised by dedicated plant immune receptors and thus act as avirulence factors in the plant-powdery mildew interaction [9] [10] . The genes encoding RALPHs , and other CSEPs , are often physically closely linked to ( retro- ) transposable elements , indicating that the duplication of these effectors may have occurred through illegitimate recombination of ( retro- ) transposon sequences [4] . An RNA interference ( RNAi ) -based screen for functionally important effector genes in B . graminis f . sp . hordei identified eight genes whose expression is required for full pathogenic development . These included two genes encoding RALPH effectors: CSEP0064/BEC1054 and CSEP0264/BEC1011 , both belonging to family 21 of the predicted CSEPs in this powdery mildew pathogen [4 , 11] . We have previously found four barley proteins that interact with CSEP0064/BEC1054 by in vitro affinity assays followed by liquid chromatography mass spectrometry analysis . These proteins comprise a eukaryotic Elongation Factor 1 gamma ( eEF1γ ) , a Pathogenesis Related Protein 5 ( PR5 ) , a Glutathione-S-Transferase ( GST ) and a Malate Dehydrogenase ( MDH ) . The respective protein-protein interactions were confirmed by subsequent yeast two-hybrid ( Y2H ) experiments [12] . Other barley proteins bind to CSEP0064/BEC1054 in vitro but do not interact in yeast . These are Pathogenesis Related Protein 10 ( PR10 ) , a ribosomal 40S subunit protein 16 ( 40S 16 ) , and a eukaryotic Elongation Factor 1 alpha ( eEF1α ) [12] . The predicted RNase structure of CSEP0064/BEC1054 was originally determined by in silico homology modelling . However , the protein , like other RALPHs , is unlikely to be catalytically active , because it lacks the critical active site residues known to be required for RNase activity [4] . Plants also possess RNases that are proposed to be implicated in defence against pathogens . Examples are the RIPs , which depurinate the sarcin-ricin loop ( SRL ) in the ribosomal RNA ( rRNA ) of the large ribosomal subunit [13] . In barley , the jasmonate-induced protein of 60 kDa ( JIP60 ) is a RIP involved in mediating host-induced cell death [14 , 15] . Here , we show that heterologous transgenic expression of CSEP0064/BEC1054 in wheat enhances susceptibility to B . graminis f . sp . tritici . Similarly , expression of this protein in Nicotiana benthamiana increases the susceptibility to the oomycete pathogen Peronospora tabacina , suggesting that CSEP0064/BEC1054 subverts a defence mechanism/ pathway conserved among monocotyledonous and dicotyledonous plant species . To provide mechanistic insights into the function of CSEP0064/BEC1054 , we solved the structure of the first RALPH by X-ray crystallography , determining unequivocally a high degree of structural similarity with fungal RNases . Furthermore , we confirm that CSEP0064/BEC1054 interacts with ribonucleic acid , and interferes with methyl-jasmonate induced cleavage of RNA in wheat . Together , these findings provide the first evidence for a RALPH-mediated interference with host RNA metabolism . Given the similarity of the predicted structure of Blumeria RALPH proteins to RNases and to the core region of fungal RIPs , we hypothesise that RALPHs may stoichiometrically outcompete host RIPs , thereby inhibiting their cell death-promoting activity and thus act as a pseudoenzyme [16] .
We previously assessed the contribution of CSEP0064/BEC1054 to the interaction of barley and its adapted powdery mildew pathogen , B . graminis f . sp . hordei , by Host-Induced Gene Silencing ( HIGS; [11] ) . In this native context , CSEP0064/BEC1054 contributes significantly to fungal virulence: when the respective gene is silenced by HIGS , the infection success ( haustorial index ) drops to less than half of the value obtained with a control construct . To explore the effect of CSEP0064/BEC1054 on interactions with additional pathogens , we generated transgenic bread wheat ( T . aestivum ) lines constitutively expressing a codon-modified version of CSEP0064/BEC1054 lacking the N-terminal signal peptide for secretion ( termed wBEC1054 ) , which was also used in our previous HIGS experiments [11] . We selected progeny of three homozygous T3 lines , two carrying single expressed copies of the CSEP0064/BEC1054 transgene ( +/+ , lines 3 . 3 . 7 and 3 . 3 . 14 ) and one null segregant ( lacking the transgene ) , referred to as azygous ( -/- , line 3 . 3 . 12 ) , which served as a negative control in our study . We first determined whether expression or potential unintended gene disruption by the transgene affects morphology and/or development of the wheat lines . To this end , we investigated the phenotype of adult T4 plants homozygous for the effector transgene ( +/+ ) or respective azygous controls ( -/- ) under the same experimental conditions as used for subsequent assays . According to common practice for the phenotypic characterisation of wheat plants [17–20] , eleven characteristics were assayed quantitatively: leaf number , maximum height , peduncle ( internode 1 ) and other internode lengths , ear length , subcrown length , fertile tiller number , tiller mass and grain number . The respective values for these parameters from the azygous individuals and the transgenics were indistinguishable ( S1 Fig ) ; that is , the presence of transgenic CSEP0064/BEC1054 does not affect the adult phenotype of wheat , in our experimental conditions . We next measured the effect of the CSEP0064/BEC1054 transgene on the susceptibility of the wheat lines to its adapted powdery mildew pathogen , B . graminis f . sp . tritici . Microcolonies with epiphytic hyphae formed can be used as a proxy for the presence of functional haustoria . This parameter can be expressed as the proportion of microcolonies relative to the number of germinated conidia ( propH; Fig 1A ) . We determined the propH value at the base , the middle and the tip of both young and mature leaves of T4 individuals of the transgenic lines ( +/+ ) and the azygous plants ( -/- ) . The median propH was consistently higher in a statistically significant manner in leaf blades of plants derived from the transgenic lines ( +/+ ) as compared to the controls ( -/- ) , irrespective of the position within the leaf ( base , middle , tip ) or the age of the leaf ( young , mature; Fig 1A; S1 Table ) . Notably , the spread of the data increased from the leaf base to the leaf tip in both young and mature leaves , as shown by the increasing size of the boxes in the boxplots and their respective error bars ( Fig 1A ) . To test the contribution of CSEP0064/BEC1054 to a plant-microbe interaction with a dicotyledonous host species and an adapted pathogen different from powdery mildew , we opted for transient expression of CSEP0064/BEC1054 in Nicotiana benthamiana and subsequent challenge with the oomycete pathogen Peronospora tabacina , the causal agent of the tobacco downy mildew disease . Agrobacterium tumefaciens-mediated transformation was used to transiently express either Green Fluorescent Protein ( GFP ) -tagged CSEP0064/BEC1054 ( C terminal tag ) , or GFP alone as a control , in N . benthamiana ( in each case co-expressed with RFP as a transformation marker ) . Leaves from four-week-old leaves were detached , and co-infiltrated with Agrobacteria delivering plasmids encoding CSEP0064/BEC1054-GFP and RFP on one side of the midrib , and with Agrobacteria delivering plasmids encoding GFP and RFP on the other . The leaves were then inoculated with P . tabacina sporangia on the abaxial surface , and the number of sporangia produced on the leaf after ten days was assayed . The presence of GFP-tagged CSEP0064/BEC1054 significantly increased the mean of the sporangia produced as compared to the GFP control ( Fig 1B ) . In conclusion , transgenic expression of CSEP0064/BEC1054 promotes virulence of diverse adapted pathogens ( B . graminis f . sp . tritici , P . tabacina ) in monocotyledonous ( wheat ) and dicotyledonous ( N . benthamiana ) plant species . In order to test physical in planta interactions between CSEP0064/BEC1054 and several host proteins previously identified by protein in vitro affinity-LCMS experiments and targeted Y2H assays ( PR5 , PR10 , eEF1α , eEF1γ , ribosomal protein 40S 16 , MDH and GST; [12] ) , we performed BiFC experiments with the fungal effector and the respective candidate targets [21] . We excluded PR5 from this analysis since PR5 is known to represent a secreted protein with extracellular localisation , but included in addition a barley nucleoside-diphosphate kinase ( NDPK ) that was used as a negative control in previous Y2H assays [12] . Apart from CSEP0064/BEC1054 we added a closely related CSEP ( CSEP264/BEC1011 , CSEP family 21 , 45% amino acid identity to CSEP0064/BEC1054 ) and a more distantly related CSEP ( CSEP0102 , CSEP family 20 , 17% amino acid identity to CSEP0064/BEC1054 ) as bait proteins to these experiments ( Fig 2A ) . Similar to CSEP0064/BEC1054 , both CSEP0264/BEC1011 and CSEP0102 are predicted to be RNase-like proteins . We first analysed the in planta subcellular localisation of all putative interaction partners ( three CSEP bait and eight barley prey proteins ) . The bait and prey proteins were translationally fused at the C-terminus with monomeric yellow fluorescent protein ( mYFP ) and transiently co-expressed with the red-fluorescent protein DsRED ( as a transformation marker ) in barley leaf epidermal cells [22] . Expression and subcellular localisation were determined via confocal microscopy ( S2 Fig ) . In the case of all CSEPs , there was a weak and diffuse fluorescence signal in the cytoplasm and the nucleus; in addition , occasionally a punctate distribution of these proteins was observed in the cytoplasm . The PR10-mYFP fusion was distributed fairly homogeneously throughout the cytoplasm , with brighter fluorescence evident in the nucleus . The GST-mYFP fusion protein yielded a very diffuse and faint signal in the cytoplasm and nucleus , with bright puncta in the cytoplasm , and a bright signal in a section of the nucleus . Fluorescence of three elongation factor fusions ( eEF1γ-mYFP , eEF1α ( 1 ) -mYFP and eEF1α ( 3 ) -mYFP ) was detectable in the cytoplasm , and for eEF1α ( 1 ) -mYFP and eEF1α ( 3 ) -mYFP ( two paralogs of eEF1α we cloned from barley ) fluorescence was also observable in the nucleus . The 40S 16-mYFP fusion protein yielded a very diffuse and faint signal in the cytoplasm and the nucleus , with bright puncta in the cytoplasm , and a bright signal in the nucleus . The NDPK- and MDH-mYFP fusions were both observed in the nucleus and cytoplasm ( S2 Fig ) . Taken together , all bait and prey fusion proteins yielded detectable in planta mYFP fluorescence and were present in the cytoplasm and in part in the nucleus , allowing for potential interaction in these cellular compartments . BiFC analysis was performed in barley leaf epidermal cells by co-expression of the three CSEP proteins ( CSEP0064/BEC1054 , CSEP0264/BEC1011 and CSEP0102 ) , N-terminally tagged with the C-terminal half of mYFP , the barley prey proteins C-terminally tagged with the N-terminal domain of mYFP , and DsRED ( as a transformation marker ) . We semiquantitatively assessed BiFC-mediated YFP fluorescence intensity based on 9–45 inspected cells per tested bait-prey combination and four distinct phenotypic categories ( see Materials and Methods for details ) . Overall , we noticed some cell-to-cell variation of the BiFC signal , consistent with the known propensity of the BiFC assay to stabilise weak protein-protein associations depending on experimental conditions and expression levels [23] . Nonetheless , for CSEP0064/BEC1054 , a high number of cells with a clear YFP signal was found upon co-expression with PR10 ( Fig 2B and 2C ) . This situation differed from the six other tested barley candidate proteins and the NDPK negative control , for which only occasionally fluorescent cells , typically with lower fluorescence intensity , were found ( Fig 2B ) . For CSEP0264/BEC1011 , which is closely related to CSEP0064/BEC1054 ( 45% amino acid identity; Fig 2A ) , we likewise scored a positive interaction with PR10 . In addition , the CSEP0264/eEF1α ( 3 ) combination yielded a high number of fluorescent cells ( Fig 2B and 2C ) . By contrast , all tested interactions of barley prey proteins with CSEP0102 , which is more distantly related to CSEP0064/BEC1054 ( 17% amino acid identity; Fig 2A ) , failed to show a consistent BiFC signal ( Fig 2B ) . In case of the positive bait-prey pairings , YFP fluorescence was observed in the nucleus and in the cytoplasm . This pattern largely matched the subcellular localisation of the CSEP bait proteins expressed alone ( S2 Fig ) . To provide detailed insights into the molecular architecture of RALPH effectors , we determined the structure of CSEP0064/BEC1054 by X-ray crystallography . An initial model obtained from a dataset phased by iodide-SAD ( single-wavelength anomalous diffraction ) was subsequently used to calculate phases for a native dataset at 1 . 3 Å via molecular replacement . Data processing and refinement statistics for the structures are outlined in S2 Table . Analysis using the Dali server [24] confirmed that CSEP0064/BEC1054 is a close structural homologue of T1 RNases , a family of enzymes with high degree of specificity for cleavage of substrates at the 3’ end of guanosyl residues . The crystal structure reveals a canonical ( α+β ) fold comprising a core four-stranded anti-parallel β-sheet packed against an α-helix , and a second shorter peripheral anti-parallel β-sheet formed from the N-terminal hairpin and C-terminal strand β7 ( Fig 3A ) . This arrangement is constrained by a disulphide bridge formed between residues C6 and C92 , a feature that is universally conserved in the T1 RNase family [25] . In T1 RNases , a catalytic triad is located inside a concave face formed by the main β-sheet and loops β3-β4/β6-β7 [26 , 27] . Notably , this triad is absent in CSEP0064/BEC1054 ( Fig 3B , [4] ) , but several other features in the protein suggest a role in nucleic acid binding . Structural superposition of CSEP0064/BEC1054 with F . moniliforme F1 RNase ( 1FUS ) , indicates that contacts made by the RNase with the phosphate and ribose moieties of 2’ guanosine monophosphate ( 2’GMP ) are conserved ( Fig 3C ) . Residues Y34 and Q52 in CSEP0064/BEC1054 overlay with residues Y38 and E58 from the F1 RNase that form contacts with the phosphate moiety of 2’GMP . Additionally , catalytic residue R77 is replaced by Y70 in CSEP0064/BEC1054 , a residue that may form polar contacts with the phosphate . Differences in the conformation of loops β3-β4 and β6-β7 account for the main variations in the active site architectures between CSEP0064/BEC1054 and other T1 RNases . In the crystal structures of T1 RNases , these loops form an occluded binding cleft for the substrate [27–29] . In the crystallized conformation of CSEP0064/BEC1054 , these loops do not fold over , leaving the main β-sheet with a larger exposed hydrophobic surface ( Fig 3D ) . Relative to the T1 RNases , CSEP0064/BEC1054 has a more compact β3-β4 loop , as it is two residues shorter and contains a single-turn 3–10 α-helix . In this loop , Y42 and Y45 of the F1 RNase are involved in π stacking interactions with the guanosine base [28] . Reconstruction of a comparable active site geometry would require displacement of F38 and F44 in the loop upon the concave surface of CSEP0064/BEC1054 ( Fig 3D ) . Additionally , residues N43 and N44 in F1 RNase establish hydrogen bonds with guanosine base substrates via backbone atoms; a conformational change has been observed for these residues between ligand-free and 2’GMP-bound forms of the protein [28] . For a comparable interaction , a movement in the same loop would be required to bring the backbone atoms of residues I39 and I40 in CSEP0064/BEC1054 within hydrogen bonding distance of the guanosine base . Charged amino acid residues are clearly segregated on the protein surface ( Fig 3E ) . Most of the negatively charged side chains cluster in the vicinity of a small β-sheet: strands β1 , β2 and β7 ( residues D5 , D7 , E10 , E13 , E56 , D57 , D59 , E81 and E98 ) , whereas the majority of positively charged residues localises to the periphery of the concave surface ( R27 , K28 , K68 and K91 , Fig 3D ) . In T1 RNases , these positions flank the RNA binding site , and it is thus feasible that these residues in CSEP0064/BEC1054 interact with the phosphate backbone of nucleic acids . Intriguingly , the structures of RNases T1 from Aspergillus oryzae ( 9RNT ) [27] and F1 from F . moniliforme ( 1FUS ) [28] do not display the same segregation of positive and negative charges on the protein surface . The concave surface of CSEP0064/BEC1054 is lined with aromatic residues ( Y34 , F38 , F44 , Y70 , Y79 and F83 ) that could be involved in base-stacking interactions with nucleic acids ( Fig 3D ) . Alternatively , these residues could contain a binding site for host proteins , resembling the conserved hydrophobic patches of effectors AVR1-CO39 and AVR-Pia from the rice blast fungus Magnaporthe oryzae [30] . Due to the structural similarity between CSEP0064/BEC1054 and other fungal RNA-binding proteins , we tested potential association of the protein with RNA . Firstly , CSEP0064/BEC1054 was labelled with the fluorophore NT-927 and titrated against increasing concentrations of total RNA extracted from barley up to a final concentration of 10 μg/μl in microscale thermophoresis ( MST ) experiments ( Fig 4 ) . An interaction with total RNA was observed , and whilst the complex nature of the substrate precludes accurate calculation of a KD , we estimate this to be in the low micromolar range based on an averaged molecular weight for the most abundant RNA species ( rRNA ) within a pool of extracted total RNA . To further probe interactions with specific rRNA sequences , the protein was titrated with an RNA oligonucleotide corresponding to specific 32 bp sequence derived from the SRL of the barley 28S rRNA ( Fig 4B ) . Affinity for this substrate was over two orders of magnitude weaker than for barley total RNA and the interaction could not be saturated with up to 1 mM ligand using MST , preventing a precise estimation of a KD . Isotherms suggest that the interaction affinity ( KD ) is likely to be in the high μM-low mM range ( Fig 4 ) , suggesting that this rRNA region may not be the biologically relevant ligand for CSEP0064/BEC1054 . However , no binding was observed to an RNA oligonucleotide with the sequence of the bacteriophage T7 promoter ( a non-rRNA control ) under the same conditions , suggesting that CSEP0064/BEC1054 exhibits some binding specificity to ribosomal ribonucleic acids ( Fig 4 ) . No further evidence for sequence specificity was observed . CSEP0064/BEC1054 was further structurally characterised in solution via NMR spectroscopy following isotopic 15N and 13C labelling of the protein . S3 Fig shows the 1H-15N HSQC spectrum for CSEP0064/BEC1054 , where each cross peak corresponds to an amide group in the protein . Good dispersion of cross peaks indicates that the protein is monomeric and well-folded in solution . Using conventional 3D experiments ( see Methods ) , backbone resonances from 89 out of 94 residues were assigned ( i . e . excluding the backbone imines of three prolines ) . Unassigned residues also display large crystallographic B factors ( S42 , F44 , G46 , G64 and S88 ) , indicating that they experience motions that prevent sampling of a defined chemical environment ( e . g . at intermediate exchange regime in NMR timescales ) . These residues map to loop regions on opposite faces of the core β sheet , which suggests that ligand binding by CSEP0064/BEC1054 may involve displacement of these regions towards a structure that resembles the ligand bound conformation of T1 RNases . To provide further insights into the nature of these interactions and the mechanism of binding , chemical shift perturbation ( CSP ) analyses using 1H-15N HSQC NMR spectra were analysed upon titration of CSEP0064/BEC1054 with the barley SRL RNA sequence . Titration with 8 mole equivalents of this ligand induced discrete chemical shift changes in a subset of resonances . To reach saturation of CSEP0064/BEC1054 , the titration was repeated with a single-stranded SRL DNA oligonucleotide predicted to have the same fold as the RNA SRL oligonucleotide ( it was not possible to obtain enough single-stranded RNA , for this purpose ) . This induced small CSPs for the same residues observed in the RNA SRL titration , which showed saturation at ~50 molar equivalents ( Fig 5A ) . The continuous shift in the position of resonances upon titration indicated that binding was weak , with a KD above 10 μM ( i . e . in fast chemical exchange in NMR timescales ) , thus correlating with the results obtained from MST . In these experiments , solution conditions were carefully controlled after each titration to ensure that the observed CSPs were not an artefact from pH changes . CSPs were mapped onto the crystal structure of CSEP0064/BEC1054 , ( Fig 5B and 5C ) . Interestingly , residues with the largest chemical shifts are F38 ( β3 ) , I39 , I40 ( loop β3-β4 ) and V50 ( β4 ) , i . e . in the region containing residues that determine ligand binding specificity in T1 RNases , as described above . A second subset of CSPs ( N25 , E29 , F31 , G33 ) map in the loop α1-β3 , where ligand binding is also expected to induce changes in the chemical environment of amides after interaction with the neighboring β3-β4 loop . JIP60 is a RIP ( [32] ) whose transcript and protein accumulates in response to treatment with MeJA , a stress-related phytohormone in plants [13] . The action of JIP60 on rRNA in plants can be observed by the accumulation of a characteristic degradation product , visible as new RNA species following in vitro treatment of total RNA with aniline [33] . We incubated primary leaves of the transgenic wheat plants constitutively expressing CSEP0064/BEC1054 with MeJA , extracted total RNA , and treated it with aniline . We then analysed the respective RNA fragments by separation in a microchannel-based electrophoretic cell . In the RNA samples obtained from leaves of azygous wheat ( negative controls ) , the aniline treatment resulted in the appearance of a small peak that migrated at an apparent size of about 1 , 200 bases ( Fig 6 , S6 Fig ) . In the RNA profiles from CSEP0064/BEC1054 transgenic leaves processed in the same way , the area of this peak was reduced . Likewise , this peak was reduced or absent in RNA from CSEP0064/BEC1054 transgenic plant leaves extracted at eight days post inoculation with B . graminis f . sp . tritici or at five days after MeJa treatment . In the controls with no MeJA induction , the area of the peak was very small or not detectable ( Fig 6A ) . In sum , these data suggest that MeJa treatment triggers fragmentation of rRNA , which is indicated by the occurrence of a presumptive degradation product . This process appeared to be impeded by the presence of CSEP0064/BEC1054 or by powdery mildew infection . We then measured these effects quantitatively . The area of the peak representing the presumptive rRNA degradation product was determined , relative to the area of the small ( 18S ) and large ( 28S ) rRNAs . The abundance of the newly occurring RNA species was estimated to be up to about 10% of the 28S rRNA peak , depending on the experimental conditions ( Fig 6B ) . The average areas of the peak corresponding to the putative degradation product in RNA from uninfected plants homozygous for the CSEP0064/BEC1054 transgene ( line 3 . 3 . 14 ) was lower than in the azygous controls ( line 3 . 3 . 12 ) , but this difference is not statistically significant . In azygous plants , powdery mildew infection further decreased the formation of the degradation peak , almost completely abolishing it . The infection status had a statistically significant effect on the mean area of the presumptive degradation peak: overall , powdery mildew infection prevented the formation of the peak . The marked decrease in the degradation peak area also occurred in another transgenic line ( line 3 . 3 . 7; S7 Fig ) . The statistical analysis of the effect of the transgene showed that the peak area was intermediate between that of the azygous infected and non-infected leaves , and was not significantly different from either . Therefore expression of CSEP0064/BEC1054 was not sufficient to inhibit the action of the MeJA-induced RNA degradation , to the extent seen in infected plants .
CSEP0064/BEC1054 is a barley powdery mildew candidate effector protein that is highly expressed at early stages of infection [34] . Full expression of the gene is necessary for virulence of B . graminis f . sp . hordei . [11] . Here we investigated further the molecular and structural basis for function of this presumptive pathogen effector . First , we measured the proportion of germinating conidia that lead to the formation of functional haustoria ( propH ) as deduced by the development of fungal microcolonies on the surfaces of infected leaves on transgenic wheat expressing CSEP0064/BEC1054 . PropH was significantly higher in leaves from plants that are homozygous for the transgene , compared to the azygous controls ( Fig 1A ) . We noted differences in the outcome along the leaf blade: the effect of the transgene was more marked at the base of the leaf blades , intermediate in the middle sections and all but disappeared at the apical sections . This is not altogether surprising as leaf maturation in monocots displays a basipetal pattern [35 , 36] , and gene expression also occurs in a longitudinally non-uniform manner for leaves of species belonging to the Poaceae [37–39] . The slope in propH could be related to the basipetal gradients observed for immune-related transcripts . Genes encoding transcriptional regulators , such as WRKY factors , display differential expression along leaf blades , with two rice genes coding for WRKY proteins being expressed most highly at the leaf tip , and 13 most highly at the leaf base [39] . Several WRKYs play central roles in modulating disease resistance [40] . Therefore , a gradient in WRKYs and other proteins that control disease resistance [39] could explain the differences along the leaf blades in the effectiveness of CSEP0064/BEC1054 in counteracting host defence we observed in our experiments . Similar patterns of susceptibility were also recently observed in barley leaves infected by P . palmivora [41] . In a separate set of experiments , leaves subjected to Agrobacterium–mediated transient expression of CSEP0064/BEC1054 with a C-terminal GFP tag in the dicot N . benthamiana were subsequently inoculated with the oomycete P . tabacina . Expression of CSEP0064/BEC1054-GFP led to an increase in the P . tabacina sporangia formed when compared with a GFP-only control , indicating an increased susceptibility to the oomycete pathogen ( Fig 1B ) . Taken together , these results point to CSEP0064/BEC1054 affecting plant immunity that limits the infection with pathogens adapted to their specific host . Importantly , the immune pathways targeted by the candidate effector appear to be conserved in monocotyledonous and dicotyledonous plant species , and affect defence against both fungal and oomycete biotrophic pathogens . Several host proteins interact with CSEP0064/BEC1054 in vitro and in yeast [12] . Here , we used BiFC to test whether these host targets associate with CSEP0064/BEC1054 in barley epidermal cells , i . e . in the native cell context of this powdery mildew effector candidate . The fluorescence signal of CSEP0064/BEC1054 tagged with intact mYFP , as control , was diffuse and less intense in comparison to the fluorescence of mYFP-tagged candidate interaction partners ( S2 Fig ) . Faint signals could be due to poor translation , misfolding and/or rapid turnover of this fusion protein–processes that might be indicative of intolerance of this protein—at least at high levels—in eukaryotic/host cells . This scenario would be consistent with the cell growth interference phenotype previously observed in yeast expressing CSEP0064/BEC1054 [12] . Of the seven tested proteins , only PR10 interacted reproducibly with CSEP0064/BEC1054 in BiFC experiments in barley ( Fig 2B and 2C ) . The fluorescent signal matched the expression and subcellular localisation pattern observed for CSEP0064/BEC1054-mYFP ( S2 Fig ) . While this interactor was originally identified in affinity-LCMS experiments , the Y2H tests for PR10 had given inconclusive evidence of interaction due to up-regulation of the β-galactosidase reporter gene , and inconsistent results from the other reporter systems used . Apart from CSEP0064/BEC1054 , also the closely related CSEP0264 gave positive BiFC signals with PR10 . This CSEP in addition interacted with eEF1α ( 3 ) , while all other tested interactions yielded essentially background signals ( Fig 2B ) . Absence of positive BiFC signals does not necessarily mean that the respective interactions do not occur in planta . We only tested one of the four possible BiFC constellations regarding the tagging of the bait and prey proteins with halves of the YFP fluorophore . Further experiments will thus be needed to obtain a more comprehensive picture of the in planta host targets of CSEP0064/BEC1054 . The fact that the PR10 host protein is capable of consistently associating with CSEP0064/BEC1054 in independent , orthogonal assays ( affinity-LCMS and BiFC ) supports the notion that this interaction may reflect the situation in the true powdery mildew ( barley–B . graminis interaction ) context . This idea is further supported by the fact that the closely related CSEP0264 , but not the distantly related CSEP0102 , also interacts with PR10 ( Fig 2B and 2C ) . The existence of multiple interactions between single effectors and host proteins is not unprecedented and has been well documented in several instances [42 , 43] . The fact that eEF1α ( 3 ) , which was originally identified as an interactor of CSEP0064/BEC1054 in in vitro and Y2H assays , yielded positive BiFC signals with CSEP0064 but not CSEP0064/BEC1054 may indicate that CSEP0264 is the bona fide interactor of this host protein in planta . Cross-interaction of eEF1α ( 3 ) and CSEP0064/BEC1054 in affinity-LCMS and in yeast could be based on the high sequence similarity of CSEP0064/BEC1054 and CSEP0264 ( Fig 2A ) , which may allow for the association of CSEP0064/BEC1054 with eEF1α ( 3 ) in non-native conditions . The significance of interactions between pathogen effectors and the PR10 protein may be that they are capable of modulating the antimicrobial activity of these defence-related polypeptides . PR10 proteins belong to the family of Bet v 1 ( birch pollen allergen ) homologs [44] . Interestingly , some PR10 proteins have been reported to have RNase activity [45] . An unrelated barley PR protein ( PR17c ) that is required for defence against B . graminis f . sp . hordei also physically interacts with a B . graminis f . sp . hordei candidate effector protein ( CSEP0055; [46] ) . These putative effectors may somehow modulate the antifungal activity of the PR proteins . It remains to be seen whether this is indeed the case for the CSEP0064/BEC1054-PR10 interaction . Our studies also aimed to test whether CSEP0064/BEC1054 binds RNA . Binding isotherms using MST show a clear interaction with total RNA , suggesting that the protein can either bind to RNA unspecifically or that it is capable to recognise a distinct sequence in a well populated species , e . g . an rRNA motif . Experiments using RNA oligonucleotides show that CSEP0064/BEC1054 recognises the SRL of rRNA as a specific ligand , relative to a sequence encoding the bacteriophage T7 promoter , used as a negative control ( Fig 5 ) . Using 1H-15N HSQC NMR spectra , we observed discrete chemical shifts changes upon titration of the SRL RNA oligonucleotide , indicating a weak interaction . This implies that , for a biologically relevant association , the protein may require additional binding elements such as those present in an RNA-protein complex like the intact ribosome , or that a different RNA sequence is recognised in vivo . Structural modelling of CSEPs from the B . graminis f . sp . hordei genome [4] suggested that RNase-type folds are considerably overrepresented relative to other protein folds . For example , CSEP0064/BEC1054 ( from CSEP family 21 ) was predicted to exhibit a fold characteristic to members of the T1 RNase family . As previously noted [4] , B . graminis f . sp . hordei RALPHs lack the canonical catalytic triad of RNases and are thus unlikely to possess RNase activity . Nonetheless , two main structural features indicated that CSEP0064/BEC1054 may be an RNA-binding protein . In fact , ( 1 ) the distribution of specific positive charges and ( 2 ) the clustering of aromatic side chains in the concave face of the domain ( Fig 3D ) are similar to that observed on the surfaces of F1 RNases involved in RNA ligand binding [27 , 47] . However , it appears that these features would not confer CSEP0064/BEC1054 the same substrate specificity observed in case of these RNases . In particular , this RALPH candidate effector lacks the specific arrangement of backbone and sidechain atoms required for guanosine binding and could instead display a preference for other RNA substrates , or even bind nucleic acids non-specifically , as observed in members of the RNase T2 family [48] . A defined negatively charged patch on the CSEP0064/BEC1054 surface is evident ( Fig 3E ) . This feature contrasts with the widespread occurrence of positively charged patches in RNA-binding proteins , with an average area five times larger than negatively charged ones [49] . In CSEP0064/BEC1054 , this region may serve to bind RNA via a counterion , as observed in the interaction of ribosomal protein L11 to rRNA using Mg2+ [50] , or to repel the ligand and electrostatically orient it towards the proposed binding site , as demonstrated for the negatively charged patch of the Gp2 inhibitor of Escherichia coli RNA polymerase [51] . Fungal ribotoxins are another class of proteins related to T1 RNases [52] . Submission of CSEP0064/BEC1054 to the Dali server [53] identified fungal ribotoxins as structural homologues of this effector candidate . Ribotoxins display elongated loop regions and a stretch of lysine residues ( K111-K113 ) that are critical for interaction with the SRL bulged G motif [54 , 55] . Although CSEP0064/BEC1054 does not display comparable loop regions , it may specifically recognise RNA and/or protein features on the host ribosome to protect rRNA from the action of RIPs from the host . Many other plant ribotoxins also lack the triple lysine motif of fungal ribotoxins ( including JIP60 ) , and contact the SRL via alternative sequences , namely conserved tyrosine and tryptophan residues that form the “N-glycosidase signature” [56] . An additional observation is that CSEPs that are predicted to possess an RNase-like fold , also show some conservation of specific amino acid residues within the putative RNA-binding site but not elsewhere ( S4 Fig ) . This conservation underscores the potential importance of the role for RALPHs’ RNA-binding for their effector function: effector paralogues may recognise the same host target , and might have diverged under strong evolutionary pressures to escape recognition by surveilling host resistance ( R ) proteins [7] . Notably , a CSEP with a predicted RNase-like fold ( CSEP0372 ) has been recently recognised as a CSEP with MLA avirulence activity ( AVRa13 ) in B . graminis f . sp . hordei . Perception of CSEP0372 by the cognate barley nucleotide-binding domain and leucine-rich repeat ( NLR ) R protein MLA13 results in isolate-specific resistance . Among 17 fungal isolates examined , this CSEP gene was found to be present as five allelic variants of which two confer virulence and three confer avirulence to the fungal pathogen . Another avirulence protein reported in this study ( AVRa1 ) shows only weak predicted structural similarity with RNAses [9] . More recently , four additional CSEPs with different MLA avirulence activities were identified in B . graminis f . sp . hordei ( AVRa7 , AVRa9 , AVRa10 and AVRa22 ) . When analysed on the basis of the CSEP0064/BEC1054 X-ray structure resolved in the present work , only AVRa7 and AVRa13 exhibited significant structural similarity with CSEP0064/BEC1054 , while no meaningful structural similarities with CSEP0064/BEC1054 were noted in the case of AVRa1 , AVRa9 , AVRa10 and AVRa22 . In summary , these data suggest that allelic MLA immune receptors are capable to mount potent immune responses upon recognition of structurally unrelated CSEPs as avirulence determinants [57] . The structural similarity of CSEP0064/BEC1054 with RNases and ribotoxins raises the possibility that RALPHs like CSEP0064/BEC1054 bind motifs similar to those recognised by RIPs expressed by the plant host . This prompted us to test the effect of CSEP0064/BEC1054 expression on the integrity of host RNAs . In general , MeJA can induce production of RIPs [13] , which cleave an adenine base from the large rRNA subunit sugar-phosphate backbone [58–60] . This exposes the phosphodiester bond in the sugar-phosphate backbone , which can then undergo chemical hydrolysis within the cell [58 , 61 , 62] . In vitro , the process can be reconstituted by treatment of depurinated rRNA with aniline , which cleaves the sugar-phosphate backbone at the site of the modified nucleotide [63] . This results in the formation of two defined rRNA fragments , ca . 3 , 000 and 400 nucleotides long , respectively . The smaller rRNA fragment has been observed in barley and is an indicator of RIP activity [33] . Our experiments showed that RNA extracted from MeJA-induced wheat leaves , treated with aniline , contains a new fragment with an apparent size of about 1 , 200 nucleotides ( Fig 6A ) . This new RNA species is likely to be a product of depurination/cleavage from a large , abundant RNA , such as rRNA , because it only appears after in vitro treatment with aniline . At present we do not know its exact identity , but the size of the RNA fragment is consistent with the products of RNA cleavage previously observed in MeJA-induced RIP , such as JIP60 [33] . The area under the peak was significantly reduced in plants infected with B . graminis f . sp . tritici ( Fig 6B ) . Furthermore , we observed no additional peaks in RNAs from leaves infected by B . graminis f . sp . tritici . The expression of a single transgenic RALPH effector was not as potent as actual infection , but resulted in an intermediate effect which was not statistically different from either the uninfected or infected ( azygous ) samples . Moreover , the combination of infection and transgenic expression of CSEP0064/BEC1054 was similarly intermediate . It remains to be seen whether expression of multiple RALPH effectors , observed during infection [34] , would result in an inhibition that is statistically significant . We have observed an increased susceptibility in different plants to filamentous pathogens upon expression of CSEP0064/BEC1054 ( Fig 1 ) . This effect is consistent with the activities displayed by secreted effectors dedicated to modulate or inhibit the immune system of the host [64] . Although the precise activity of CSEP0064/BEC1054 has not yet been determined , the observations made so far suggest an RNA-binding function that counteracts the role of endogenous plant RNases like RIPs . Depurination of a specific nucleotide in the SRL RNA by RIPs [65] is a mechanism in plants to limit the spread of fungal infections: it impairs binding of the eEF2/GTP complex to the ribosome [66] . This , in turn , inhibits protein synthesis and ultimately leads to apoptosis [67 , 68] . Thus , suppressing the function of RIPs could be a prime target for CSEP0064/BEC1054 and other proteins in the large family of RALPHs encoded in the B . graminis genomes . Several lines of experimental evidence are consistent with this hypothesis . Firstly , our sequence and structural analyses show that this protein is a non-catalytic homolog of fungal RNases ( Fig 3C ) . Secondly , our binding data demonstrate that CSEP0064/BEC1054 is capable of recognising host RNA ( Figs 4 and 5A ) . While the interaction measured is weak , we cannot rule out that the binding affinity of CSEP0064/BEC1054 in vivo is enhanced by extended contacts with an RNA motif like the SRL and neighbouring proteins on the ribosomal surface . A similar binding mode has been invoked for ribotoxins and RIPs . For example , structural studies show that the fungal ribotoxin restrictocin can form a stable interaction in solution with the SRL RNA [69] , and docking of the restrictocin-SRL RNA complex into the structure of the ribosome showed proximity of this enzyme to ribosomal proteins L6 and L14 [55] . Thirdly , our data also show that CSEP0064/BEC1054 interacts in planta with PR10 protein ( Fig 2B and 2C ) . At least some PR10 protein variants have been shown to function as RNase [45] CSEP0064/BEC1054 may thus interfere with barley PR10 RNase function . In this work we found that the presence of a single CSEP0064/BEC1054 in planta appears to afford some protection of a major host RNA species from MeJA-induced cleavage ( Fig 6A ) , but this effect is weak and barely significant on its own . It may be that the expression of several RALPHs are needed to obtain a clear and significant impact . The data is consistent with a model in which CSEP0064/BEC1054 competes with a RIP like JIP60 for an RNA-binding site whose integrity is critical for ribosomal function . This would then meet the need of an obligate biotrophic pathogen , such as the powdery mildew fungus , to deal with the consequences of MeJA induction at early stages of infection [70] . Given the predicted structural similarity of CSEP0064/BEC1054 with more than 100 paralogs in the B . graminis f . sp . hordei CSEP repository [4] , it can be expected that additional effector candidates of this pathogen play a similar role . The predicted functional redundancy of these CSEPs might be explained by buffering against the loss of individual effector genes in the highly plastic fungal genome and/or the potential sequential delivery of effector variants during pathogenesis to escape detection by the plant immune system [71] . We hypothesise that CSEP0064/BEC1054 could protect rRNA from the activity of plant RIPs ( Fig 7 ) . Preventing the degradation of rRNA would help to preserve the living cell as a food source for the fungus . This is an essential role for a fungus that is an obligate biotrophic pathogen of plants and goes some way to explain why these candidate effectors are such a prominent component of the CSEP complement in grass powdery mildew fungi .
Barley , H . vulgare L . cv . Golden Promise , and wheat , T . aestivum L . cv . Cerco , were grown in Levington Seed and Modular Compost Plus Sand FS2 ( Everris , Ipswich , UK ) in 13 cm square pots . Plants were kept under long-day conditions , 8 h/16 h hours dark/light cycles , at 21 oC and 33% humidity . Seven days after planting , the barley and wheat seedlings were transferred to 216 dm3 Perspex boxes , and inoculated with B . graminis f . sp . hordei strain DH14 [8] , or B . graminis f . sp . tritici strain “Fielder” ( Donal O’Sullivan , NIAB , UK ) respectively . The Bimolecular Fluorescence Complementation ( BiFC ) experiments were performed with barley cv . “Margret” , but grown in 10 cm square pots filled with “Einheitserde” compost ( Einheitserde , Frondenberg , Germany ) . Transgenic wheat experiments were performed using T . aestivum L . cv . Fielder , which is also susceptible to the strain of B . graminis f . sp . tritici used here . N . benthamiana was grown in Levington Seed and Modular Compost Plus Sand FS2 ( Everris , Ipswich , UK ) , mixed 2:1 with five-millimetre Vermiculite ( 5 mm diameter; Sinclair , Lonconshire , UK ) in 9 cm square pots , with one plant per pot . Plants were grown under long-day conditions , with 8 h darkness and 16 h light , at 25 oC and 33% humidity , and watered twice per week . Four weeks post planting , detached N . benthamiana leaves were placed on wet blue-roll ( VWR , Chicago , USA ) , and inoculated with P . tabacina [72] sporangia suspended in autoclaved demineralised water . Three days post inoculation ( dpi ) of barley seedlings with B . graminis , total RNA was extracted using the using the Qiagen RNeasy Mini Kit ( Qiagen , Crawley , UK ) , and the concentration determined using a NanoDrop-1000 spectrophotometer ( Thermo Scientific , Wilmington , USA ) . Complementary DNA ( cDNA ) synthesis was performed using 3 mg of barley RNA as a template , with the SuperScript Double-Stranded cDNA Synthesis Kit ( Invitrogen , CA , USA ) . Jasmonate Induced Protein 60 cDNA ( Jip60 ) was amplified from this cDNA template , with PCR conditions as previously described [12] . The resulting PCR product was inserted into the entry vector pCR8 ( Invitrogen ) , as described by the manufacturer . The initial amplification of genes coding for barley proteins to test for interaction with CSEP0064/BEC1054 was performed in the same manner as for Jip60 . Re-amplification of the plant genes and fungal genes from the pCR8 vector was performed with Phusion High-Fidelity DNA Polymerase ( Thermo Fisher Scientific , Schwerte , Germany ) . The PCR products were either purified using the DNA Clean & Concentrator TM-5 kit ( Zymo Research , Freiburg Germany ) , or using the QIAquick PCR Purification Kit ( Qiagen ) . The PCR products for pCR8 were treated with restriction enzyme Dpn1 ( New England Biolabs , Herts , UK ) to digest the template plasmid . The remainder were inserted into pDONR201 ( Invitrogen , Carlsbad , CA ) using the Gateway BP Clonase Enzyme Mix ( Invitrogen ) . The resulting entry vectors were transformed into chemically competent TOP10 E . coli ( Invitrogen ) , and the transformed bacteria selected on Lysogeny Broth ( LB ) agar plates containing 100 μg/ml spectinomycin ( pCR8 ) or 50 μg/ml kanamycin ( pDONR201 ) . Single colonies from the resulting transformants were picked and grown overnight in LB media with the appropriate antibiotic . Their plasmid DNAs were then purified and sequenced to confirm insertion , orientation and absence of unwanted mutations . Gateway LR Clonase enzyme mix ( Invitrogen ) was used to recombine the Gateway entry vectors with the Gateway expression plasmids to produce the plasmids for bombardment or for agroinfiltration . The resulting plasmids were transformed into chemically competent TOP10 E . coli , and grown overnight on LB medium supplemented with ampicillin . Single colonies of E . coli were picked , and the respective plasmids sequenced to confirm the insertion , orientation and absence of unwanted mutations . For BiFC assays , powdery mildew effector and barley candidate interactor genes were expressed in the pE-SPYCE/pUC-SPYNE-Gateway BiFC vector system [73 , 74] . All effectors were expressed in pE-SPYCE-Gateway and tagged N-terminally with the C-terminal half of YFP . All barley genes were expressed in pUC-SPYNE and tagged C-terminally with the N-terminal half of YFP . As transformation control , dsRED expressed in pUbi was used . For agroinfiltration , A . tumefaciens ( GV3101 ) was transformed with the plasmids pK7FWG2 , pB7RWG2 , or pK7WGF2 containing GFP-tagged CSEP0064/BEC1054 [75] . Bacteria , containing the plasmids to be used for biolistic transformation of barley primary leaves , were cultured overnight in 200 μl LB media with ampicillin , and purified using the NucleoBond Xtra Midi kit ( Machery-Nagel , Düren , Germany ) . Gold micro-carriers ( Gold powder , spherical , APS 0 . 8–1 . 5 μm , 99 . 96 ( Alfa Aesar , Karlsruhe , Germany ) , were prepared previously described [76] . In summary , they were weighed in 30 mg aliquots , coated with 5 μl of DNA solution at 100 ng/μl ( one μl of DsRED plasmid , 2 μl of bait plasmid and 2 μl of prey plasmid; or for the positive expression controls , 1 μl of DsRED plasmid and 2 μl of expression control plasmid , where all DNA solutions were prepared at 100 ng/μl ) whilst being vortexed . Following this , 20 μl of 0 . 1 M spermidine ( Sigma-Aldrich , Munich , Germany ) and 50 μl of 2 . 5 M CaCl2 were added dropwise whilst still vortexing . The coated micro-carriers were stored in 60 μl of 100% ethanol on ice until use . Primary leaves were harvested from seven-day-old barley leaves , and their adaxial surfaces bombarded with coated micro-carriers using a PDS-1000/He System with a Hepta Adaptor as per the manufacturer’s instructions ( Bio-Rad , Munich , Germany ) . A vacuum of 27 inches of mercury was used with 900 psi rupture disks ( Bio-Rad ) . Following bombardment , barley leaves were maintained on water agar ( 1% supplemented with 85 μM benzimidazole . Three days after bombardment , the leaves were coated with perfluorodecalin ( 95% , Sigma-Aldrich ) for a minimum of 30 min before imaging . Leaf samples were analysed using a Leica TCS SP8-X laser-scanning microscope ( Leica , Wetzlar , Germany ) , mounted with a 20x 0 . 75 numerical aperture water-immersion objective . Transformed cells were identified through observation of DsRED fluorescence , which was excited at 561 nm using the 560 nm diode laser . Fluorescence emission was detected between 600 and 640 nm using a photomultiplier tube detector . mYFP fluorescence was excited at 514 nm with an argon laser at 20% base power and 5% laser power for imaging . Fluorescence emission was detected between 520 and 540 nm using a HYD detector with 250 V gain for all images . Sequential imaging was conducted by 400 Hz scanning speed and 5x frame averaging . Image capture and analysis was performed using Leica SP8 software . Further editing was performed using FIJI software v2 . 0 [77] . For semiquantitative assessment of BiFC signals , transformed cells were manually assigned to one of four categories: strong , medium weak or no detectable YFP fluorescence . Based on 5–8 exemplary cells per category , the following scores were assigned: strong fluorescence ( ~45% positive pixels per cell– 9 ) , medium fluorescence ( ~17% positive pixels per cell– 3 ) , weak fluorescence ( ~5% positive pixels per cell– 1 ) and no fluorescence ( ~2% positive pixels per cell– 0 ) . Scoring was done in a blinded manner ( identity of samples hidden ) . A . tumefaciens ( GV3101 ) was transformed with the plasmids by electroporation using a MicroPulser Electroporator as per the electroporator manual ( BioRad ) , followed by recovery for two hours in LB media ( 1 ml ) , shaking at 28 oC for 2 h . The transformed Agrobacterium was subsequently plated onto LB media containing 100 μg of spectinomycin for the plasmids pK7FWG2 , pB7RWG2 , or pK7WGF2; or 50 μg/ml ampicillin for the colocalisation vectors . Transformed colonies were grown for two days , a single Agrobacterium colony selected , the presence of the insert checked by PCR , and the colony streaked onto a fresh plate . Following a further two days of growth , the colonies were resuspended in 2 ml of MMA buffer ( 10 μM MgCl2 and 10 μM MES ( 2-[N-morpholino] ethanesulfonic acid , pH 5 . 7 ) . The bacteria were centrifuged ( 5 min , 8000 g ) , and resuspended in 2 ml MMA buffer ( 10 mM ) . The OD600 was measured , and a bacterial suspension created with a final OD600 = 0 . 5 for RFP constructs , or OD600 = 0 . 2 for GFP constructs . Three to four weeks old N . benthamiana plants were selected for agroinfiltration at OD600 = 0 . 5 . The Agrobacterium suspensions were infiltrated into the abaxial surface of leaves ( leaves three and/or four from the apex ) . The leaves were harvested from the plant two to four days after infiltration , and maintained on damp absorbent paper in clear plastic boxes , under long day conditions ( 16 h/8 h light/dark photoperiod at 18 oC ) . Infiltrated leaves were mounted in water , and analysed using a Leica SP5 resonant inverted confocal microscope . Excitation and emission wavelengths were 543 nm and 588 nm , respectively , for RFP , 488 nm and 680 nm for plastid autofluorescence , and 488 nm and 495 nm for GFP . RFP was excited with an argon laser , and autofluorescence and GFP were excited using a helium-neon laser . Image analysis and processing were performed using Leica LAS X ( Leica Microsystems , Milton Keynes , UK ) and Fiji software ( ImageJ ) . Agrobacteria containing the plasmid pK7FWG2/BEC1054 were infiltrated into one half of an N . benthamiana leaf , and Agrobacteria with the GFP-only construct into the other . Both Agrobacterium strains were infiltrated at an OD600 = 0 . 5 . P . tabacina [72] was used to inoculate the leaves within two hours of agroinfiltration . At ten days after inoculation , inoculated leaves were shaken in water ( 5 ml ) and the number of sporangia harvested were measured through counting with a haemocytometer . Wobble CSEP0064/BEC1054 ( wbec1054 ) is a synthetic gene corresponding to B . graminis f . sp . hordei CSEP0064/BEC1054 , but lacking the N-terminal signal peptide and containing silent “wobble” mutations , minimising the sequence identity of the gene with the wild-type Blumeria gene; at the same time , the codon usage was optimised for expression in wheat and barley [11] . The wbec1054 gene was cloned into pENTRY ( Invitrogen ) between the attL1 and attL2 sites , and then recombined into the vector pActR1R2-SCV through LR recombination ( Invitrogen ) . such that the wbec1054 gene would be expressed from the actin promoter in vivo [78] . The resulting constructs were transformed into electrocompetent Agrobacterum strain AgI1 [79] . The wbec1054 gene was transformed into T . aestivum cv . Fielder through Agrobacterium-mediated transformation . Immature seeds were collected at 16–20 days post anthesis , and surface-sterilised [80] . Isolated embryos were co-cultivated with Agrobacterium at 23°C for two days in the dark [81] . The embryonic axes were removed , and the subsequent tissue culture performed as described previously [80] . The copy number of the nptII selectable marker gene was analysed via quantitative real-time PCR ( qPCR ) using the ΔΔCt method [82] . Plants were grown to maturity , and the T1 generation seeds harvested for further analysis . Seed dormancy was interrupted by incubation for five days at 32 oC ( day ) , followed by 4 oC for one night . Transgenic wheat seeds were sown in 2x2 cm propagation tray chambers , under the growth conditions listed above for wheat and barley , but with the addition of 2 g/l Osmocote Patterned Release Fertiliser ( Everris , Ipswich , UK ) . Seedlings were transferred after two weeks to square plastic pots ( 9 cm ) containing the same potting mixture . Transgenic wheat DNA ( generations T1 to T3 ) was extracted using the KAPA3G Plant direct PCR protocol ( Kapa Biosystems , ROCHE ) . Transgene presence and copy number was determined for the generations T1 to T4 by qPCR . Analysis was performed through the ΔΔCt method ( ΔΔCt = ΔCt ( control gene ) −ΔCt ( gene of interest ) ) , with wheat β-tubulin ( tubb6; U76897 . 1 ) as the control , and wbec1054 as the gene of interest . An expression of 0 . 00 relative to tubb6 corresponded to homozygous null plants , referred to hereafter as azygous ( -/- ) , 0 . 2–0 . 5 as heterozygous plants ( +/- ) , and 0 . 5–1 as homozygous plants ( +/+ ) . The phenotypic characteristics of mature wheat plants from the T4 generation of lines homozygous ( +/+ ) or azygous ( -/- ) for wbec1054 were investigated . Eleven characteristics were assayed: leaf number , maximum height , peduncle ( internode 1 ) and other internode lengths , ear length , subcrown length , fertile tiller number , tiller mass and grain number . Statistical analyses were performed for all characteristics except the subcrown , as the majority of the tiller subcrowns became detached during the drying out phase . The subcrown belonging to the primary tiller could not therefore always be accurately determined . Primary leaves were harvested from transgenic wheat plants from the T4 and T5 generations of plants homozygous or azygous for wbec1054 , and segments ( 2 cm each ) taken from the base , middle and tip using a flat blade . The “mature” leaves correspond to leaf four on eleven-week-old plants , “young” leaves were the primary leaves from three week-old plants . The leaf segments were placed on wet blue-roll paper , and inoculated with B . graminis f . sp . tritici ( isolate “Fielder” ) on the adaxial leaf surface . One hour post inoculation , leaf segments were transferred onto water agar ( 0 . 5% agar supplemented with 16 mg/l benzimidazole ) with the infected adaxial side up . Plates were maintained for three days under the growing conditions described above ( Plant and filamentous pathogen growth conditions ) . Staining of infected leaf segments was performed using 0 . 1% trypan blue dye in ethanolic lactophenol ( 1:3 . 35 ) ( RAL Diagnostics , Martillac , France ) for two hours at 80 oC . Destaining was then performed using chloral hydrate ( 2 mg/ml ) for 2 h . A Carl Zeiss Axioskop 2 plus microscope ( Zeiss , Cambridge , UK ) was used to view fungal structures . The proportion of germinated conidia that formed at least one haustorium ( propH ) was calculated ( where propH = ( haustorial forming germinated conidia/total number of germinated conidia ) ) . Colonies that formed epiphytic hyphae were used as a proxy for the presence of at least one functional haustorium . Total RNA was extracted using the Qiagen RNeasy Mini Kit ( Qiagen ) . RNA was quantified with a NanoDrop-1000 spectrophotometer ( Thermo Scientific ) . Aniline ( 1 . 2 μl , 1 M ) ( ≥99 . 5% , Sigma ) was used to treat 10 μl , containing up to 1 μg of extracted RNA in RNase free water , and incubated in the dark at 60 oC for three minutes , following which 2 μl of 5 M ammonium acetate stop solution with 100 mM EDTA was added and the mixture transferred on ice . The RNA was then precipitated by adding ethanol ( 1 ml ) , incubated at -80 oC for 20 min , and then collected by centrifugation . The quantity of RNA recovered was measured ( NanoDrop 1000 ) and then analysed using an Bioanalyzer RNA Nano 6000 kit ( Bioanalyzer 2100 , Agilent Technologies , Santa Clara , CA ) . The peaks of interest were indentified manually , and the areas under the peaks measured using the manucfacturer’s software ( Agilent Technologies 2100 Expert , 2009 ) . The order of the cytoplasmic and chloroplastic rRNA peaks was obtained from the manufacturer’s guide book ( http://citeseerx . ist . psu . edu/viewdoc/download ? doi=10 . 1 . 1 . 493 . 5004&rep=rep1&type=pdf ) . The sizes of the small and large peaks were obtained from the following PDB models DOI: 10 . 2210/pdb4v7e/pdb and DOI: 10 . 2210/pdb5mmj/pdb . A gene fusion coding for thioredoxin , a hexahistidine tag , a TEV digestion site and the mature form of CSEP0064/BEC1054 ( Uniprot N1JJ94 , residues 22–118 ) was expressed in the pNIC-Trx plasmid ( kindly provided by Oxford SGC ) using the Shuffle T7 Express ( NEB ) E . coli strain . Cell pellets were resuspended in 50 mM Tris , 300 mM NaCl , pH 8 . 0 ( buffer A ) , and lysed at 25 kpsi using a cell disruptor ( Constant Systems Ltd , Warwickshire , UK ) . Post clarification , supernatants were loaded onto Ni-NTA resin ( Qiagen ) , and washed with buffer A containing 10 mM imidazole prior to elution with buffer A containing 300 mM imidazole . After dialysis in buffer A , CSEP0064/BEC1054 was digested with TEV protease in the same buffer and passed down Ni-NTA resin as described previously to remove thioredoxin and the hexahisitidine tag , prior to size exclusion chromatography on an S75 16/60 column ( GE Healthcare , Buckinghamshire , UK ) in 20 mM phosphate pH 7 . 0 , 150 mM NaCl ( buffer B ) . Purified CSEP0064/BEC1054 was dialysed into crystallisation buffer ( 10 mM Tris , 150 mM NaCl , pH 7 . 0 ) and concentrated to 10 mg/ml for crystallisation . A variety of commercially available solution conditions for crystallisation ( Hampton Research , CA , USA ) were screened using the sitting-drop vapour diffusion method . CSEP0064/BEC1054 was combined with the mother liquor on a 1:1 ratio in 200 nl drops . Crystals obtained in 0 . 1 M sodium acetate buffer pH 5 . 0 , supplemented with 30% PEG 4000 , 0 . 4 M ( NH4 ) 2SO4 were cryoprotected with 30% glycerol and flash frozen for data collection . A native dataset to 1 . 3 Å resolution was collected at a wavelength of 0 . 92 Å using the I04 beamline ( DIAMOND Light Source , Oxford , UK ) and a second derivative dataset collected at a wavelength of 0 . 95 Å ( above the iodide f” edge ) from crystals that had been soaked in mother liquor supplemented with 0 . 5 M sodium iodide . Initial processing , scaling and structure factor calculation of native and iodide SAD datasets was performed using XDS [83 , 84] and TRUNCATE [85] respectively , from within the XIA2 program [86] . Phasing of the derivative dataset was performed via single-wavelength anomalous diffraction with density modification , using autoSHARP software ( Global Phasing Ltd . , Cambridgeshire , UK ) [87] . Following calculation of protein phases , a partial model was built automatically in ARP/wARP [88] . The model was extended through manual model-building in Coot [89] . The refined CSEP0064/BEC1054 structure was thus used as a search model to phase the higher resolution native dataset via molecular replacement using Phaser MR [90] . Iterative cycles of model building and reciprocal space refinement were performed in Coot [89] and Refmac5 [91] , respectively , until convergence of Rwork values , where 5% of reflections were excluded for cross-validation . Both models obtained from SAD and native datasets contained a single copy of CSEP0064/BEC1054 in the asymmetric unit , and all residues were built with the exception of the N-terminal alanine , the side chain of R76 and the C-terminal G97 , due to missing or ambiguous density in maps . Model validation was performed using tools in Molprobity ( Chen , Arendell et al . , 2010 ) . CSEP0064/BEC1054 was concentrated to 300 μM in buffer B for NMR experiments , where 10% 2H2O v/v was added to provide a deuterium lock signal . 2D 1H15N HSQC spectra ( Kay et al . , 1992 , 1994 ) were recorded at 308 K on a Bruker 600 MHz AvanceIII spectrometer equipped with a TCI cryoprobe ( Cross Faculty Centre for NMR , Imperial College London , UK ) . The chemical shifts of the Cα , Cβ , HN , and CO atoms of the 13C , 15N labelled CSEP0064/BEC1054 protein were obtained from HNCACB/CBCA ( CO ) NH and HNCO/HN ( CA ) CO experiments using standard methods . Data were processed using NMR-Pipe [92] , and analysed in CCPN Analysis [93] or using an in-house version of NMRView ( One Moon Scientific ) [94] . For MST experiments , the recombinant CSEP0064/BEC1054 protein was labelled using the Monolith NT . 115 protein labelling kit ( NanoTemper technologies , Munich Germany ) using red fluorescent dye NT-647 NHS ( amine-reactive ) according to the manufacturer’s instructions . Assays were performed using a Monolith NT . 115 MST machine ( Nanotemper Technologies ) , where LED power was kept at 20% and MST power at 40% . Assays were performed in standard or hydrophilic capillaries in 20 mM Tris buffer pH 7 . 4 , 150 mM NaCl , 0 . 05% Tween , where RNA was titrated against labelled CSEP0064/BEC1054 kept at a concentration of 50 nM . CSEP0064/BEC1054 was titrated with total RNA ( extracted from barley ) from a starting concentration of 10 μg/ul , or in vitro transcribed SRL RNA ( 5´-ACCUGCUCAGUACGAGAGGAACCGCAGGU-3´ ) or bacteriophage T7 promoter sequences ( 5´-AATTTAATACGACTCACTATAGG-3´ ) from a starting concentration of 1 mM . Curve fitting was performed using the NTAnalysis software ( Nanotemper Technologies ) in the Thermophoresis + T-Jump mode for the SRL ligand , and using the Hill equation for the total RNA . KD values calculated using non-linear regression . For NMR experiments , CSEP0064/BEC1054 protein at a concentration of 50 μM was titrated with from 0 . 5 to up to 80 M equivalents of a DNA SRL oligonucleotide sequence , where 2D 1H15N HSQC spectra were recorded for each titration point as outlined . Experiments were performed in 50 mM Tris , 150 mM NaCl , at 308 K . Analysis of chemical shift perturbations was performed in CCPN Analysis [93] . Bartlett tests were performed for numerical datasets to determine whether the variance was homogeneous [95] . General Linear Models ( GLMs ) were conducted on all datasets except for the infection of transgenic wheat with B . graminis f . sp . tritici , where a Generalised Linear Mixed Model ( GLMM ) was utilised . Where possible , model simplification was performed , and non-significant or non-interacting factors removed , resulting in the minimal model . For the GLMs , linear hypotheses were tested in a pairwise manner using Games-Howell post-hoc tests . The only assay in which a two-way interaction of factors was detected was the RNA extraction and analysis ( described above ) . A GLMM was used , as non-normal repeated measures ( i . e . the fact that the old and young leaves were from the same plants , and the different leaf sections used originated from the same leaves ) , can be accounted for through the addition of random effects [95] . For the GLMM , the count data corresponding to the total number of germinated conidia ( with and without functional haustoria ) was bound as a single vector , creating the response variable “y” . To take into account pseudoreplication ( due to sampling repeatedly from the same plants/leaves ) , age , genotype and leaf segment were set as fixed effects , and a binomial family was used due to the response variable being count data . The linear hypotheses were investigated in a pairwise manner using the “multcomp” package in R . For the phenotyping assay , a “Poisson” family structure was used to account for count data , and datasets were logged to account for overdispersion in the original models ( where the residual deviance is much greater than the degrees of freedom ) . Sequence data from this article can be found in the EMBL/GenBank data libraries under the following accession number ( s ) : 40S 16 , accession KP293844; eEF1γ , accession KP293852; eEF1α ( 1 ) and eEF1α ( 3 ) , accessions KP293845 and KP293846; GST , accession KP293847; MDH , accession KP293848; NDPK , accession KP293849; PR10 , accession KP293851; CSEP0064/BEC1054 , accession CCU83233 . 1; CSEP0264 , accession CCU83219 . 1; and CSEP0102 , accession CCU74258 . 1 . The structure of CSEP0064/BEC1054 is available in protein data bank ( PDB ) under accession number 6FMB . | Powdery mildews are common plant diseases which affect important crop plants including cereals such as wheat and barley . The fungi that cause this disease are obligate biotrophs: they have an absolute requirement for living host cells which they penetrate with feeding structures called haustoria . These fungi must be highly effective at avoiding immune recognition which would lead to death of the host cell and the pathogen . We assume they do this by delivering effector proteins to the host . While several hundred secreted effectors have been described in cereal powdery mildews , it is unknown how they work . Here , we use X-ray crystallography and nuclear magnetic resonance ( NMR ) spectroscopy to determine the structure and interactions of the effector CSEP0064/BEC1054 , representative of the largest class of effectors resembling fungal RNases . We find that this effector binds nucleic acids . Expression of the effector in plants increases susceptibility to infection . Moreover , transgenic CSEP0064/BEC1054 expression in wheat inhibits the degradation of host ribosomal RNA induced by ribosome-inactivating proteins ( RIPs ) . We propose a novel mechanism of action for the RNase-like effectors in powdery mildews: they may act as pseudoenzymes to inhibit the host RIPs , known components of plant immune responses that lead to host cell death . | [
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... | 2019 | The fungal ribonuclease-like effector protein CSEP0064/BEC1054 represses plant immunity and interferes with degradation of host ribosomal RNA |
Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics . The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology . More recently , causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals , such as extracellularly recorded spiking activity . Usually , these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known . Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis . In this work , we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold . We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data . We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions , which affect both intrinsic neural dynamics and synaptic transmission . Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known . The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations . In general we show that advanced statistical models allow for the characterization of effective network structure , deciphering underlying network dynamics and estimating information-processing capabilities .
Nervous systems show highly complex dynamics . This complexity originates from the intrinsic dynamics of each neuron , from its synaptic connections , and modulation state [1]–[3] . Unfortunately , information about synaptic relationships is generally sparse or often completely missing ( see , e . g . [4]–[6] , and references therein ) . Moreover , the inference of effective connectivity is based on limited information , such as the timing of spikes emitted by a subset of all neurons in the network . Here , effective connectivity is considered to be the network of directed , causal effects of one neural element over another ( as opposed to structural or functional connectivity , see [7] ) . We can use spike trains to estimate effective connectivity networks , but how these effective networks relate to actual connectivity remains an open question [8]–[10] . There are many ways to build effective networks based on observed spiking activity . A commonly used network inference algorithm is Granger causality analysis [11] , [12] . The strength of a causal link between two network nodes is measured by how well the knowledge of past activity of one node helps to predict the activity of the other node . Granger causality analysis has been applied to a variety of different imaging data at different spatial scales of brain activity [13]–[19] , including spiking activity [20]–[23] . However , an inherent difficulty exists in validating these inference techniques because the underlying , true synaptic connectivity is typically not known . Usually , connectivity inference algorithms are validated on simulated data sets [15] , [20] , [24]–[28] , and it remains largely unknown how well their predictions match the underlying structural connectivity . In a recent study , Kispersky et al . [29] applied a linear Granger causality analysis to spiking activity from a physiological preparation , whose circuitry is well studied and understood [30] , [31] . The analysis suggested an effective connectivity pattern of a three-node circuit that did not match the known physiological connectivity . The authors attributed this result to the presence of strong oscillatory components of the spiking activity and the inability of the analysis to capture the intrinsic pacemaker rhythm . In this paper , we will continue the analysis of the spike train data with the goal of inferring network consistent with known connectivity . Generalized linear models take into account the point process nature of spike trains and have been used to infer connectivity in other biological neuronal networks [9] , [23] , [32]–[34] . Here , we will show for the first time that this approach , based on spike train data only , can identify relative connection strengths that match the known physiology of the pyloric circuit of the stomatogastric ganglion ( STG ) of the crab even though synaptic transmission in the pyloric circuit is graded and only partly mediated by spikes . If a threshold is applied on the estimated connection strengths , the physiological connectome of the circuit can be correctly reconstructed from the model . To obtain this result , it is important to consider the functional shape and magnitude of the interactions in the model rather than statistical significance as it is classically quantified by Granger causality analysis . In the second part of the study , we show that both a nonlinear point process model and our measure of coupling strength are necessary to successfully infer the connectivity . Finally , we show that inference using the point process model is robust to parameter changes , can be reproduced across several independent biological data sets , and can be used to predict how altered connectivity affects network function , i . e . , the generation of the triphasic burst pattern . We demonstrate the ability of the method to track changes in the effective network connectivity structure caused by partial blocking of individual membrane currents or synaptic transmission . Our results add to the evidence in favor of applying point process statistical models to capture the statistics of spike trains . They constitute the first step toward the analysis of the relationship between structure and activity of larger neural circuits .
Extracellular recordings were obtained from three units of the crab stomatogastric ganglion ( STG ) , which produce the pyloric rhythm [31] . Spike train activity follows a triphasic pattern starting with bursts of the anterior burster/pyloric dilator neurons ( AB/PD , abbreviated as PD in the following ) , followed by sequential activation of the lateral pyloric neuron ( LP ) and pyloric neurons ( PY ) ( Figure 1A , left ) . Neurons fired stereotypical bursts with a similar number of spikes within each burst over the whole recording session ( mean Fano factor , calculated as the variance over the mean of the distribution of spike counts per burst , averaged over the three neurons ) . The physiological connections between the three units of the stomatogastric nervous system responsible for the pyloric rhythm are well understood [31] ( Figure 1A , right ) . Notably , all synaptic connections are inhibitory , and there is no direct synaptic coupling from the PY neuron to the PD unit . Synapses can be qualitatively classified as weak and strong [35]–[38] ( Figure 1A , right , strength indicated by line width ) . A central question is: Given the spike trains , can we infer the connectivity of the circuit ? Kispersky et al . demonstrated that in the presence of the strong oscillatory components , Granger causality analysis based on a linear firing rate model is unable to deduce the physiological connectivity pattern [29] . Instead , it identifies three strong interactions following the sequential activation of the PD , LP , and PY neurons ( Figure 1B , upper left ) . Our results show that two modifications to the approach of [29] permit accurate inference of the physiological circuit . First , the linear rate model is replaced by a nonlinear point process model that takes into account the structure of the data . Second , rather than basing the strength of the coupling on a statistical significance criterion as in Granger causality analysis [23] , we propose to measure coupling strength directly as the magnitude of the estimated , directed coupling between two spike trains . With these two modifications , a statistical fit to the data can approximately recover the structure of the synaptic circuitry between the three units ( Figure 1B , lower right; note that the missing physiological connection possesses the weakest coupling strength ) . Any other possible combination of model and coupling measure leads to inaccurate reconstructions ( Figure 1B ) . In point process models , the spiking activity of a neuron is conditionally explained by the previous firing activity of the neuron and activity of the recorded population ( see Figure 2A for an illustration ) . Each neuron's previous spiking contributes to its predicted activity through self-coupling filters and the firing of other neurons in the population contribute with ( possibly distinct ) cross-coupling filters . All contributions are linearly summed and transformed into an instantaneous firing probability via a sigmoidal , nonlinear transfer function . We define coupling strength here as the net area under the ( directed ) cross-coupling filter . This implies that a strong coupling could be obtained either by a consistent influence of one neuron to the target neuron over an extended period of time or via a strong , but timely interaction . We fit such point process models to an extended recording of spontaneous activity of the pyloric circuit and obtained highly significant values for all possible cross-interactions . Hence , judging network structure only from the statistical significance of the model parameters did not reveal relative coupling strengths ( see below for a more detailed analysis using the Granger causality approach ) . The coupling filters used by our model can be interpreted as synaptic-like interaction filters ( Figure 2B ) . Here , negative ( positive ) values indicate an effective inhibitory ( excitatory ) effect on the spiking probability at the specified delay . Self-coupling filters ( Figure 2B , panels along the diagonal ) show three features: an initial refractory period , a rapid transition to a positive peak due to the natural bursting activity of the spike trains , followed by an extended effective inhibition . The magnitude and time scales of these features can be mapped to biological findings . For example , the positive peaks at lags between 20 ms ( LP ) and 50–60 ms ( PD and PY ) directly correspond to the typical inter-spike intervals within the bursts . The spike-triggered depolarization of the membrane potential is on the same time scale and can be measured with intracellular recordings [35] . The following spike rate adaptation is on the order of 200 to 400 ms and longer ( Figure 2B , panels along the diagonal ) , consistent with reported ( short ) adaptation time scales of 200–300 ms [35] , [36] . Hartline also reported adaptation on longer timescales ( 3–4 s ) which is consistent with the shape of the self-coupling filters in models for which longer time lags are considered . The six cross-history kernels ( off-diagonal panels ) can be separated into two groups: couplings in the direction of the firing order during the pyloric rhythm ( PY-to-PD , PD-to-LP , and LP-to-PY ) and couplings counter to the order of a pyloric cycle ( LP-to-PD , PY-to-LP , and PD-to-PY ) ( Figure 2B ) . The first group has weak to moderate inhibitory coupling , the second group is inferred as strongly inhibitory over the whole range of examined time lags because no spikes are observed in the target neurons during the time lags . The net interaction type of all inferred cross-couplings is inhibitory , in accordance with known synaptic properties of these neurons [31] . Notably , the only connection not present in the biological circuit ( PY-to-PD ) , is the weakest one inferred by the point process model . Therefore , by applying a threshold based on a priori knowledge of the approximate expected density of the network ( i . e . , based on the expected number or strength of synaptic interactions ) , a connectivity diagram can be obtained , matching the known circuit connectivity ( Figure 1B , lower right ) . Not only is the physiologically absent connection the weakest in the model estimate , the relative strengths of the other couplings qualitatively match the known physiology: Experimental studies of directly measured IPSPs between all coupled pairs have revealed a qualitative distinction of synaptic strengths between “weak” and “strong” synapses . For the specific three-neuron circuit ( PD , LP , and PY ) considered here , the LP-to-PY coupling is considered weak , while all other connections are considered strong [35]–[38] . This is in agreement with our results ( Figure 1B , lower right , and Figure 2B ) . For the time scales of some interactions ( LP-to-PD , PY-to-LP , and PD-to-PY ) we can only extract lower bounds based on the model fit , but the order of magnitude matches with what is known from physiology for these specific connections ( time scales of 80 ms [35] and longer [37] ) . The shape of the inferred couplings from the PD to the LP unit shows a time scale of approximately 50 ms , consistent with reported values ( 70 ms [35] , [38] ) . The time scale of the LP-to-PY connection is with approximately 20 ms in close agreement with experimental findings ( 20–40 ms [35] , [36] ) . To characterize how well our model fits the observed spiking pattern , we used the model to generate simulated spiking activity following a period of observed spikes . We find that stochastic simulations from the model generally produce a spiking pattern qualitatively similar to the pyloric rhythm observed in the real data set ( Figure 2C ) . In spite of the involved stochasticity in simulating novel spiking activity from the model , the rhythm is accurately maintained for arbitrary periods of time ( Figure 2C , the pyloric rhythm was maintained for at least 500 s in 9 out of 10 stochastic simulations ) . The mean burst Fano factor of the stochastic sample is and much smaller than 1 , consistent with the statistics of the real spike trains . Although the model assigns a nonzero value to the PY-to-PD coupling , it is not essential to produce or maintain the pyloric rhythm expressed by the model circuit . To demonstrate this , we set that particular cross-coupling filter to zero in the maximum-likelihood fit and left all other parameters unchanged . When we used this modified model to simulate new spike trains , it displayed a triphasic rhythm ( Figure 2D ) qualitatively similar to the one obtained using the full model ( Figure 2C ) or even the recorded activity ( Figure 1A , left ) . Thus we conclude that the estimated PY-to-PD coupling is negligibly weak so that we can correctly predict it to be missing from the biological circuit . First , we show robustness to the amount of data used for fitting . Specifically , we fit a sequence of models with increasing amounts of data used to train the model and observed the evolution of coupling strengths over time ( Figure 2E ) . We found that the particular connection ( PY-to-PD ) , which is absent biologically , consistently possesses the weakest coupling strength among all six inferred edges . In general , all estimates of coupling strengths remain relatively robust with regard to the length of data analyzed . Specifically , the difference between the mean coupling strength calculated using half of the data compared to using the full data is not significantly different from zero ( paired t-test , , ) . Convergent coupling strengths can be obtained from 30 s or more of spiking data . Model parameters were fitted using standard maximum-likelihood techniques . Prior to fitting , explanatory variables that perfectly predicted the absence of spikes were removed together with the corresponding data bins . Their maximum-likelihood coefficients diverge to minus infinity , so we set them to . This ensured the resulting probability of spiking to be practically zero . Relative coupling strengths remain unchanged for all sensible values of the cut-off parameter ( Figure 2F ) . Therefore , our results are robust to changes in the value of . It is known that the maximal time period to consider history effects can have a profound effect on the inferred networks , for both linear and point process models . For the point process model considered here , the maximal time lags for the self- and cross-coupling filters were not chosen arbitrarily , but based on a model selection procedure that selected an optimal time scale based on a penalized likelihood criterion ( Figures S1A and B ) . To investigate whether the difference between the weakest ( PY-to-PD ) and the remaining connections was significant , we computed the uncertainties associated with the coupling strengths based on the maximum-likelihood estimate of the model and its covariance structure ( see Text S1 ) . The standard deviations show that the PY-to-PD connection is significantly weaker than any other connection ( effect size in standardized units; one-sided z-test for the difference between the weakest and second-weakest connection , , ; Figure S1C ) . We also performed a goodness-of-fit test tailored to the point process model based on the multivariate time-rescaling theorem [39] . While the individual fit to the PD neuron is formally rejected at a significance level of 5% , overall goodness-of-fit indicates a reasonable model fit . Furthermore , goodness-of-fit tests performed on the joint spike train of all three units do not suggest a major model misspecification ( see Figure S2 and Text S1 for details ) . Passing all multivariate tests increases our confidence that the dependency structure of the network is being correctly inferred . Finally , we repeated the model selection and fitting procedure for three additional independent preparations from different animals , each with spike train recordings of variable length . All recordings qualitatively showed a stable pyloric rhythm , although the temporal scales , like the burst cycle period and the exact temporal phase relationships between units , varied considerably across data sets . For all four data sets , we found qualitatively similar results regarding the inferred connection strengths ( Figure 2G ) . Notably , for all network patterns , the biologically nonexistent connection is inferred to be the weakest compared with all possible connections . Furthermore , relative connection strengths are comparable across all four data sets and filter shapes showed similar qualitative features ( not shown ) . This finding indicates an additional robustness of the presented analysis approach , namely that the same network pattern can be observed in independent preparations . Kim et al . and others used a measure based on Granger causality to quantify the effective coupling between spike trains [22] , [23] . The Granger causality score quantifies changes in model likelihoods that reflect statistical significance of couplings rather than a functional interpretation . The Granger causality ( GC ) score for a directed connection between neuron X and Y is derived by comparing the relative predictions of two nested models: If we improve the accuracy of prediction of a model that only uses Y's and other neurons' histories by additionally incorporating the activity of neuron X , the GC score will be significantly different from zero . Granger causality scores are always non-negative and do not distinguish between excitatory and inhibitory couplings . We used this Granger causality measure using the same point process model as above and parameters determined by the model selection procedure and failed to obtain couplings consistent with known physiology ( Figure 1B , lower left ) . Neither by varying the length of data used for fitting ( Figure 3A ) nor by varying the maximal time lag of cross-coupling filters ( Figure 3B ) were we able to yield a network pattern compatible with the known physiology . This conclusion holds true for all four data sets ( Figure 3C ) . In general , we find no significant correlation between the Granger causality scores and coupling strength ( CS ) defined as the net area under the interaction filters ( Figure 3D ) . One might wonder whether a linear rate model as ( implicitly ) used in [29] combined with our definition of coupling strength might recover the known network architecture . To this end , we constructed a multivariate linear firing rate model as in Kispersky et al . [29] ( see Materials and Methods and Figure 4A for an overview ) . The analysis yielded nine couplings ( self-couplings included ) between the three neurons ( Figure 1B , upper right ) . All self- and between-neuron couplings had highly statistically significant coupling strengths ( Figure 4B ) . Visual inspection of the coupling filters offered little insight as to whether a potential coupling could be classified as inhibitory or excitatory , and what relevant time scales of the interaction would be . To test whether the linear model provided a good fit to the data , we used the estimated model to simulate activity after a period of observed activity . If the model were appropriate , we would expect it to produce qualitatively similar spiking activity consistent with the observed data . Instead , we found the linear model is unable to maintain the pyloric rhythm , and activity values start to diverge after only two seconds of simulated activity ( Figure 4C ) . While the linear model qualitatively captures the alternating activation of the three units , it fails to predict any stationary activity . Moreover , the burst-like structure of the spiking activity and the fine temporal relationships between bursts are lost as soon as model output is no longer directly computed from the observed data ( Figure 4C , inset ) . Thus , stochastic sampling from the model produces activity whose statistics are very different from the training data - a general sign of model misspecification . A more detailed goodness-of-fit analysis confirms this suspicion ( see Text S1 and Figure S3 ) and provides evidence that the linear model is insufficient to accurately describe the statistics of the actual recordings . A further exploration of the parameter space , similar to the previous section , shows that no parameter choice , such as the amount of data used and how far the coupling filters extend in time , leads to a network that would be consistent with physiology ( Figures 4D and E ) . Overall , this indicates that the specified coupling in the linear model is not capturing the true dependency structure of the neurons . In addition , we varied the two remaining free parameters of the linear model: , the kernel bandwidth to obtain smooth rate estimates from the spike trains , and f , the sampling frequency of the time series . None of the parameter configurations led to the inference of the physiological network architecture ( results not shown ) . When we analyzed all four data sets , the physiologically nonexistent connection corresponds to the weakest one in only two out of the four cases . In addition , coupling strengths grouped by connection across all four networks did not show a consistent pattern ( Figure 4F , compare to Figure 2G ) . Moreover , coupling strengths derived from the point process model and the linear rate model were uncorrelated ( Figure 4G ) . For completeness , we reproduced the original analysis of [29] that used the linear rate model together with the Granger causality measure ( Figure 1B , upper left ) . The failure to retrieve the physiological connectome is independent of the definition of coupling strength ( see Figure S4 ) . The analysis demonstrates that although Granger causality estimates can be highly parameter-dependent , the physiological network pattern was not among any network patterns identified for any combination of parameters . Therefore , the failure to recover the correct connectivity in this framework was not due to an inappropriate choice of parameters . Instead , it was caused by intrinsic limitations of the analysis for the type of data considered here . In agreement with the conclusions of [29] , the linear rate model is not an appropriate tool to accurately infer the known physiological connectivity of the pyloric network . To this point , we have considered the standard pyloric rhythm in its default configuration . A useful method of network inference should also detect and track changes that occur to the coupling strengths . To this end , we applied the point process model to two data sets where the isolated pyloric circuit is perturbed by pharmacological agents . In the first data set , CsCl was applied to a preparation of the pyloric circuit of the STG . CsCl is known to block an intrinsic current , the h-current ( ) , in all cells [40] . The current is an inward depolarizing current that slowly activates upon hyperpolarization of the membrane potential [41] . The spike train statistics show that blocking the h-current has little qualitative effect on the pyloric rhythm generated by the circuit ( Figure 5A ) . This is in agreement with previous experimental reports [40] , although we observe changes in individual bursting properties: The burst cycle period increased and overall firing rates of the three neurons were reduced , that is , each burst contained on average less spikes than in the control condition . Firing rates were otherwise stationary within the control and CsCl condition . We expect that blocking would affect the coupling filters in our model in two ways: First , in both conditions , the PY-to-PD coupling is physically nonexistent and therefore , its inferred coupling strength should be the weakest among all estimated couplings . Second , all other coupling strengths should increase . This is because is an inward current that counteracts inhibitory ( hyperpolarizing ) synaptic coupling from other neurons . Blockade eliminates the post-inhibitory rebound and reduces the likelihood of spikes being triggered after inhibition . Hence , blocking the cell's intrinsic h-current should effectively amplify incoming inhibitory couplings . The same reasoning would predict a strengthening of all inhibitory components of the self-coupling filters , such as the fast component responsible for the refractory period . Fitting the point process model to the control data set shows a similar pattern as the other four preparations considered so far ( Figure 5B ) . Particularly , the PY-to-PD connection strength is estimated close to zero and is overall the weakest link in the inferred network . After application of CsCl , all physically present coupling strengths increased their magnitude significantly ( mean change in coupling strength for all couplings except the PY-to-PD coupling strength: , Wilcoxon signed rank test , ; Figure 5B and relative changes in Figure 5C ) . This difference should be contrasted to the change in the ( nonexistent ) PY-to-PD coupling whose change between the two conditions is two orders of magnitude smaller and in the opposite direction ( ) . Therefore , although blocking the intrinsic h-current has no immediate effect on physical synaptic transmission in the network , the predicted modulation of coupling strengths is consistent with the observed changes . For the second data set , we considered a pyloric circuit before and after application of picrotoxin ( PTX ) . PTX is known to block inhibitory synaptic transmission in the STG [42] and affects the functional pyloric rhythm ( Figure 5D ) . When PTX is applied , LP and PY units fire nearly tonically and for longer time periods during a pyloric cycle and partly overlap with firing activity of the PD unit . Overall , firing rates were otherwise stationary in the two recordings . In the STG , most synapses of the LP and PY cells are inhibitory and mediated by glutamate [43] . Synapses of the PD cell use cholinergic neurotransmission . However , the PD neurons are electrically coupled to AB cells which in turn project to the LP and PY neurons via glutamate [31] . Assuming the AB neuron's activity matches the observed PD activity and is left intact in the preparations , the coupling filter originating from the PD neuron summarizes the joint synaptic effects from the PD/AB group [30] , [43] . Therefore , all of the five physical cross-couplings are ( partly ) due to glutamatergic neurotransmission and we hypothesize the application of PTX should decrease the coupling strengths for all of these connections . The inferred coupling strength of the nonexistent PY-to-PD link should remain close to zero and unaffected by application of PTX . Indeed , when we fit the point process model to the data sets before and after application of PTX , we find cross-couplings are decreased toward zero , i . e . , become weaker ( Figure 5E ) . Notably , the PY-to-PD link remains the weakest coupling strength in both conditions , as predicted . The decrease in strength of the five physical synaptic interactions is significant ( Wilcoxon signed rank test , ) and its absolute effect size ( ) is two orders of magnitude bigger than for the only nonexistent link ( , Figure 5F ) . To find out which couplings in the network are crucial for the presence of the stable pyloric rhythm , we simulated spike trains from four different models estimated from the PTX condition . The models differed in the constraints placed on the allowed network pattern . As before ( Figures 2C and D ) experimental spike trains of the PTX condition were used for five seconds , afterward spikes were stochastically simulated using either the fully connected model network , a model with the PY-to-PD link forced to zero , a network structure allowing only for non-glutamatergic synapses or a model with no cross-interactions at all ( Figure 5G , from left to right and from top to bottom ) . All models except the uncoupled one produce spike trains comparable to the real data . The model without any cross-interactions shows burst-like and tonic activity but neurons do not fire in a stable relative phase . This demonstrates that the point process model captures the physiological changes induced by PTX , i . e . , the effective network connectivity is reduced to the ( weaker ) PD-to-LP and PD-to-PY links with all other couplings being effectively absent . In a network with only one synaptic connection or in a fully disconnected network , neurons with temporal irregular activity cannot maintain their relative phase relationships ( Figure 5G , bottom right ) . Therefore , the network with two synapses is the minimal circuitry to maintain the pyloric rhythm ( Figure 5G , bottom left ) , consistent with the experimental findings [37] , [38] . Overall , these results illustrate the utility of the point process model in inference of effective connectivity . Bath application of two pharmacological agents alter the expected circuit connectivity by changing either the intrinsic currents of each neuron ( CsCl ) or the synaptic interactions between neurons ( PTX ) . In both cases , the point process method detected the anticipated changes .
The crab stomatogastric nervous system is well suited to study network inference algorithms like the point process model . The circuit consists of a small number of elements whose synaptic interactions are well studied and whose monosynaptic connectivity is established [30] . Furthermore , one can routinely and concurrently record from the important units of the circuit . Despite the small size of this network , the rhythmic activity of the neural elements makes it challenging to infer the correct , causal relationships [29] . Most of our analysis can be readily applied to other small neural circuits , e . g . central pattern generators in the respiratory system in vertebrates [46] , [47] or motor systems in invertebrates [48] , [49] , as well as to recordings of larger populations . When applying effective network analyses like point process models to any circuit , the challenge of assigning action potentials to single neurons ( spike sorting ) arises . Identifying single spiking events and the accuracy of categorizing them as arising from distinct neurons becomes increasingly challenging in recordings of larger neuron populations . Fortunately , in the STG individual pyloric neurons can be recorded on separate nerves making spike sorting trivial . We note that , in general , efficient spike identification is a requirement for the success of any network inference method like the one presented here . Synaptic transmission in the STG occurs as a graded ( analog ) release of neurotransmitters and is thus mediated by sub-threshold depolarizations as well as spikes [50] , [51] . Therefore , spikes are not the major source of transmitter release , but are dominantly used to signal to the muscles over long ranges . It is not evident a priori that a model that treats the time of spikes as the sole input , i . e . , does not have access to the membrane potential , can correctly perform connectivity inference . For the circuits considered here , this did not seem to pose a problem because , at least in the STG , prolonged membrane depolarizations always appear simultaneously with spiking activity . Therefore , spikes are proxy measurements to determine the state of the membrane potential . Furthermore , the time scales of the graded synaptic interactions are similar to the ones observed from spike-triggered transmitter release and the ones estimated in our model [51] , [52] . In other circuits where graded transmission does not correlate with spike times , knowledge of the subthreshold voltage activity of the neurons might be necessary to infer structural circuit information . We note that synaptic transmission in cortical networks is heavily dependent on spike-triggered , chemical transmission , so the proposed method does in principle generalize to these data . A model of the central pattern generator for the pyloric rhythm can be evaluated using at least two criteria: One criterion is how close the model reproduces a given , observed set of spike trains and their statistics , e . g . , the number of spikes per burst and the average inter-burst duration . For understanding the functional behavior of the circuit , a broader criterion can be applied: A model would match the data if it qualitatively reproduces the stable , triphasic burst pattern , regardless of the exact spike train statistics . It is evident that many models will fulfill either one or both criteria with the first criterion being an additional constraint on the second . This explains why deviations from the best-fitting model ( according to the first criterion ) can still generate spike patterns that may be equally functionally valid ( e . g . by enforcing a certain network structure different from the physiological or fully connected case , see Figure 5G ) . Finally , although the pyloric network generates a triphasic motor pattern , these cells are part of a larger circuit , the stomatogastric ganglion of the crab; and the inferred connections are potentially confounded with indirect ( cascade ) synaptic effects or unobserved common input [53]–[55] . In general , there is unlikely to be a confound in the specific case of the pyloric circuit because the three observed units ( PD , LP , and PY ) are sufficient for generating and maintaining a pyloric burst rhythm in vitro [31] . In principle , an effective coupling from the PY to the PD unit could be realized by a polysynaptic pathway through the inferior cardiac ( IC ) neuron [31] . This would render the potentially observed coupling as effectively excitatory . However , we found no evidence for an effectively excitatory PY-to-PD coupling in our analysis , indicating a small magnitude of such second-order effects for this circuit analysis . Furthermore , analysis of recordings that included the activity of the IC neuron showed that inferred couplings were not significantly altered by the rhythmically active IC neuron ( results not shown ) . To elucidate the reasons why Granger causality analysis using a linear model failed to recover the true connectivity in [29] , we applied a series of goodness-of-fit tests to identify model misspecifications . We identified that two major changes are necessary for correct inference: First , the use of a nonlinear point process model instead of a linear rate-based model , and second , an alternative definition of coupling strength based on the net area of the coupling filter instead of a reliance on statistical significance . We will now discuss these two aspects in detail . Analysis with an underlying linear rate model is based on the assumption that neural firing rates are linearly interacting . Even the inclusion of very long time scales in the linear model did not lead to a correct inference using any of the proposed connectivity measures . This observation points to a general limitation of the simple linear autoregressive models . Further , the physical mechanism for the LP and PY neurons to initiate spiking is a release from inhibition [56] . This mechanism cannot be sufficiently captured by a linear model because a strong inhibition would predict negative firing rates and thereby increase the mean-squared error of the predicted activity - the criterion that linear models try to minimize . The biophysical mechanisms that govern the rhythm are highly non-linear , too . By contrast , the nonlinearity in the point process model has more flexibility in modeling inhibitory interactions ( including modulated release from inhibition , e . g . , via application of CsCl ) . Both the linear and nonlinear models are multivariate , i . e . , they condition the directed couplings based on all other observed network activity . A firing rate model includes a smoothing preprocessing step on the input spike trains . When applied to data from the STG this preprocessing preserves the qualitative phase relationship between the neurons during the pyloric rhythm , but temporal information about the spike timings is lost . In a system that relies heavily on graded synaptic transmission , like the STG [50] , [51] , this may not result in a loss of information . However , in networks where spikes causally affect the postsynaptic membrane potential , we expect that fine temporal relationships between spikes and postsynaptic activity ( or absence thereof ) are predictive of synaptic coupling . Here , we circumvented the smoothing step by proposing a point process model that explicitly models neural data as a sequence of events in time . We have observed that the amount of available data strongly influenced the statistical significance of a directed coupling . Typically , for our data sets , 15 seconds of data or more are enough to yield highly significant Granger causality ( GC ) scores for all six connections , for both the linear and point process models . Hence , statistical significance alone is not useful in this case to determine the presence of effective coupling between two neurons . Because the magnitude of the GC score varies as well with the amount of data , its value cannot be used for inference of coupling strength beyond relative comparisons ( see also [57] ) . Moreover , Granger causality scores were practically uncorrelated with our proposed measure of coupling strength . This is because Granger-based significance analysis strongly depends on the amount of data used and absolute values of inferred GC coupling strengths are difficult to interpret . In the statistical framework proposed here , inference is based on the effect size of an inferred coupling rather than statistical significance . We propose defining coupling strengths as a property of the estimated filters . This allows better interpretability of the results and the separation of coupling strengths between the nonexistent biological connections and the remaining ones . Granger causality analysis and related approaches are sometimes called model-free procedures [45] , [58]–[60] , but are still based on implicit model assumptions . These assumptions are rarely checked in practice , and the final GC scores and their P-values are commonly the only factors used for inference . For the proposed point process model and functional definition of coupling strength , by making the model assumptions explicit , we allow for the application of rigorous goodness-of-fit and model selection procedures that help in choosing a suitable model . The point process model was primarily used as an inference tool to deduce the connectivity between a set of observed neurons . Although this constitutes a statistical and phenomenological model ( i . e . , it does not explicitly model biophysical processes ) , we have shown its potential as a generative model ( Figures 2C and D ) . The coupling filters of the point process model have both statistical and physiological interpretations , analogous to biophysically-based synaptic interactions . Coupling filters interact in a multiplicative way , i . e . , they modulate an underlying baseline firing rate instead of increasing or decreasing the firing rate by a fixed amount . The coupling strength ( the integrated area under the interaction kernel ) is related to the number of spikes that , depending on the instantaneous postsynaptic firing rate , are generated or suppressed on average due to a single presynaptic spike [35] . Because of the sigmoidal nonlinearity between the linear summation of couplings and the resulting firing rate , the effect of a presynaptic spike can vary dynamically depending on the current gain ( slope ) of the transfer function . Therefore , the model can partly distinguish synaptic interactions from postsynaptic excitability unlike previous approaches . From a biological perspective , the sum of contributions of past neural activity in a point process model can be interpreted as the influence on the neurons' membrane potential . The coupling filters correspond to synaptic interactions , e . g . , in the spike-response model [61] . The shapes of the filters suggest the time scales of the synaptic ( or effective ) interaction , their sign ( excitatory versus inhibitory ) , and amplitudes . The model is flexible enough to allow for polyphasic responses although our definition of the coupling strength reduces the response to a scalar value ( see [37] for examples of polyphasic interactions in the STG ) . Periodic structure in the spike trains ( such as bursting and the time scale of the periodic pyloric rhythm ) can be read off from the peaks in the filters at the corresponding time lag because they represent the modulation of the firing probability locked to the exact spike timings . Although the coupling filters have a similar interpretation in the linear model , in the STG analysis , their shapes were not suggestive of the type of interaction . The relationship between the effective coupling filters and biological postsynaptic potentials is not unique . This is especially true for inhibitory connections in the STG: Consider presynaptic spiking activity that always occurs at a fixed relative phase of the postsynaptic burst cycle in which the postsynaptic neuron is already hyperpolarized . The observation of the absence of any postsynaptic spike does not contain any information about the amplitude of the synaptic conductance beyond a minimal value that prevents the postsynaptic neuron from firing . In these cases , estimates of coupling filters diverge and we cap them at an arbitrary value that does not affect the qualitative results of the analysis . This so-called phase response saturation has been shown in experiments and detailed neuron models of the pyloric rhythm [62] , [63] and should serve as a reminder that neural couplings might not be uniquely identified when no information about the subthreshold activity is available . We note that while using the net integral of the coupling filters as a measure of coupling strength has led to a good correlation between inferred coupling strengths and the presence of real couplings , other measures of coupling strengths might be useful to consider as well . These could include other features of the kernel ( such as its peak amplitude ) or be limited to certain temporal scales ( e . g . , near-simultaneous , or short versus prolonged interactions ) . For the point process model presented here , all available data were used and free parameters were chosen with a straightforward , but rigorous model selection procedure . Because a nonzero coupling strength is recovered for each possible connection , different binary connectomes can be obtained by varying a threshold that determines whether a connection is substantial . Using a threshold to determine a binary circuit diagram based on statistical significance alone would result in the inference of a fully connected network . Yet , we have observed that setting the known missing physiological connection to zero did not change the functional behavior of the modeled circuit suggesting that statistical significance is not an appropriate metric for determining functional interactions in this data set . It is known that networks with different neuron parameters can express very similar pyloric-like rhythmic activity [64] , [65] . A more sophisticated procedure that chooses an optimal threshold in a data-driven way based on physiological significance is desirable . Finally , an advantage of point process models is the availability of goodness-of-fit tests that are not always assessed in practice in Granger causality analysis . When we applied model adequacy tests to the linear rate model , we could identify its shortcomings in capturing the structure of the data . The results hinted at the necessary modifications to construct a model whose network inference could match the physiology . Because any model-based assessment of connectivity is expected to show model misspecifications given enough data , we suggest methods that explicitly consider the structure of the data in building the model and use interpretable measures of connectivity rather than statistical significance levels . A series of goodness-of-fit tests , tailored to the point process nature of the model , strengthened our confidence in the model's inferred network structure and demonstrated the robustness of our results . In general , effective connectivity will not necessarily be equal to physiological or structural connectivity [66] , [67] , even if our study suggests sophisticated statistical models might permit inference of actual physiological connectivity from extracellular recordings . Especially for larger-sized ( and cortical ) networks , effective connectivity between a subset of neurons will be different from physiological connectivity . This is because of indirect connections and shared , unobserved inputs . Nevertheless , because monosynaptic direct couplings should form a subset of inferred effective connections [68] , such a measure can still be useful [22] , [23] , [34] , [66] , e . g . , to improve decoding performance ( see [69] for a recent demonstration with multi-electrode recordings in different cortical regions ) , to distinguish different network states or to track plasticity-induced changes [33] , [70] . We have demonstrated , for example , that partial blockade of synaptic transmission strongly reduced the strength of inferred couplings . In addition , changes of intrinsic currents not explicitly represented in our model can be characterized using the notion of an effective coupling between neurons or coupling of a neuron with itself . As such , we expect the class of point process models presented here could also be useful in other contexts of neurophysiology , such as characterizing single-neuron responses [71] , [72] or general network dynamics [70] , [73] . Although we have shown that linear models do not recover the physiological network architecture in the pyloric circuit , they may be more applicable to large networks where measurements reflect averaged population activity and nonlinearities may potentially average out [12] . Ultimately , to compare the relative performances of the models put forward here , the approach taken in this study must be scaled to larger networks and recordings [74] . Although simultaneous recordings from many neurons are now routine , we lack the necessary independent assessment of their structural connectivity . Experimental protocols necessary to obtain both signals and structural information of neural circuits are being actively developed: A recent study combined in vivo functional imaging using two-photon calcium imaging with subsequent paired patch-clamp recordings of the same individual cells in slices [75] . For a small number of cell pairs , synaptic connectivity could be unambiguously inferred using the intracellular recordings . Progress in multi-photon imaging has been made to achieve the temporal resolution necessary to infer sequences of spikes from such functional recordings [76]–[78] . Taken together , these approaches could be used to validate connectivity inference algorithms based on spike trains or imaging signals in the future [79]–[81] . A growing scientific community is interested in multi-neuron models and connectomics . As these data become more widely available , principled methods that incorporate known statistical structure in the data — such as the one proposed here — will be of fundamental importance .
Full experimental details for the four data sets can be found elsewhere [29] . Briefly , Jonah crabs ( Cancer Borealis ) were purchased from a commercial food supplier ( Commercial Lobster , Boston MA ) and held in artificial seawater tanks at . Prior to dissection , animals were put in ice for 30 minutes to numb them . The stomach was removed from the animals and pinned into a dish and immersed in physiological saline containing: NaCl , 440 mM; KCl , 13 mM; MgCl2 , 26 mM; CaCl2 , 13 mM; Trizma base , 11 mM; maleic acid , 5 mM; pH 7 . 45 . Under a microscope the stomatogastric nervous system ( STNS ) was separated from surrounding tissues and pinned into a smaller dish for electrophysiological recordings . Vaseline mixed with mineral oil was used to build waterproof wells around identified nerves to record action potentials from stomatogastric ganglion ( STG ) neurons . Steel electrodes were placed into these wells with reference electrodes in the bath to record electrical signals . These signals were recorded with an AM Systems Model 1700 AC Amplifier and digitized with an Axon Instruments Digidata 1440A ( Axon Instruments , Sunnyvale , CA ) . pClamp software ( Molecular Devices , Sunnyvale , CA ) , running on a PC computer , was used to record extracellular signals continuously . During recordings , saline was continuously perfused and recording temperature was kept as close to as possible with a Peltier cooling system ( Warner Instruments , Hamden , CT; Harvard Apparatus , Holliston , MA ) . Spikes were extracted for three different neurons ( PD , LP , and PY ) from three different nerves ( pdn , lvn , and pyn ) , respectively . Single spikes were extracted by a threshold criterion . Spike trains were analyzed off-line using Spike2 software ( CED , Cambridge , UK ) and then exported to MATLAB for further processing . Recordings were obtained from four preparations for recording periods ranging between 140 and 300 seconds . Results reported in the text and figures refer to a single data set ( #1 ) , unless otherwise noted . Results are qualitatively similar for all four data sets . For predicting changes of coupling strength by pharmacological conditions , data were acquired in a similar way as described above . Specifically , CsCl at 5 mM concentration was applied to the preparation to block h-currents . Recordings include 300 s of data before the application ( control ) and 300 s after application of CsCl ( condition ) . Visible spike sorting artifacts were removed by visual inspection . The model selection procedure selected a maximal lag of 1 s for the self-history filters and 350 ms for the cross-coupling filters where maximal lags were jointly optimized for both data sets using the BIC-penalized likelihood criterion . For the application of picrotoxin ( PTX ) , the control condition consists of 360 s of recordings before the application and 120 s of stationary activity 6 minutes after application of PTX ( Sigma Aldrich , St . Louis , MO ) at added to the saline . Spike trains were acquired as described previously . The model selection procedure selected a maximal lag of 1300 ms for the self-history filters and 100 ms for the cross-coupling filters where maximal lags were jointly optimized for both data sets using the BIC-penalized likelihood criterion . For the generation of stochastic spike trains from the model , maximal lags for the model of the PTX condition were manually chosen to accommodate the long period of the pyloric rhythm ( approximately 5 seconds ) . A multivariate point process model of the spiking activity was constructed using the conditional intensity framework [82] for which the instantaneous firing intensity ( or rate ) for neuron Y is given by: ( 1 ) where summarizes the activity of all neurons up to time t and possibly other extrinsic variables , and denotes the length of a time period . For a time-discrete model with , the probability of spiking in a time bin i becomes: ( 2 ) Observed spike trains were converted into a binary sequence of spiking activity that indicates whether or not there was a spike in the time window . The model can be easily adapted to multi-unit activity ( MUA ) by replacing the Bernoulli likelihood with a Poisson likelihood that allows an arbitrary number of spikes per time bin . The point process likelihood is approximated by the likelihood of the binary Bernoulli model so that the log likelihood of the data is given by: ( 3 ) is modeled as a nonlinear transformation of a linear sum of explanatory variables: ( 4 ) where sums the effects of the recent spiking activity of the neuron itself ( ) , the activity of other neurons ( and ) and possibly other factors . Hence , is the sum of these three terms plus a constant baseline: ( 5 ) Here , the constant baseline regulates the spontaneous firing activity and are convolutions of the spike train of neuron c with a coupling filter . Coupling filters are modeled with spline basis functions with knot points separated by 5 ms up to the maximum lag ( see [34] for details ) . Specifically , if denotes the nth spike time of neuron c and is the jth out of basis functions for a self-coupling filter ( ) , then: ( 6 ) Similarly , the contributions from the cross-coupling terms are given by: ( 7 ) where the basis functions of the cross-coupling filters are denoted by , ( and similarly for ) . Note that although spline basis functions are used for both self- and cross-coupling filters , due to different maximal lags ( unless ) . The exact shape of the basis functions , i . e . , the order of the spline representation , did not have a significant impact on the reported results ( see Figures S1F and G ) . We determined the maximal lags for the self- and cross-coupling filters separately by a BIC criterion [83] . For the self-history kernel , models with varying maximal lags ( up to two times the burst cycle period of the data set ) were fit without any cross-couplings . The burst cycle period is defined as the length of the data set divided by the number of bursts separated by an interspike interval of more than 200 ms . The negative log-likelihood evaluated on the data used for fitting ( Equation ( 3 ) ) was corrected by a term where p is the number of model parameters and N is the number of sample points to yield the BIC value: . We then summed BIC values for all neurons of the same data set . Once we determined the maximal lag for the self-coupling filter , we fitted full models including the cross-coupling filters and varied their maximal lag up to 1 . 2 times the burst cycle period of the data set . The maximal range of tested values was chosen so that a U-shaped curve could be obtained in all cases . The lag that corresponded to the minimal BIC value was then chosen as the maximal lag for all six cross-coupling filters . Model parameters were fitted using standard maximum-likelihood techniques [84] , [85] . Prior to fitting , explanatory variables whose presence allowed the perfect prediction of the absence of spikes were removed together with the corresponding data bins . This was the case , for example , whenever spikes of a putative presynaptic neuron were never followed by a spike of the modeled neuron at a fixed delay . The maximum-likelihood solution for the value of the interaction filter at this delay diverges to minus infinity . To ensure convergence of the model estimation procedure , the corresponding coefficients were fixed to −20 so that the resulting probability of spiking is practically zero . Furthermore , a lower bound of −20 was imposed on all coefficients . The results of the analysis are not dependent on the exact value of this cut-off parameter ( Figure 2F ) . Statistical significance of single parameter values can be ( approximately ) established using the Wald statistic [85] . Here , we are interested in the statistical significance of a specific interaction filter that is composed of basis functions with associated parameters . If denotes the subset of parameters of the complete estimated parameter vector and the corresponding entries of the observed Fisher information matrix , then the compound test statistic follows ( approximately ) a distribution with degrees of freedom [85] . In practice , all parameter estimates were highly statistically significant so that the approximative nature of the formula is negligible . Spike train activity was simulated from the model by drawing stochastic samples according to with given by Equation ( 4 ) and similarly for neurons X and Z . The initial spike-history terms were computed from 1 second of observed spike trains . We applied the previously described analysis steps to all four data sets . Specifically , the model selection procedure ( using BIC-corrected log likelihoods ) is performed separately for each data set . We report and visualize the results only for the first data set , unless otherwise noted . For all data sets , we used the complete recording periods unless otherwise noted . Firing rates for the three neurons X , Y and Z are obtained from the spike train recordings by first convolving the spike trains with a half-Gaussian ( i . e . , causal ) filter with standard deviation . The resulting function is discretized into a time series with sampling frequency of . The values of both parameters , and f , are chosen to be consistent with [29] , but we additionally analyzed variations of both parameters in the context of a sensitivity analysis ( see Results ) . Furthermore , linear trends of all time series are locally removed [27] . A multivariate linear model is then constructed for the ( normalized , i . e . , zero-mean ) firing rate at time using auto- and cross-regressive terms as follows: ( 8 ) where is the maximal time lag to consider for the self-coupling , ( ) are the model coefficients for the self-interaction ( interaction with time-series Z ) and is the noise term . All model parameters were estimated using standard techniques of linear regression . The maximal time lag was set to ( i . e . , maximal lag of 400 ms ) [29] , unless otherwise noted . A threshold of ( Bonferroni-corrected for multiple comparisons; six cross-couplings ) is used to determine significant interactions . To generate stochastic samples from the model , multivariate models were first fit to each neuron . For the first second , the time series , and were taken to be the smoothed spike trains of the real recordings and preprocessed as described before . Then , for , new activity samples were iteratively simulated via Eq . ( 8 ) with being now an i . i . d . sample from a normal distribution with variance obtained from the model fit . For the point process model , we define the directed coupling strength between two neurons as the net integral of the corresponding cross-coupling filter: ( 9 ) An equivalent definition can be made for the linear rate model . In our case , coupling strengths were qualitatively similar whether a multivariate or only pair-wise model was used ( Figures S1D and E ) . The type of the directed interaction between X and Y is completely specified by the filter coefficients . The reduction of the ( potentially multifaceted ) interaction into a single quantity like CS is not unique . For the point process model , Equation ( 9 ) captures the integrated modulatory effect of a spike of one neuron onto the spiking activity of the other neuron . We chose the integral of the filter in lieu of , e . g . , its peak , because it is a linear function of the model coefficients and thus is more robustly estimated from a finite amount of data . Moreover , potentially polyphasic interactions , such as interactions that are both excitatory and inhibitory on different time scales , are reduced to their dominant mode . An example of such polyphasic dynamics for the self-interaction filter might include short inhibitory refractory effects , followed by excitatory burst-like rebounds and longer suppressive periods . We use the absolute value of the integral in Equation ( 9 ) to obtain a measure of coupling strength that is independent of the actual direction of modulation ( excitatory versus inhibitory ) . This direction of interaction can be assessed by computing without taking the absolute value: is classified as a net inhibitory interaction , is effectively excitatory . Due to the constraints of the model , CS measures a combination of synaptic interactions and post-synaptic excitability if the latter cannot be completely accounted for by the self-coupling filters , like voltage-dependent ion channel dynamics . Granger causality analysis attempts to assess the strength of a causal ( i . e . , directed ) interaction between two time series X and Y in the presence of other explanatory variables , e . g . a third time-series Z . We briefly describe the framework here , more details may be found elsewhere ( for linear models , see [27] , [86]; for point process models , see , e . g . , [22] , [23] ) . To estimate the causal strength of the directed link , two models are constructed: First , an autoregressive model of Y is built using Y's own history ( and the activity of any other explanatory variable , here , the activity of the third neuron Z ) to predict its next value . For the point process model , this leads to replacing Equation ( 5 ) by: ( 10 ) For the linear rate model , the corresponding equation is: ( 11 ) with residual term , i . e . , the difference between the predicted and observed value . In this context , we restrict the analysis to linear autoregressive models with normal innovations , i . e . , the residuals are assumed to be independent random samples of a Gaussian distribution . To assess the interaction , this reduced model is compared to the full , multivariate models as defined above . If the inclusion of X's history significantly decreases the variance of the residuals , there exists a directed link from in the sense of Granger causality . For linear models , the reduction in variance can be measured by the log ratio: and its significance can be tested using the F-test procedure ( see [27] for details ) . For point process models , the Granger causality score is defined by the log-likelihood ratio , or , in other terms , the difference in model deviances [23] , [84] . Because the two models are nested and likelihoods are evaluated on the training data , Granger causality scores are always non-negative . | To appreciate how neural circuits control behaviors , we must understand two things . First , how the neurons comprising the circuit are connected , and second , how neurons and their connections change after learning or in response to neuromodulators . Neuronal connectivity is difficult to determine experimentally , whereas neuronal activity can often be readily measured . We describe a statistical model to estimate circuit connectivity directly from measured activity patterns . We use the timing relationships between observed spikes to predict synaptic interactions between simultaneously observed neurons . The model estimate provides each predicted connection with a curve that represents how strongly , and at which temporal delays , one circuit element effectively influences another . These curves are analogous to synaptic interactions of the level of the membrane potential of biological neurons and share some of their features such as being inhibitory or excitatory . We test our method on recordings from the pyloric circuit in the crab stomatogastric ganglion , a small circuit whose connectivity is completely known beforehand , and find that the predicted circuit matches the biological one — a result other techniques failed to achieve . In addition , we show that drug manipulations impacting the circuit are revealed by this technique . These results illustrate the utility of our analysis approach for inferring connections from neural spiking activity . | [
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] | 2013 | Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone |
The molecular mechanism for meiotic entry remains largely elusive in flowering plants . Only Arabidopsis SWI1/DYAD and maize AM1 , both of which are the coiled-coil protein , are known to be required for the initiation of plant meiosis . The mechanism underlying the synchrony of male meiosis , characteristic to flowering plants , has also been unclear in the plant kingdom . In other eukaryotes , RNA-recognition-motif ( RRM ) proteins are known to play essential roles in germ-cell development and meiosis progression . Rice MEL2 protein discovered in this study shows partial similarity with human proline-rich RRM protein , deleted in Azoospermia-Associated Protein1 ( DAZAP1 ) , though MEL2 also possesses ankyrin repeats and a RING finger motif . Expression analyses of several cell-cycle markers revealed that , in mel2 mutant anthers , most germ cells failed to enter premeiotic S-phase and meiosis , and a part escaped from the defect and underwent meiosis with a significant delay or continued mitotic cycles . Immunofluorescent detection revealed that T7 peptide-tagged MEL2 localized at cytoplasmic perinuclear region of germ cells during premeiotic interphase in transgenic rice plants . This study is the first report of the plant RRM protein , which is required for regulating the premeiotic G1/S-phase transition of male and female germ cells and also establishing synchrony of male meiosis . This study will contribute to elucidation of similarities and diversities in reproduction system between plants and other species .
The transition from mitotic to meiotic cell cycle is a central issue of reproductive development in all sexually reproducing species . Meiosis is a fundamentally different type of cell cycle from mitosis , and a pivotal event for eukaryotes to halve the chromosome number and form haploid gametes . The basic meiotic processes are evolutionarily conserved among eukaryotic species . In contrast , the signalling cascade that leads to meiosis initiation shows great diversity among species [1] . The mechanism initiating meiotic entry remains largely elusive in plants . Based on experiments using explanted pollen mother cells ( PMCs ) of Trillium , the commitment of mitotic cells to meiotic division is thought to be established during the premeiotic DNA replication ( premeiotic S ) or G2 phase in plants [2] , [3] . Only Arabidopsis SWITCH1 ( SWI1 ) /DYAD and its maize homolog AMEIOTIC1 ( AM1 ) are known to be required for the initiation of plant meiosis . Both are plant-specific coiled-coil proteins with unknown functions [4]–[6] . The maize am1 mutant displays the replacement of male and female meioses by somatic mitoses , and eventually the degeneration of ameiotic meiocytes [7]–[9] , indicating that AM1 is implicated in the decision of germ cells being directed to meiosis or mitosis . Thus , the primary function of AM1 is supposed in the premeiotic S or G2 . However , immunocytological analyses revealed that AM1 diffuses inside the nucleus during premeiosis , and its localization shifts to meiotic chromosomes and pericentromeric regions during early meiosis [6] , suggesting that AM1 plays a second role in progression of early meiosis . SWI1/DYAD does not seem to act directly to initiate meiosis , because it also acts in the regulation of meiotic chromosome structure and cohesion [4] . Thus , molecular mechanisms specifically underlying meiotic entry have been poorly understood in the plant kingdom . Flowering plants have evolved an intricate network of regulatory mechanisms to ensure proper timing of the transition to flowering [10] . In addition , to achieve simultaneous fertilization within a limited season , the timing of meiotic entry is also strictly regulated . Male meiosis usually occurs in a large population of synchronously dividing cells to ensure sufficient fertility of the organisms . In plants , it is also synchronous among PMCs within an anther and among anthers within a single flower . The synchrony of male meiosis is thought to be established during premeiotic interphase . This is because the thymidine base analog , bromodeoxyuridine ( BrdU ) , becomes incorporated synchronously into PMCs at premeiotic S [11] , [12] , while their preceding mitoses seem to occur asynchronously . The Arabidopsis mutant , tardy asynchronous meiosis ( tam ) , exhibits a phenotype of delayed and asynchronous cell divisions during male meiosis [13] . The TAM gene encodes an A-type cyclin that abundantly accumulates in nuclei of male meiocytes during prophase I [14] , strongly suggesting that cyclins and cyclin-dependent kinases govern the synchronous progression of plant meiosis . RNA-recognition-motif ( RRM ) proteins play crucial roles in the regulation of germ cell development , especially meiosis , in yeast and metazoan species . They participate in the processing , transport , localization , and translation of mRNAs [15] . In fission yeast , the RRM protein , Mei2 , is necessary for the initiation of meiosis by turning off the DSR-Mmi1 system for selective elimination of over a dozen meiosis-specific transcripts during the mitotic cell cycle [16] . Deletions encompassing the human Y-linked Deleted in azoospermia ( DAZ ) gene cluster , encoding RRM proteins , result in a complete loss or severe reduction of germ cells in the testis . In all species examined , the expression of DAZ , DAZ-like ( DAZL ) and their homologs has been reported only in germline cells [17]–[19] . These RRM proteins target the adenylate-uridylate-rich element ( AU-rich element; ARE ) found within the 3′ untranslated region ( 3′UTR ) of mRNAs , and control mRNA turnover rate and translation in cooperation with poly ( A ) -binding proteins [20] , [21] . Boule , the Drosophila ortholog of vertebrate DAZL , binds to the 3′UTR of Twine mRNA , which encodes a meiotic-type Cdc25 kinase , and promotes the translation of Twine and the premeiotic G2/M transition [18] . Mouse DAZL also binds to the 3′UTR and promotes the translation of Sycp3 mRNA , which is a component of the synaptonemal complex ( SC ) [22] . In plants , redundant roles of five members of Arabidopsis mei2-like RNA binding proteins ( AMLs ) are suggested in meiotic chromosome organization [23] . The AMLs are composed of three RRMs , like fission yeast Mei2 , whereas their function is presumably different from that of yeast Mei2 in meiotic entry . Therefore , in plant reproduction , no RRM protein functionally analogous to that of yeast and metazoans has been reported . In this study , we identified a novel rice RRM protein , MEIOSIS ARRESTED AT LEPTOTENE2 ( MEL2 ) . In mel2 anthers at early meiosis , most germ cells failed to enter the premeiotic S and meiosis , and a part escaped from the defect in the premeiotic S and underwent meiosis with a significant delay or continued mitotic cycles aberrantly . Rice MEL2 had partial similarity with human DAZ-Associated Protein1 ( DAZAP1 ) [24] . However , MEL2 carried ankyrin repeats and the RING finger motif in addition to the RRM . This motif combination was conserved among the monocot Poaceae species , but not in dicot plants or in other organisms , despite the control of premeiotic germ-cell cycle essential for the reproduction of all eukaryotes . We will discuss structural differences and functional similarities of rice MEL2 to RRM proteins involved in the mammalian DAZ system mainly by analysis of the mel2 mutant .
Figure 1A illustrates the initiation and differentiation of rice germline cells described by Nonomura et al . [25] , [26] . Primordial germ cells , or archesporial cells , are initiated at the hypodermis of the stamen and the ovule primordium . In the stamen , archesporial cells generate sporogenous and parietal cells . Male sporogenous cells undergo several premeiotic mitoses , and many meiocytes are produced in each of the four locules within the anther . Parietal cells continue periclinal divisions and generate three-layered inner-anther walls , the most interior of which become tapetal cells to provide nutrients and pollen-wall materials to male meiocytes and microspores . In the ovule primordium , plural archesporial cells are initiated . Subsequently , only a single archespore which adheres to the nucellar epidermis differentiates into a single sporogenous cell , and matures into a single female meiocyte . During premeiotic maturation , both male and female meiocytes enlarge far more in cytoplasmic and nuclear volumes than somatic cells . To elucidate the genetic network that supports early germ-cell development , we selected a seed-sterile mutant line , ND00287 , in rice . The sterile segregants of this line developed normally throughout their life cycle except for seed production ( Figure S1 ) . The sterile phenotype segregated as a single recessive mutation ( fertile:sterile = 141∶47 , chi-square ( 3∶1 ) = 0 . 00 ) . Microscopic observation revealed that whereas the wild type had equally sized PMCs , sterile mutants produced divergent sizes of PMCs ( Figure S2 ) , probably a result of insufficient maturation and enlargement of premeiotic cells . Gametogenesis was disrupted in both male and female organs of the mutant ( Figure S1 ) . This phenotype resembled that of the mel1 mutant that we previously identified [26] , and thus this gene was designated MEL2 . In anthers , the appearance of PMCs at the premeiotic interphase was unlikely to have been affected by the mel2 mutation , except callose accumulation around the cells was lacking in the mutant ( Figure 1B , 1G ) . Callose is one of the cell wall component , and plays a vital role in the process of pollen development [27] . Interestingly , in mel2-1 mutant anthers , 0 . 69% of premeiotic germ cells ( n = 291 ) underwent the mitotic metaphase , whereas no PMCs did in the wild type ( n = 311 ) ( Figure 1B , 1G ) . While the wild-type PMCs entered and underwent normal meiosis , the mutant PMCs were drastically hypervacuolated ( Figure 1C , 1H ) . In the wild type , haploid microspores were released from tetrads after the completion of meiosis ( Figure 1D ) . In contrast , highly vacuolated mutant PMCs failed to produce tetrads and microspores ( Figure 1I ) . In addition to a failure in meiosis , tapetal cells also became aberrantly vacuolated and hypertrophic ( Figure 1I ) . Highly vacuolated PMCs underwent apoptotic DNA fragmentation , revealed by the TdT-mediated dUTP-biotin nick end labeling ( TUNEL ) method ( Figure 1E , 1J ) . Aberrantly hypertrophic tapetal cells also caused apoptosis at a step earlier than the programmed cell death ( PCD ) in normal process of tapetal development ( Figure 1E , 1J ) . A serious defect in meiosis progression was also observed in the megaspore mother cell ( MMC ) , the female meiocyte . When three of the tetrad spores had been degraded in the wild-type ovule ( Figure 1F ) , the mutant MMC was still before meiotic cell division ( Figure 1K ) or the tetrad before degradation of three spores ( Figure 1L ) . Surprisingly , in contrast to PMCs , no conspicuous vacuolation was observed in the MMC . Though the ultrastructure of PMCs was also observed , no remarkable difference was observed between the wild type and mel2 mutant at the premeiotic interphase ( data not shown ) . Howerver , at the meiotic prophase I , mel2 PMCs were hypervacuolated , but not in the wild-type PMCs , and in addition , mitochondria were enlarged in mel2 PMCs extremely more than those in wild types ( ) . The formation of megamitochondria is known to precede apoptosis in the cells treated with various free radical-generating chemicals [28] . Thus , the ultrastructural analysis also suggested that the mel2 PMCs were directed to apoptosis . Southern blot analysis of the ND00287 population revealed that the Tos17 insertion showed complete genetic linkage with the seed-sterile phenotype ( Figure S4 ) . This insertion tagged the gene locus , Os12g0572800 , in Rice Annotation Project Database build4 ( RAP-DB , http://rapdb . dna . affrc . go . jp/ ) . When the 10-kbp wild-type genomic fragment including this locus was introduced into mel2 homozygous plants , the transformants recovered fertility ( Figure S4 ) . Furthermore , NE04525 carrying another allelic Tos17 insertion in this locus ( mel2-2 ) exhibited the same mel2 phenotype ( data not shown ) . Thus , we concluded that the Tos17 insertion into Os12g0572800 caused the mel2 mutation . Full-length MEL2 cDNA was obtained from young panicles , including germ cells at developmental stages earlier than meiosis , by 5′-rapid amplification of cDNA ends ( RACE ) technology . To determine the transcriptional start site of MEL2 mRNA , three rounds of 5′RACE were performed . Four independent RACE libraries were produced by a gene-specific antisense primer nearest to the 5′ end ( Figure S4B ) , and 16 of 17 RACE sequences terminated at the same 5′-endpoint . The putative start site predicted in this study mapped to 254 bp upstream from the location annotated in the RAP-DB . The MEL2 gene was composed of 14 exons and 13 introns ( Figure S4 ) . The MEL2 cDNA encoded a novel protein of 1 , 160 amino-acid residues ( aa ) of previously unknown function ( DDBJ , AB522964 ) . An online motif search revealed three conserved domains in the deduced MEL2 sequence: ankyrin repeats ( ANKs , PF00023 ) , an RNA recognition motif ( RRM , PF00076 ) , and a C3HC4-type RING finger motif ( RING , PF00097 ) ( Figure 2 , Figure S5 ) . An N-myristoylation consensus sequence , which allows protein binding to the plasma membrane or other intracellular membranes in eukaryotic cells [29] , was found at the N-terminal end . ANKs are implicated in protein-protein interactions [30] . Rice MEL2 contained 10 imperfect and tandemly aligned copies of ANKs ( Figure S5 ) . The RRM consisted of 80–90 aa with two highly conserved short motifs , an RNP1 octamer and an RNP2 hexamer , which are found in numerous proteins involved in post-transcriptional processes [31]–[33] . MEL2 contained a single RRM that conserved both RNP1 and RNP2 sequences ( Figure 2 ) . The MEL2 peptide sequence excluding the ANKs ( 451 aa to the end ) showed similarity to human DAZAP1 in a BLASTp search ( Score = 62 . 4 bits; E-value = 1e−07 ) [34] . DAZAP1 contains two RRMs at the N-terminus and a proline-rich domain at the C-terminus [24] . The C-terminal half of rice MEL2 was also rich in proline residues ( 615 to 1 , 042 aa , Figure 2 ) . The rice MEL2 sequence was evolutionarily conserved among Poaceae species; Sorghum bicolor , Brachypodium distachyon , and Zea mays ( Figure 2 , Figure S6 ) . The Sorghum locus Sb08g018890 and the Brachypodium locus Bd04g03890 encoded putative proteins of 1 , 083 and 1 , 076 aa , 77 . 1% ( 813/1 , 055 aa ) and 75 . 5% ( 791/1 , 048 aa ) identical to rice MEL2 , respectively . The Zea locus AC208308 . 3_FGP002 showed high conservation of the RRM and RING , but not the ANK , probably because the sequence information was incomplete in maize genome . The RRM followed by the proline-rich sequence was also conserved in these three species . No proteins carrying a combination of the three domains , ANK , RRM and RING , were found within the genome of the dicot model plant Arabidopsis thaliana , nor did we detect any proteins with the three-domain combination in genome information from 787 species included in the Archaea , Bacteria , Eukaryota and Viruses by in silico searches of the GTOP web database [35] , [36] . Thus , we concluded that the motif combination found in rice MEL2 is unique to Poaceae or monocot plants . The rice genome contained another predicted gene locus , Os12g0587100 , similar to MEL2 ( Figure S7 ) . Os12g0587100 was located about 0 . 9-Mbp from the MEL2 locus toward the telomere side on the long arm of chromosome 12 . Putative coding sequences of this locus were highly homologous to those of the MEL2 gene , while the homology was lost in the exon 12 and the following exons . The spatiotemporal expression of MEL2 mRNA was examined by reverse-transcription PCR ( RT-PCR ) and in situ hybridization techniques . Based on the results of RT-PCR , the MEL2 mRNA was expressed mainly in young panicles and flowers ( Figure S8 ) . In situ hybridization revealed that the MEL2 expression was initiated in male and female archesporial cells at the hypodermis of stamen and ovule primordia ( Figure 3A–3C ) . In the ovule , the MEL2 mRNA was expressed in multiple archesporial cells during early stages ( Figure 3B , 3C ) , and subsequently in a single sporogenous cell ( Figure 3D , 3E ) . In the stamen , strong MEL2 signals were detected in sporogenous cells ( Figure 3D , 3F ) , and in addition , faint signals were also observed in parietal cells , which generate tapetal cells ( Figure 3F ) . The MEL2 signal disappeared before meiosis in both male and female organs ( Figure 3G ) . Thus , we concluded that the MEL2 gene was expressed in male and female germline cells from their initiation to meiosis , and weakly in male nursery cells including tapetal cells . The mel2-1 mutant flowers transcribed only aberrant types of MEL2 mRNA , which contained a 4 . 0-kbp Tos17 insertion within the seventh exon ( Figure S9 ) . This insertion was predicted to cause an in-frame stop codon at the 5′-end of the insert , and to result in a truncated form of MEL2 protein without the RRM and RING , if any was translated . Thus we concluded that the mel2-1 was a null allele . The MEL2-like Os12g0587100 locus was also transcribed , and its transcripts were detected in young panicles and flowers ( Figure S8 ) . Sequencing the RT-PCR product revealed that the MEL2-like cDNA included many nucleotide polymorphisms against MEL2 cDNA , which could induce a shift in reading frame that would result in lack of RRM and RING motifs ( data not shown ) . Therefore , the MEL2-like locus was considered to be a pseudo-gene . If MEL2 had functioned during premeiotic mitosis prior to premeiotic interphase , the number of PMCs would be decreased in mel2-1 mutant anthers . However , it was not different significantly between PMC numbers in the wild type ( 103 . 7±15 . 3 per anther locule; average of three anthers ) and the mutant ( 97 . 0±6 . 6 ) . In addition , no remarkable aberration in the morphology of reproductive tissues and germ cells was observed in the mel2 anthers during the premeiotic-mitosis stage ( Figure 1G ) . These observations indicate that MEL2 function was excluded from the premeiotic-mitosis stage of male germ cells . To investigate whether the mel2 germ cells could pass through the premeiotic interphase normally , we examined the expression profile of several cell cycle-related genes in mel2 mutant anthers . The rice histone H4 mRNA is abundantly expressed during S-phase , and CDKB2;1 ( cdc2Os3 ) is enriched during G2/M transition , but is less abundant or absent during S-phase in rice mitosis [37] . During premeiotic mitosis , CDKB2;1 was expressed in patches in both wild-type and mel2-1 anthers ( Figure 4A , 4F ) , indicating that premeiotic germline mitoses occur asynchronously in the wild type and are unaffected by the mel2 mutation . At the boundary of premeiotic interphase and meiosis , no expression of CDKB2;1 mRNA was detected in the wild type ( Figure 4B ) , indicating that the cells entered synchronously into premeiotic S , whereas CDKB2;1 was still expressed in patches in the mel2 anther ( Figure 4G ) . At the same stage , H4 mRNA was expressed in all PMCs of wild-type anthers ( Figure 4C ) . In contrast , in mel2 anthers , only a few PMCs exhibited H4 signal ( Figure 4H ) . The asynchronous expression of CDKB2;1 and H4 was observed in anthers of three independent mel2 plants at the stages that the wild type underwent early meiosis . The MEL1 gene , which encodes an Argonaute family protein , is expressed exclusively in germline cells before meiosis [26] . In the wild type , MEL1 mRNA was strongly detected in sporogenous cells undergoing premeiotic mitosis ( Figure 4D ) , and was rapidly downregulated during premeiotic interphase ( Figure 4E ) . Premeiotic mel2 sporogenous cells also expressed MEL1 , same as wild-type cells ( Figure 4I ) . However , the expression continued aberrantly in early meiotic stages ( Figure 4J ) . Next , we performed the BrdU incorporation experiment for the premeiotic athers . In the wild type , ten of 31 flowers at the premeiotic interphase had anthers in which PMCs incorporated BrdU into their nuclei synchronously , whereas the most mel2 flowers had anthers that showed asynchronous incorporation of BrdU into the PMC nuclei ( Figure 5A ) . In most of mel2 anthers , only 20% of PMCs incorporated BrdU simultaneously ( Figure 5B , 5C ) . These results clearly indicate that MEL2 plays essential roles in the decision for germ cells to enter the premeiotic S-phase . Next , we examined whether the mel2 mutant PMCs were able to enter meiosis . Telomere clustering or a bouquet structure , a typical feature of zygotene meiocytes in maize [38] , was observed in four of 10 wild-type meiocytes , whereas no bouquet was observed in any of 31 mel2-1 meiocytes ( Figure S10 ) . In wild-type soma and premeiotic meiocytes , centromeres and telomeres were arranged in peripheral and interior regions of the nucleus , respectively , and their positions were inverted during meiotic entry . However , mel2 PMCs at early zygotene lacked this inversion , retaining a soma-like centromere arrangement ( Figure S10 ) . Further , we performed immunofluorescent detection of rice meiotic proteins PAIR2 and ZEP1 . PAIR2 transiently associates with meiotic chromosome axes and is required for SC establishment [39] . ZEP1 is a component of the transverse filament of SC [40] . Both genes were transcribed normally even in mel2 flowers ( Figure S8 ) . In wild-type meiocytes at early zygotene , PAIR2 associates along meiotic chromosome axes , and filamentous ZEP1 signals begin to elongate between homologous axes ( n = 101 ) ( Figure 6A ) . In contrast , in all mel2 meiocytes ( n = 118 ) at early zygotene , neither PAIR2 nor ZEP1 was detected on chromosomes ( Figure 6B ) . These observations indicate that mel2 meiocytes fail to enter meiosis when the wild type undergoes early zygotene . At late zygotene in wild-type meiocytes , ZEP1 stretches extended overall meiotic chromosomes and most PAIR2 proteins had been removed from the axes , indicating the completion of homologous synapsis ( Figure 6C ) . In mel2 meiocytes , 79 . 7% of meiocytes ( n = 64 ) exhibited faint or abnormally dotted signals of PAIR2 in nuclei ( Figure 6D ) . All these meiocytes showed a soma-like centromere arrangement . However , the remaining 20 . 3% showed an early zygotene-like , filamentous appearance of PAIR2 and a meiotic centromere arrangement ( Figure 6E ) , whereas ZEP1 proteins , which failed to be loaded on homologous axes , accumulated aberrantly in the cytoplasm . It was impossible to observe whether the mutant PMCs emanating filamentous PAIR2 signals underwent subsequent meiotic steps , because of significant cell disruption due to hypervacuolation ( Figure 1I ) and apoptosis ( Figure 1J ) . These results strongly suggest that in mel2 mutant , most of male germ cells which show the defect in the premeiotic G1/S transition result in the lack of meiosis , even though meiotic genes have been normally expressed . The 20% cells could escape from the defect in the transition and enter early meiotic stages , but extremely later than usual , and yet they failed to establish the SC between homologous chromosome pairs until the apoptosis . The seed-sterile phenotype of mel2 homozygous plants was rescued by introducing transgenes expressing the MEL2 protein with a T7-peptide tag at the C-terminus ( C-tagged ) ( Figure S11 ) , indicating that this recombinant protein is functional in vivo . Unfortunately , we failed to obtain any clear signals of the T7-tagged protein in western blotting ( data not shown ) , probably because of the low level and spatiotemporal limitations of its expression . However , indirect immunofluorescence enabled to visualize the subcellular protein localization . In wild-type PMCs undergoing premeiotic mitosis , a faint signal was observed in the cytoplasm ( Figure 7A ) . In premeiotic interphase , signals were found in the cytoplasm , especially concentrated at the perinuclear region ( Figure 7B ) . By early meiosis I , MEL2 had been released to the cytoplasm of PMCs , and in turn , a faint signal appeared at the perinuclear cytoplasmic region of the inner anther-wall cells , including tapetal cells ( Figure 7C ) . MEL2 signals finally disappeared at post-meiotic stages of PMCs and anther-wall cells ( data not shown ) . This localization was observed in seven of eight anthers from two independent plants ( C6#2 , C9#2 in Figure S11 ) . The C-tagged MEL2 signal was excluded from the nucleoplasm in any of these stages . In transgenic plants expressing N-tagged MEL2 protein , the immunofluorescent signal diffused over all the cytoplasm in premeiotic PMCs ( Figure 7D ) . N-myristoylation is a post-transcriptional protein modification , in which myristic acid is covalently attached to an N-terminal glycine residue , exposed during cotranslational N-terminal methionine removal by N-myristoyltransferase [41] . Thus , the immunofluorescence of N-tagged MEL2 may represent the first methionine residue with the T7 tag that had been removed and diffused throughout the cytoplasm .
This study provides the first evidence that the novel RRM-containing protein plays essential roles in meiotic entry in rice . In the mel2 mutant , the progression of male and female meioses was significantly affected , and the male meiocyte and tapetal cells were hypervacuolated and directed to apoptosis ( Figure 1 , Figure S3 ) . The mel2 mutation disturbed the most germ cells to transit into the premeiotic S-phase in anthers ( Figure 4 and Figure 5 ) . However , twenty percents of the cells could enter the S-phase ( Figure 5B ) and undergo meiotic processes in which chromosomes showed a typical appearance of early zygotene ( Figure 6 ) , while the wild-type cells underwent late zygotene . In these cells , neither precocious separation of sister chromatids nor sister centromeres was observed . This result indicates that the MEL2 function might be excluded from the regulation of meiotic chromosome structure and cohesion , different from the function of Arabidopsis SWI1/DYAD . Thus , the role of rice MEL2 could be specified in the premeiotic cell-cycle control . The MEL2 function in premeiotic interphase will be tangible in comparison with that of maize AM1 , the coiled-coil protein also controlling meiotic entry . AM1 is implicated in the decision of germ cells being directed to meiosis or mitosis [6] . Interestingly , in the am1 mutant , ameiotic mitoses replacing meioses occur synchronously [7] , indicating that the synchrony of male meiosis is genetically separable from the meiosis commitment , and also that the AM1 function can be allocated into the meiosis commitment following the establishment of synchrony . In contrast , in the rice mel2 mutant , most of male germ cells could not enter the premeiotic S and lost the synchrony ( Figure 4 and Figure 5 ) . Thus , it is strongly suggested that the MEL2 function precedes the establishment of synchrony , the meiosis commitment and the function of maize AM1 . The ameiotic mitoses also occurred in the mel2 anther , but only in a small amount of male germ cells ( Figure 1G ) , probably representing that some of the PMCs would return to the mitotic cell cycle before the meiosis commitment . Taken together , we conclude that MEL2 plays an essential role in the premeiotic G1/S-phase transition in rice . Pawlowski et al . [6] proposed the existence of a novel checkpoint system monitoring faithful transition of leptotene to zygotene based on the degeneration phenotype of am1 mutant PMCs . Our results may support this proposal in rice . The mel2 mutant PMCs initiated hypervacuolation and apoptosis simultaneously when wild-type PMCs underwent early meiosis ( Figure 1 ) . This degeneration of meiocytes would be an indirect effect of the mel2 mutation , because it was never observed in the mel2 MMCs ( Figure 1K , 1L ) . In yeast and metazoans , a system referred to as pachytene checkpoint monitors for defects in homologous recombination and synapsis , and meiocytes arrested in pachytene will eventually be eliminated [42] . In contrast , plants are thought to lack the typical meiotic checkpoint [43] . This consideration has been based on most plant meiotic mutants being able to complete meiosis , while fragmentation or nondisjunction of chromosomes takes place . However , most plant materials examined so far are thought to have been mutated in meiotic machinery , but not in premeiotic events . In turn , the function of maize AM1 and rice MEL2 is supposed in premeiotic events , in contrast to the meiotic mutants previously reported in plants . In this study , we mainly focus on male meiosis , because in rice , it is easier to be observed than female meiosis . However , the mel2 mutation also affected the progression of female meiosis ( Figure 1K , 1L ) . The fundamental role of MEL2 might be in the initiation of premeiotic G1/S transition in the appropriate timing in both male and female cells . The central region of MEL2 protein resembles human DAZAP1 , whereas MEL2 possesses a single RRM , in contrast to the doublet in DAZAP1 ( Figure 2 ) . DAZAP1 is a member of the proline-rich RNA-binding proteins ( PRRPs ) [24] , and also of the heterogeneous nuclear ribonuclear proteins ( hnRNPs ) , known to bind to newly synthesized RNA transcripts and participate in their processing and export [44] . A role of human DAZAP1 in transcription is suggested by its specific exclusion from the transcriptionally inert XY body in the nuclei of pachytene spermatocytes , and a requirement for active transcription for its nuclear localization [45] , [46] . Mouse DAZAP1 is also detected in the nucleus of pachytene spermatocyte , and its localization dramatically shifts from the nucleus to the cytoplasm during the maturation of spermatids [45] , [47] . In male dazap1 mutant mice , spermatogenesis is arrested before the first meiotic division , and the cells are directed to apoptosis , whereas the female has largely normal oogenesis [48] . Human DAZAP1 is an interacting counterpart of DAZ and DAZL proteins [24] . DAZ , DAZL and BOULE are able to form an RNA-protein complex with another RNA-binding protein , PUM2 , while they may function in distinct molecular complexes during germ cell development [49] , [50] . In addition , yeast two-hybrid screening of testis proteins revealed that human DAZ interacts with DZIP3 ( DAZ-interacting protein3 ) /hRUL138 [49] , which has the potential for RNA binding and RING E3-ubiquitin ligase . DZIP3/hRUL138 is expressed ubiquitously in various tissues , and is localized to certain cytoplasmic structures , especially perinuclear regions , but excluded from the nucleoplasm [51] . Thus , in the mammalian system , RNA-binding proteins , such as DAZ , DAZL , DAZAP1 and PUM2 , first associate with the target mRNA precursors in the nucleus of germline cells . They export the mature targets to the cytoplasm , form a complex with ubiquitously expressed cytoplasmic proteins , such as DZIP3/hRUL138 , on the cytoplasmic nuclear membrane or endoplasmic reticulum , and regulate the translation of target mRNAs . Rice MEL2 localized the perinuclear region , but it was excluded from the nucleoplasm of germ cells , distinct from mammalian DAZ families ( Figure 7 ) . We hypothesize that MEL2 may be a hybrid form of a DAZAP1-like protein and a DAZ-interacting E3 ligase , such as DZIP3/hRUL138 , and may have evolved to acquire a germline-specific function in ancestral monocots . This idea raises the possibility that RING E3 ligase-dependent ubiquitination is required for germline development commonly in eukaryotic species . It is also suggested that unknown DAZ-family proteins that transport the target mRNAs from the nucleoplasm to the cytoplasm exist in plant germline cells . The Arabidopsis locus At5g57740 or XBAT32 encodes a MEL2-like protein composed of ANK and RING motifs at the N- and C-termini , respectively , but not of RRM , and it promotes lateral root formation by inhibiting ethylene biosynthesis [52] , [53] . XBAT32 is expressed ubiquitously in various Arabidopsis tissues , but most abundantly in anthers . We hypothesized that domain-shuffling events occurred between an RRM protein and a XBAT32-like protein required for meiotic entry in ancestral monocots after the monocot-dicot divergence around 200 million years ago [54] . In the mel2 mutant , synchronous progression of premeiotic S-phase was completely disrupted ( Figure 4 and Figure 5 ) . This mel2 phenotype indicated that the genetic system controlling the premeiotic G1/S-phase transition would closely relate to the system terminating the premeiotic mitosis and establishing the synchronous progression of premeiotic- and meiotic-cell cycles in the rice anther . Figure 7F summarizes a transition of subcellular localization of MEL2 protein . It is plausible by analogy with the mammalian DAZ system that the perinuclear localization of MEL2 functions in the translational inhibition of some cell-cycle related gene ( s ) , cooperating with the perinuclear translational machineries . MEL2 may temporarily arrest the progression of asynchronous germ-cell cycles at premeiotic G1 end or the onset of S-phase at the perinuclear region , and the synchronous release of MEL2 to the cytoplasm allows the cells to enter premeiotic S-phase synchronously within an anther . The identification of binding substrates of MEL2 will contribute to evidence this hypothesis . According to this idea , unknown signalling factor ( s ) should be hypothesized to mediate cell-cell communication and promote the synchronous release of MEL2 from the perinucleus . During premeiotic interphase and early meiosis , it is known that male meiocytes form a single coenocyte in an anther locule , in which the cytomictic channels connect each other of the cells [55] . This channel network may help the signalling for synchrony of male meiosis . In addition to failure of meiotic entry , the mel2 mutation caused the hypervacuolation and hypertrophy of tapetal cells ( Figure 1I ) . This is different from the case of the maize am1 mutant , in which no tapetal-cell degeneration has been reported [7] , [9] . Tapetal cells provide nutrients and pollen-wall materials to microspores , and degenerate , probably by PCD [56] . It is demonstrated that several gibberellin ( GA ) -related rice mutants display a hypertrophy of tapetal cells and result in male sterility [57] . This hypertrophic phenotype is attributed to the absence of PCD in the tapetum , because externally supplied GA can restore the tapetal phenotype of the oscps1 mutation , which causes defects in GA biosynthesis . The hypertrophic tapetum in the GA-related mutations seems to resemble that in the mel2 mutation . However , as opposed to GA mutants , apoptosis identified by the strong TUNEL signal arose in the mel2 tapetum ( Figure 1J ) . In addition , GA-related mutants can undergo meiosis and produce tetrad spores [57] , distinct from the mel2 mutant . These observations may suggest that mel2-dependent hypertrophy of the tapetum is independent of the GA-signalling pathway . Both MEL2 mRNA and protein were expressed weakly in tapetal cells ( Figure 2 and Figure 6 ) . Thus , tapetal degeneration in mel2 anthers would be a primary effect of the absence of MEL2 protein , while it is difficult to neglect the possibility that degeneration of PMCs directly causes tapetal-cell hypertrophy . MEL2 expression in tapetal cells appeared during early meiosis I ( Figure 7 ) . Tapetal cells are known to become multinucleate or polyploidized by mitoses without cytokinesis , in many cases during meiotic I prophase [58] . In rice , tapetal cells become binucleated , and in Arabidopsis , binucleation occurs synchronously at early leptotene . Thus , MEL2 function may be required not only for meiotic entry , but also for synchronous tapetal-cell binucleation , the disruption of which may induce hypertrophy and precocious tapetal-cell death . However , the synchronous expression of H4 among tapetal cells was frequently observed even in mel2 anthers ( Figure 4H ) . It remains unclear whether this result excludes MEL2 function from the synchronization of tapetal-cell division . In conclusion , we have proved that the RRM protein plays an essential role in plant germ-cell development in addition to yeast and metazoans , although the protein's structure , function , timing of expression , and subcellular localization differ between rice and non-plant species . This study also suggests that genome shuffling and the generation of a novel motif combination in ancestral monocots may have brought rice MEL2 a unique function in germline cell-cycle control . Further analysis of MEL2 function will contribute to better understanding of post-transcriptional or post-translational regulation of plant germ-cell development , and also to elucidating similarities and differences in reproduction systems between plants and other species .
Seed-sterile mutant lines were selected as described [25] . For cytological and expression analyses , the F2 plants four-times backcrossed with cv . Nipponbare ( BC4F2 ) were used . Non-transgenic plants were grown in a field in the city of Mishima , Shizuoka , Japan . Transgenic plants were grown in the growth chamber , LPH-2HCT ( NK system ) , at 30°C for 14 hrs with the light and at 25°C for 10 hrs in dark . The linkage relationship between the sterile phenotype and transposed Tos17 fragments was analyzed by DNA gel blot hybridization and polymerase chain reaction ( PCR ) using the R3 population of 188 plants segregating the mel2 seed-sterility . DNA extraction , DNA gel blotting , cloning and isolation of the Tos17-tagged genome sequence were performed as described [25] . PCR genotyping for the mel2 mutant populations was performed using the mixture of three primers: 868 , 869 and T17LTR4MF for mel2-1 allele , or 870 , 871 and T17LTR4MF for mel2-2 allele ( Table S1 ) . 50- to 100-ng genomic DNAs and above three primers in 5-µL water were added to the same volume of GoTaq Green Master Mix ( Promega ) . The longitudinal length of flower buds and anthers was measured under the dissection microscopy SMZ645 ( Nikon ) . The anther length is generally used as a criterion to determine developmental stages of germline cells in rice [59] . This criterion was also used in this study , because the anther length was increased proportional to longitudinal flower ( or lemma ) length , whose elongation was unaffected by mel2 mutation , until the end of meiosis ( Figure S12 ) . A precise stage in each flower or anther was determined by the mRNA expression or immunofluorescence of stage-specific gene or protein markers . The full-length MEL2 cDNA was obtained from 3 . 0-cm young rice panicles , frequently including flowers in premeiosis . RNA extraction and RACE reaction were according to the methods as described [26] . In addition to the oligo ( dT ) 20 primer , two MEL2 gene-specific , antisense primers , 871 and T2028R were used for three rounds of RTs , followed by the RACE-PCR with adaptor primers ( AP1 and AP2 ) supplied by the manufacturer ( Table S1 ) . All products were cloned into pCR-BluntII-TOPO vector ( Invitrogen ) , and sequenced by Dye Terminator Cycle Sequencing kit and ABI PRISM 3130xl Sequencer ( Applied Biosystems ) . Three independent RACE fragments were combined into a single , full-length cDNA sequence by PCRs . The entire coding region of the MEL2 gene and its 2 . 0-kbp upstream cis-sequence from the putative transcriptional start site were included within the 10-kbp of single SalI genomic fragment ( Figure S4 ) . The 10-kbp fragment was isolated from the rice BAC clone OSJNBa0036A19 , and subcloned into the pPZP2H-lac binary vector [60] . This plasmid or the empty vector as a negative control was introduced into mel2-1 homozygous calli in accordance with the method as described [61] . The genotype of calli was determined by PCR , in which the template DNA was extracted from young shoots germinating on the callus-induction medium . A sequence of the full length MEL2 cDNA was supplied for the BLAST search on RAP-DB ( http://rapdb . dna . affrc . go . jp/ ) , and we found Os12g0587100 locus ( MEL2like ) homologous to MEL2 gene within rice genome . Genomic sequences of MEL2 coding region and MEL2-like locus were compared by HarrPlot program [62] . Specific primer sets for MEL2-like , TMEL2L1402F/TMEL2L1974R and 919/TMEL2L1974R were designed as referencing HarrPlot information and used for RT-PCR against the RNA extract from young flowers . Then we succeeded to amplify the MEL2-like transcript , in which the putative intron sequences were spliced out when compared with the genomic sequence . Histological analysis of rice reproductive organs was done by using the plastic- embedded sections , the preparation method of which was described [25] . Sections were stained with toluidine blue ( Chroma Gesellshaft Shaud ) or provided for the TUNEL assay and other immunofluorescent analyses . TUNEL was performed as described previously [56] . Plastic-embedded sections of rice panicles and flowers were treated with TUNEL apoptosis detection kit ( DeadEnd Fluorometric TUNEL system , Promega ) according to the manufacturer's instruction . The fluorescent TUNEL signal was detected by FV300 CLSM system and Photoshop . Electron microscopic observation was done in accordance with the method described previously [39] . In situ hybridization against rice tissues was performed in accordance with the method as described [25] . To avoid a cross hybridization among highly homologous gene families , we adopted the high-stringency condition with 0 . 3 M NaCl and 50% formamide at 50°C for hybridization and 0 . 5xSSC at 50°C for wash . For the synthesis of RNA probes , two short ∼500-bp DNA fragments were amplified by PCRs of the MEL2 cDNA with the primer sets , 919/1034 and 1035/1036 , respectively ( Table S1 ) . Both fragments were cloned into the pCRII-TOPO vector ( dual promoter system ) ( Invitrogen ) , and transcribed to make antisense or sense RNA probes by SP6 or T7 promoters with DIG RNA labeling kit ( Roche ) . Three PCR fragments against OsCDKB2;1 cDNA were amplified by primer sets of M486F/M718R , M415F/M537R , and M609F/M739R , respectively , and cloned into pCRII-TOPO . The full-length 583-bp cDNA of rice histone H4 ( RAP-DB: Os09g0553100 ) was cloned into pBluescript SK- ( Stratagene ) . Both plasmids were provided for the synthesis of RNA probes as in MEL2 . Fresh young panicles of 3–5 cm in length were curt from stems and placed in 100 µm BrdU solution in the dark for 4 hours . Plastic sectioning and detection of incorporated BrdU were done in accordance with the method described previously [39] . Before the immunization , ten minutes treatment of sections with Proteinase K ( 0 . 1 mg/mL , Sigma ) often improved the accessibility of antibodies and the intensity of anti-T7-signals . To investigate the MEL2 expression profile , total RNAs were extracted from various tissues of wild-type rice plants; embryo and endosperm from mature seeds , seedlings , shoot apices , leaf blades , leaf sheathes , roots , flag leaves , 1 cm young panicles , young flowers in 1–2 mm , 2–4 mm and 2–7 mm lengths , and mature flowers . All tissues were cut off and handled with forceps , and immediately transferred into microtubes filled with liquid nitrogen and stored at −80°C . RNeasy Plant Mini kit ( QIAGEN ) was used for RNA extraction . Total RNAs were reverse-transcribed with the oligo ( dT ) 20 primer and SuperscriptIII reverse-transcriptase ( Invitrogen ) , and provided for semi-quantitative RT-PCR . For MEL2 mRNA , the primers , 918/919 , were used . To investigate the structure of MEL2 transcript in the mel2 mutant , the primer sets , 918/919 and 868/869 , were used . An expression of rice meiotic genes was examined in the mel2 mutant flowers by using the following primer sets; 496/647 for PAIR1 , 555/ 518 for PAIR2 , and K180/K183 for ZEP1 . The primer set ActinF/ActinR was used to amplify rice Actin cDNAs as a positive control . Indirect immunofluorescent staining of rice meiocytes was performed in accordance with the method as described [39] with minor modifications . Rat anti-ZEP1 and rabbit anti-POT1 antibodies ( Komeda , Kurata , and Nonomura , unpublished ) were diluted in 1/1000 and 1/3000 , respectively , and detected with AlexaFluor647 goat anti-rat IgG ( Molecular Probes ) and Cy3 goat anti-rabbit IgG ( Amersham ) . Maximum four channels of fluorescent signals were simultaneously observed by Fluoview FV300 CLSM system , upgraded with LD405/440 laser unit ( Olympus ) . Captured images were enhanced and pseudo-colored by Photoshop CS2 software ( Adobe ) . Plasmid constructions to produce T7 ( MASMTGGQQMG ) -tagged MEL2-expressing plants were based on the 10-kbp genomic SalI-fragment same in the complementation test . The 10-kbp SalI fragment ( Figure S1 ) was subcloned into pT7Blue vector ( Novagen ) . To add the T7 tag to the N-terminus of MEL2 , the 476-bp fragment including the translational start site was amplified with the primers MEL2gApaI2F/MEL2gNotI2R , directly cloned into pCR-BluntII-TOPO vector ( Invitrogen ) , and provided for site-directed insertion of T7-tag sequence by the inverted tail-to-tail direction PCR with primers MEL2T7NF/MEL2T7NR and for ligation as described [63] . This plasmid was again provided for PCR with MEL2gApaI2F/MEL2gNotI2R ( 476bp+T7 ) . The ApaI-NotI fragment of MEL2g/pT7Blue plasmid was replaced to the 476bp+T7 fragment by In-Fusion Advantage PCR Cloning Kit ( Clontech ) . Finally , the insert carrying the T7 tag was cut out with SalI and inserted into SalI site of the binary vector pPZP2H-lac [59] . To add the T7 tag to the C-terminus , the middle 4-kbp and the 3′-terminal 400-bp fragments of MEL2 genome were amplified with primer sets , MEL2gSmaIF/MEL2InFu1R , and MEL2InFu1F/MEL2ctransEndR , respectively . The latter 400-bp fragment was cloned into pCR-BluntII-TOPO vector , and provided for site-directed insertion of T7-tag sequence just in front of MEL2 stop codon ( 400bp+T7 ) . The plasmid used for above complementation test was digested with SmaI to remove the latter half 5 . 5-kbp genomic fragment . The rest sequence , including pPZP2H-lac and the first half of MEL2 gene , was fused with the middle 4-kbp and the 400bp+T7 fragments by In-Fusion Cloning Kit . Two resultant binary plasmids , the N-tagged MEL2 plasmid ( MEL2gT7N/pPZP2H-lac ) and the C-tagged one ( MEL2gT7C/pPZP2H-lac ) , were introduced into mel2/mel2 calli , and transgenic plants were regenerated according to the method as described [60] . Immunocytology was done by using plastic sections of transgenic anthers in accordance with indirect immunofluorescense above mentioned , with goat anti-T7 antibody ( Bethyl Laboratory ) as a primary antibody and AlexaFluor488 donkey anti-goat IgG ( Molecular Probes ) for detection . | Meiosis is a pivotal event to produce haploid spores and gametes in all sexually reproducing species and is a fundamentally different type of cell cycle from mitosis . Thus , the molecular mechanisms to switch the cell cycle from mitosis to meiosis have been studied by many researchers . In yeast and metazoans , RNA-binding proteins are known to play important roles in the post-transcriptional regulation of genes implicated in the meiotic entry and meiosis . In contrast , in the plant kingdom , the mechanisms to control the meiotic entry have largely remained elusive . In this study , we discover a novel RNA-recognition-motif ( RRM ) protein in rice ( Oryza sativa L . ) , designated MEL2 , and demonstrate that MEL2 is required for the faithful transition of germ cells from mitosis to meiotic cell cycle . Rice MEL2 shows partial similarity with human DAZAP1 , which is an RRM protein and relates to Azoospermia syndrome in human , while there are critical structural differences between germline-specific RRM proteins of mammals and plants . Our findings will lead the molecular-biological studies of plant meiotic entry to the next steps and will enable a comparison of the systems of meiotic entry between animals and plants . | [
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... | 2011 | A Novel RNA-Recognition-Motif Protein Is Required for Premeiotic G1/S-Phase Transition in Rice (Oryza sativa L.) |
The cellular machinery required for the fusion of constitutive secretory vesicles with the plasma membrane in metazoans remains poorly defined . To address this problem we have developed a powerful , quantitative assay for measuring secretion and used it in combination with combinatorial gene depletion studies in Drosophila cells . This has allowed us to identify at least three SNARE complexes mediating Golgi to PM transport ( STX1 , SNAP24/29 and Syb; STX1 , SNAP24/29 and YKT6; STX4 , SNAP24 and Syb ) . RNAi mediated depletion of YKT6 and VAMP3 in mammalian cells also blocks constitutive secretion suggesting that YKT6 has an evolutionarily conserved role in this process . The unexpected role of YKT6 in plasma membrane fusion may in part explain why RNAi and gene disruption studies have failed to produce the expected phenotypes in higher eukaryotes .
Constitutive secretion delivers newly synthesised proteins and lipids to the cell surface and is essential for cell growth and viability . This pathway is required for the exocytosis of molecules such as antibodies , cytokines and extracellular matrix components so has both significant physiological and commercial importance . The majority of constitutive secreted proteins are synthesised at the endoplasmic reticulum , pass through the Golgi , and are transported to the cell surface in small vesicles and tubules which fuse with the plasma membrane [1 , 2] . Constitutive secretory vesicles are not stored within the cell and do not require a signal to trigger their fusion with the plasma membrane which is in contrast to dense core secretory granules or synaptic vesicles [3 , 4] . In some cell types , such as MDCK cells and macrophages , there is evidence that constitutive secretory cargo passes through a endosomal intermediate on its way to the cell surface [5 , 6] . However , in non-polarised cells endosomal intermediates do not appear to play a major role in this pathway [7] . Vesicle fusion is driven by a family of molecules known as SNAREs . SNARE are generally small ( 14-42kDa ) , C-terminally anchored proteins that have a highly conserved region termed the SNARE motif that has the ability to interact with other SNAREs [8 , 9] . For membrane fusion to occur , SNAREs on opposing membranes must come together and their SNARE motifs zipper up to form a SNARE complex [10 , 11] . Detailed characterisation of the neuronal SNARE complex ( syntaxin 1A/VAMP2/SNAP25 ) required for synaptic vesicle fusion has provided a mechanistic framework for understanding the function of SNAREs [4 , 12 , 13] . There are 38 SNAREs encoded in the human genome and they can be classified as either R or Q-SNAREs depending on the presence of a conserved arginine or glutamine in their SNARE motif [14–16] . Q-SNAREs can be further subdivided into Qa , Qb and Qc SNAREs based on their homology to syntaxin and SNAP25 . A typical fusogenic SNARE complex will contain four SNARE motifs ( Qa , Qb , Qc and R ) [17] . Qbc-SNAREs such as SNAP23 , 25 , 29 and 47 contribute two SNARE motifs to the SNARE complex . R-SNAREs can also be further classified as either longin or brevin type SNAREs . Longin type R-SNAREs contain a longin type fold and are found in all eukaryotes and while brevin type SNAREs are less widely conserved across species [18] . Over the past twenty years significant progress has been made defining the SNARE complexes required for the majority of intracellular transport steps within eukaryotic cells ( reviewed in [19–23] ) . In addition , there are an increasing number of examples where the SNARE complexes required for the secretion of specific cargo such as Wnt , TNF and IL-6 have been identified [24–26] . However , these proteins are not delivered directly to the cell surface from the TGN but pass through an endosomal compartment . Many labs , including our own , have attempted to identify the machinery which drive the fusion of constitutive secretory vesicles with the plasma membrane and on the whole very little progress has been made [27–34] . This in part may be due to the fact that there are multiple routes to the cell surface from the Golgi and redundancy in the fusion machinery . If we just consider the R-SNAREs , the human genome encodes seven post-Golgi SNAREs ( Table 1 ) and a typical mammalian cell line can express at least five R-SNAREs so disruption of just one R-SNARE is unlikely to block secretion if they are functionally redundant [15 , 27] . To overcome this problem we have decided to analyse SNARE function in Drosophila cells as they have a simpler genome with less redundancy . The Drosophila genome encodes 26 SNAREs with 16 of them predicted to be localised to post-Golgi membranes based on their homology to mammalian SNAREs [14 , 15] . The complexity is reduced even further as Drosophila cell lines just express two post-Golgi R-SNAREs , Syb and VAMP7 ( based on publically available microarray data generated by the modENCODE project ) [35] . In this study , we have developed a novel , quantitative assay for measuring constitutive secretion based on a reporter cell line that can be effectively used to monitor secretion by flow cytometry , immunoblotting and fluorescence microscopy . Depletion of known components of the secretory pathway in Drosophila cells ( STX5 , SLH and ROP ) causes robust blocks in ER to Golgi and Golgi to plasma membrane transport , therefore validating this approach . As predicted , there is redundancy in the post-Golgi SNAREs and multiple SNAREs must be depleted to obtain robust blocks in secretion . We have detected strong negative genetic interactions between Drosophila STX1 and STX4 , SNAP24 and SNAP29 , STX1 and Syb , and SNAP24 and Syb . We have also detected a novel and unexpected genetic interaction between Syb and YKT6 . Depletion of YKT6 and VAMP3 in mammalian cells also causes a robust block in secretion indicating that this negative genetic interaction is conserved across species and provides evidence that these two R-SNAREs function in the late secretory pathway .
We previously used a ligand-inducible reporter system to measure constitutive secretion in mammalian cells [27 , 36] . This system utilizes a GFP-tagged reporter construct ( cargo ) that is retained in the ER until the addition of a small molecule ( AP21998 or D/D solubiliser ) , which causes the cargo to exit the ER in a synchronous pulse ( Fig 1A ) . The transport of the cargo can be monitored using flow cytometry , microscopy and immunoblotting . The cargo contains a furin cleavage site so changes in its molecular weight can be used to determine if it has reached the trans-Golgi network ( TGN ) , where the furin endoprotease normally resides . We have moved this reporter system into Drosophila S2 cells and generated a clonal cell line ( C3 ) . C3 cells have similar secretion kinetics to mammalian cells and secrete approximately 80% of their cargo in 80 minutes ( Fig 1B ) [27] . To validate the C3 cells we used RNAi to deplete the Drosophila orthologues of syntaxin 5 ( STX5 ) and Sly1 ( SLH ) , genes previously shown to be essential for ER-Golgi transport in human cells [27 , 37 , 38] . Amplicons to both of these genes were designed using FLYBASE , synthesised and transfected in to C3 cells . The mRNA level for both STX5 and SLH were reduced by over 80% as determined by qRT-PCR ( S1 Fig ) . Depletion of STX5 and SLH cause a significant block in biosynthetic transport as almost no cargo is secreted from the cells as determined by flow cytometry ( Fig 1C and 1D ) and immunoblotting ( Fig 1E ) . Similar results were obtained using alternate amplicons indicating that the observed block in secretion is not due to off-target effects ( S1 Table ) . In the STX5 depleted cells the trapped cargo is found in the Golgi ( co-localisation with Golgi marker GM130 ) and reticular and tubular structures most likely the ER ( Fig 1F and S1 Fig ) . To determine whether the assay could be used to detect blocks in post-Golgi trafficking we depleted ROP , the Drosophila Sec1 homolog [39 , 40] and STX7/Avalanche an endosomal Q-SNARE . Immunoblotting for ROP and STX7 confirmed that both proteins were efficiently depleted ( Fig 1E ) . Depletion of ROP caused a significant defect in secretion , while depletion of the endocytic SNARE STX7 did not ( Fig 1C–1E ) . An alternate ROP amplicon give a similar phenotype indicating that the defect in secretion is not due to off-target effects ( S1 Table ) . In the ROP depleted cells , a significant proportion of the retained cargo has been furin-processed suggesting that it has reached a post-Golgi compartment ( Fig 1E , appearance of lower molecular weight band in GFP blot and accumulation of processed GH in the cells ) . In support of the biochemical data we observe cargo accumulating in small vesicular structures in the ROP depleted cells ( Fig 1F ) . These membranes are distinct from the Golgi ( GM130 negative ) and start appearing approximately 20 minutes after the induction of secretion suggesting that they may be post-Golgi transport carriers which have been unable to fuse with the plasma membrane . To determine which post-Golgi SNAREs mediate fusion of biosynthetic vesicles with the plasma membrane we depleted syntaxin 1 ( STX1 ) , syntaxin 4 ( STX4 ) and synaptobrevin ( Syb ) . These SNAREs are the closest homologs of the yeast genes SSO1/2 and SNC1/2 previously shown to mediate the fusion of biosynthetic vesicles with the plasma membrane [41 , 42] . We depleted these SNAREs individually , or in combination and the knock down efficiency was determined by immunoblotting and RT-PCR ( Fig 2B and S2 Fig ) . Depletion of STX1 or Syb leads to a partial block in secretion while depletion of STX4 had no effect ( Fig 2A and 2C ) ( S2 Fig ) . Depletion of STX1 or Syb leads to a similar phenotype to that observed with ROP knock down , where furin-processed cargo is retained inside the cell ( Fig 2B ) . The block in secretion became more pronounced when STX1 and STX4 , or STX1 and Syb were depleted in combination indicating a negative genetic interaction between these genes . In the STX1-STX4 depleted cells the retained cargo is found in small vesicular structures scattered throughout the cytoplasm ( Fig 2D ) . No genetic interaction was detected between STX4 and Syb . The STX1-Syb genetic interaction can be replicated using an alternative amplicons targeting Syb . Alternative amplicons targeting STX1 did not efficiently knockdown the protein ( S1 Table ) . As depletion of Syb did not produce a complete block in secretion it was possible that another R-SNARE might be able to substitute for the loss of Syb . To address this we used publically available microarray data to determine which Drosophila R-SNAREs are expressed in S2 cells ( modENCODE project ) . The R-SNAREs Syb , VAMP7 , YKT6 and Sec22b are expressed in S2 cells , but not the neuronal R-SNARE n-Syb . This is consistent with previous studies indicating that n-Syb is exclusively expressed in neuronal tissue [43] . We depleted the R-SNAREs individually or in combination and determined the knock down efficiency by immunoblotting ( Fig 3B ) . Depletion of Syb , YKT6 and Sec22b caused a partial block in secretion as determined by flow cytometry and immunoblotting of the cargo ( Fig 3A–3C ) ( S3 Fig ) . Depletion of Syb or YKT6 causes retention of furin-processed cargo indicating a late block in secretion ( Fig 3B , GFP and GH blots ) . This block became more severe when YKT6 and Syb were depleted in combination . The level of block was comparable to that observed when STX5 is depleted as almost no processed GH was detected in the media ( Fig 3B , GH media blot ) . In support of the role of Syb and YKT6 in the fusion of secretory carriers with the plasma membrane we observe an accumulation of secretory carriers in cells depleted for both of these genes ( Fig 3D ) . The observed genetic interaction between Syb and YKT6 are not due to off-target effects as they can be reproduced using alternate amplicons targeting both genes ( S1 Table ) . Importantly , no genetic interaction was detected between the R-SNARE Sec22b and Syb indicating that YKT6-Syb interaction is specific and not due to general toxicity ( Fig 3A–3C ) . In support of YKT6 having a role in the fusion of secretory carriers with the plasma membrane we were able to immuoprecipitate YKT6 in a complex with STX1 from S2 cells ( Fig 3E ) ( Table 2 ) . In S . cerevisiae , it has previously been reported that YKT6 and Sec22 function redundantly in ER to Golgi transport [44] . To determine if this is also the case in Drosophila cells we depleted YKT6 and Sec22b individually and in combination ( Fig 4B ) . As in S . cerevisiae , we see a robust block in constitutive secretion when YKT6 and Sec22b are depleted in combination ( Fig 4A and 4C ) ( S4 Fig ) . The level of inhibition is very similar to that seen when STX5 is depleted . In the Sec22b/YKT6 depleted cells the cargo is trapped in the ER and has failed to reach the Golgi ( Fig 4B and 4D ) . This is in contrast to what is observed when YKT6/Syb are depleted where there is an accumulation of furin processed cargo ( Fig 4B ) . We also depleted YKT6 in combination with STX1 and STX4 . No genetic interaction was detected between YKT6 and STX1 or YKT6 and STX4 ( Fig 4A and 4C ) . Our data suggests that the Qa-SNAREs STX1/4 and the R-SNAREs Syb and YKT6 mediate the fusion of secretory carriers with the plasma membrane . A canonical SNARE complex also requires Qb and Qc SNARE domains , often provided by a Qbc-SNARE . The Drosophila genome encodes three SNAP genes: SNAP24 , 25 and 29 ( ubisnap ) [15] . Only SNAP24 and SNAP29 are expressed in S2 cells based on publically available microarray data ( modENCODE project ) . We depleted SNAP24 and SNAP29 individually or in combination and validated the knock down for SNAP29 using immunoblotting ( Fig 5B ) . Depletion of SNAP24 or SNAP29 did not block secretion of the reporter construct . However , depletion of SNAP24 and SNAP29 in combination caused a significant block in secretion ( Fig 5A–5C ) ( S5 Fig ) , similar to that seen when ROP is depleted . Similar results were obtained using alternate amplicons indicating that the observed block in secretion is not due to off-target effects ( S1 Table ) . In the SNAP24-SNAP29 depleted cells a significant amount of furin-processed cargo is retained within the cells suggesting a late block in secretion ( Fig 5B , GH and GFP blots ) . In support of the biochemical data we observe an accumulation of secretory carriers is the cells depleted for both SNAP24 and SNAP29 ( Fig 5D ) . Consistent with SNAP24 having a role in the fusion of secretory vesicles with the plasma membrane we were able to immunoprecipitate SNAP24 in a complex with STX1 ( Fig 3E ) ( Table 2 ) . We also investigated the effect of depleting SNAP24 and SNAP29 in combination with Syb . Depletion of SNAP24 and Syb in combination caused a robust block in secretion ( Fig 5A–5C ) . The retained cargo was furin-processed indicating a late block in secretion ( Fig 5B , GH and GFP blots ) . As in the SNAP24-SNAP29 knock down the cargo accumulated in small transport vesicles which did not co-localise with the Golgi ( Fig 5D ) . No genetic interaction was detected between Syb and SNAP29 . Depletion of SNAP29 in combination with SNAP24 and Syb did not cause a stronger block in secretion . In support of these observations we obtained similar results using alternate amplicons ( S1 Table ) . We have uncovered an unexpected role for YKT6 in the fusion of biosynthetic vesicles with the plasma membrane in Drosophila cells . We next sought to determine if human YKT6 has a similar role . We have previously shown that combinatorial depletion of the human post-Golgi R-SNAREs VAMP3 , 4 , 7 , and 8 does not block secretion in HeLa cells [27] . We depleted VAMPs 3 , 4 , 7 , 8 , and YKT6 individually or combination and determined the effect on secretion using our mammalian reporter line ( HeLa-M C1 ) . As previously reported depletion of VAMPs 3 , 4 , 7 and 8 individually causes little retention of the secretory cargo ( Fig 6A and 6B ) . However , depletion of YKT6 causes partial retention of the cargo consistent with our previous results [27] . As in Drosophila cells combinatorial depletion of YKT6 and VAMP3 causes an almost complete block in secretion . This genetic interaction is specific because no interaction was detected with either VAMP4 or VAMP7 . To investigate the specificity of the genetic interaction further we depleted the R-SNARE Sec22b in combination with YKT6 or VAMP3 ( Fig 6A and 6B ) . As observed in Drosophila cells ( Fig 3A–3C ) no genetic interaction was detected between Sec22b and VAMP3 suggesting that the observed phenotype when Syb and YKT6 are depleted is not simply caused by a general defect in trafficking or toxicity . As in S . cerevisiae and the Drosophila cells we detect a strong negative genetic interaction between Sec22b and YKT6 [44] .
The aim of this study was to identify the SNAREs required for the fusion of constitutive secretory carriers with the plasma membrane in higher eukaryotes . To address this we have developed a simple and robust assay for measuring secretion in Drosophila cells . Using well characterised targets ( STX5 , SLY1 and ROP ) we have validated the system and have shown that the assay is capable of differentiating blocks in ER to Golgi and Golgi to plasma membrane transport based on proteolytic processing and accumulation of the secretory cargo in post-Golgi transport vesicles . Our experimental data suggests that there are at least three fusion complexes operating at the Drosophila PM ( Fig 7A ) . The first complex consists of STX1 , SNAP24/29 and Syb . The second complex consists of STX4 , SNAP24/29 and Syb . The third complex consists of STX1 , SNAP24 and YKT6 . The reason we have excluded the possibility of a STX4 , SNAP24/29 , YKT6 complex is because depletion of both STX1 and Syb led to a complete block in secretion . Indicating that STX4 and YKT6 are unable to form a SNARE complex that can substitute for the loss of STX1 and Syb . Genetic interaction data also suggests that SNAP29 is unable to substitute for the loss of SNAP24 under conditions when both SNAP24 and Syb are depleted . This data suggests that the third SNARE complex specifically consists of STX1 , SNAP24 and YKT6 . At present it is unclear whether these SNARE complexes define parallel pathways to the plasma membrane or simply reflect the ability of these SNAREs to substitute with each other . The most striking observation in this study is that we have uncovered an unexpected role for YKT6 in the fusion of secretory carriers with the plasma membrane . Depletion of YKT6 and Syb/VAMP3 in combination causes a complete block in secretion and leads to an accumulation of post-Golgi transport vesicles within Drosophila cells . YKT6 is a lipid anchored R-SNARE that has been shown to function on many pathways including ER to Golgi transport , intra-Golgi transport , endosome-vacuole fusion , endosome to Golgi transport and exosome fusion with the plasma membrane [24 , 45–51] . YKT6 actively cycles on and off membranes in a palmitoylation dependant manner so potentially it is well suited to function on a wide variety of intracellular pathways [52] . Due to the promiscuous nature of YKT6 some caution must be taken when interpreting our functional data . It is possible that loss of YKT6 may be indirectly affecting post-Golgi transport and fusion at the plasma membrane . However , the simplest interpretation of our data is YKT6 is directly involved in this process as we are able to biochemically detect an interaction between YKT6 and STX1 . Using the knowledge obtained from the Drosophila system , we re-examined the role of R-SNAREs in constitutive secretion in mammalian cells . As previously reported , depletion of VAMP3 and other post-Golgi R-SNAREs did not perturb secretion in HeLa cells [27] . However , depletion of VAMP3 and YKT6 in combination caused a complete block in secretion . This data suggests that YKT6 and VAMP3 may be functioning in the fusion of secretory carriers with the plasma membrane in mammalian cells . We have made significant efforts to localise endogenous YKT6 and VAMP3 on post-Golgi secretory carriers . However , our attempts have been hampered by the fact the endogenus YKT6 is expressed at very low levels and over expressed YKT6 does not target correctly to membranes and remains cytoplasmic . As expected , there is redundancy in the Q-SNAREs required for the fusion of secretory carriers with the plasma membrane . However , it is clear that certain SNAREs have a more prominent role in this process . The main Q-SNAREs at the Drosophila plasma membrane are STX1 and STX4 ( share homology with SSO1 and 2 ) . Depletion of STX1 causes a partial block in secretion while depletion of STX4 does not . It is unclear why STX1 is the favoured Qa-SNARE . It could simply be that STX1 is more abundant than STX4 or has a higher affinity for the R-SNARE on the vesicle [53] . It may also reflect the route by which the synthetic cargo traffics to the cell surface . We have also observed redundancy between the Qbc-SNAREs SNAP24 and SNAP29 ( orthologues of Sec9 ) . We are only able to detect a complete block in secretion when both are depleted . It has previously been shown that SNAP29 interacts with STX1 . However , the complexes it forms are not SDS-resistant suggesting that they may not be fusogenic [54] . A potential problem with gene disruption and RNAi mediated depletion studies is compensation by other genes in the same family . For example , VAMP2 and 3 are upregulated in certain tissues of the VAMP8 knockout mouse and VAMP3 is upregulated in VAMP2 deficient chromafin cells isolated from VAMP2 null mice [55 , 56] . Based on our immunoblotting data we did not observe any compensation between R-SNAREs when they are depleted using RNAi in Drosophila cells ( Fig 3B ) . We also did not see any evidence of this in our previous work performed in HeLa cells [27] . We initially thought that STX1 and STX4 were being upregulated in STX5 and Syb depleted cells based on immunoblotting ( Figs 2B and 4D ) . However , when the samples were directly prepared in Laemmli sample buffer , rather than a TX100 based extraction buffer , no difference in the levels of these SNAREs was observed ( S2 Fig ) . It is possible that the change in extractability may be caused by an alteration in the localisation of the Q-SNAREs from TX100 insoluble micro-domains at the plasma membrane [57] . However , we have not tested this hypothesis . To directly assess changes in gene expression during the RNAi experiments we measured the mRNA levels several SNAREs using RT-PCR ( S2 Fig ) . Depletion of STX1 leads to an upregulation of STX4 and Syb . However , we did not observe a significant change in the protein level of these SNAREs by immunoblotting . Thus it is unclear how significant these changes are . In the future , it will be interesting to determine how the expression levels of SNAREs , which function on the same pathway , are co-ordinated and regulated . To validate our genetic interaction data we have interrogated a published S . cerevisiae proliferation-based genetic interaction map to determine if the yeast homologues share similar genetic interactions to those observed in Drosophila cells ( under the assumption that constitutive secretion is essential for growth ) [58] . We have detected negative genetic interactions between Drosophila STX1 and STX4 , STX1 and Syb , Syb and SNAP24 , SNAP24 and SNAP29 , YKT6 and Sec22b and Syb and YKT6 ( Fig 7C ) . Similar genetic interactions were also observed in S . cerevisiae indicating that the data generated from Drosophila cells is physiologically relevant and the genetic interactions are evolutionary conserved . Importantly the homologues of YKT6 and Syb/VAMP3 were also found to genetically interact in yeast ( YKT6 and SNC2 ) . In summary , we have identified the SNARE complexes required for the fusion of constitutive secretory vesicles with the plasma membrane in Drosophila cells . We have uncovered a novel role for YKT6 in the fusion of secretory vesicles with the plasma membrane which is conserved from yeast to man . This observation may in part explain why RNAi and gene disruption studies in higher eukaryotes have failed to yield the expected phenotypes . In the future , it should be possible to use our secretion assay in combination with SNARE depletion as a tool to further dissect the post-Golgi pathways involved in secretion and generate post-Golgi secretory carriers for proteomic profiling .
Rabbit polyclonal antibodies were raised against GFP and the cytoplasmic domains of Drosophila STX4 , SNAP29 , Syb , VAMP7 and Sec22b . The antibodies were affinity purified as in [59] . The rabbit polyclonal antibody against Drosophila STX7 was a generous gift from H . Krämer . The mouse monoclonal antibodies against Drosophila STX1 ( 8C3 , depositors Benzer , S . and Colley , N . ) , ROP ( 4F8 ) and Actin ( JLA20 , depositor Lin , J . J . -C ) were purchased from the Developmental Studies Hybridoma Bank [39] . The Rabbit polyclonal to Drosophila GM130 was purchased from Abcam . The mouse monoclonal to human growth hormone ( 2H81G10 ) was a generous gift from Genentech Inc . , . The rabbit polyclonal antibody that cross-reacts with Drosophila YKT6 was a generous gift from Jessey Hay [60] . Secondary antibodies for immunoblotting were purchased from Jackson ImmunoResearch Laboratories . Secondary antibodies for immunofluorescence microscopy were purchased from Invitrogen Molecular Probes . Drosophila D . mel-2 ( Invitrogen ) and C3 cells were maintained in Express Five® SFM media ( Invitrogen , ) supplemented with 100 IU/mL penicillin , 100 μg/mL streptomycin , and 2 mM glutamine ( Sigma-Aldrich ) at 25°C in an cooled incubator . Expression of the reporter construct in C3 cells was maintained by the addition of 5μg/mL Blasticidin ( PAA Laboratories ) . HeLa-M and C1 cells were grown in high glucose DMEM supplemented with 10% fetal calf serum , 100 IU/mL penicillin , 100 μg/mL streptomycin , and 2 mM glutamine ( Sigma-Aldrich ) at 37°C in a 5% CO2 humidified incubator . Expression of the reporter construct in C1 cells was maintained by the addition of 1 . 66μg/mL puromycin ( PAA Laboratories ) . siRNA transfections were performed as in Gordon et al . , 2010 . The sequence of the siRNA used in the experiments can be found in ( S2 Table ) . The reporter construct used to generate the C3 cell line was generated by subcloning the expression cassette from pC4S1-eGFP-FM4-FCS-hGH ( Ariad Pharmaceuticals ) into pAC-V5-His-A expression vector ( Invitrogen ) . 2μg of pAC-S1-eGFP-FM4-FCS-hGH was co-transfected with 50ng of pCoBLAST into 500 , 000 S2 cells using the TransFast transfection reagent ( Promega ) . A population of cells stably expressing the reporter construct was generated by the addition of 25 μg/mL blasticidin ( PAA Laboratories ) . The cells were selected for two weeks and then autocloned into a 96 well plate using a MoFlo Flow cytometer ( Beckman Coulter ) based on GFP fluorescence . We were initially unsuccessful in this process until we supplemented the media with 5% FCS and put two cells in each well of the plate . 96 well plates were sealed with Parafilm M ( Pechiney Plastic Packaging ) to minimize evaporation during cell culture . Positive wells were identified using fluorescence microscopy . Clonal cell lines were screened for their ability to efficiently secrete the reporter construct and Clone 3 cells chosen as they have the most uniform expression of the reporter construct . HA tagged Drosophila STX1 was generated using PCR and cloned into the copper inducible expression vector pMT/V5-HIS ( Invitrogen ) . A stable population of cells was generated by co-transfecting the plasmid with pCoBLAST and selected as above . Primers for generating dsRNA amplicons were designed using the Harvard Drosophila RNAi Screening Center database ( http://www . flyrnai . org ) or the GenomeRNAi database ( http://rnai2 . dkfz . de/GenomeRNAi ) . Amplicons were chosen which were predicted to have the fewest off-target hits . Primers sequences were copied verbatim from the websites and T7 sequences added to the 5’ end of both primers for each amplicon ( S2 Table ) . Primers were synthesized by Sigma Genosys . A cDNA library was generated from S2 cells and used as a template for amplicon synthesis . The cDNA library was made by purifying RNA from S2 cells using a QIAshredder and RNeasy Protect Mini purification kit; followed by cDNA synthesis using the QuantiTect Reverse Transcription kit ( Qiagen ) . The DNA template for the amplicons was generated using two rounds of PCR from the cDNA library . A sample of this DNA was sequenced to confirm that the correct target had been amplified . Double stranded RNA was synthesized using the DNA template and T7 Ribomax Express RNAi System ( Promega ) according to manufacturers’ instructions . The reaction was cleaned up using a DNAse and RNAse digestion step followed by column purification using the RNeasy Midi kit ( Qiagen ) . A small amount of the reaction was run on agarose gel to confirm that the amplicon was the correct size . The RNA concentration was determined using a Nanodrop spectrophotometer ( Thermo Scientific ) . Knock downs were performed by transfecting 20μg of dsRNA into 500 , 000 S2 cells using TransFast ( Promega ) . The cells were then analysed 96 hours post transfection . S2 cells were lysed and the RNA purified using a QIAshredder and RNeasy Protect Mini purification kit following the manufacturer’s instructions ( Qiagen ) . The mRNA levels of specific genes were quantified using the Taqman RNA-to-CT 1-Step Kit ( Applied Biosystems ) . Pre-designed sets of primers and FAM-labeled fluorescent probes designed against target genes were ordered from Applied Biosystems , and these were used according to manufacturers’ instructions ( S3 Table ) . qRT-PCR reactions were run on an Applied Biosystems 7900HT Fast Real-Time PCR System . To quantify knockdown efficiency , relative quantification was performed using the ΔΔCT method [61] . For a list of qRT-PCR primes used in this study please see ( S3 Table ) . Secretion of the reporter construct was induced in Clone 1 ( HeLa M ) or Clone 3 ( S2 ) cells by the addition of AP21998 ( Ariad Pharmaceuticals ) or D/D solubilizer ( Clontech ) . Following secretion , the cells were placed on ice for 10 minutes to halt vesicle trafficking . C1 cells were detached using cold EDTA-Trypsin solution ( PAA Laboratories ) for 2 hours on ice . C3 cells are semi-adherent so were detached with pipetting . The fluorescence of the cells was measured using a BD FacsCalibur equipped with an HTS 96-well sampling robot ( BD Biosciences ) . Live cells were gated using forward and side scatter and dead cell exclusion ( 2 μg/mL 7-AAD for clone 1 cells or 1 μg/mL PI for clone 3 cells ) ( Molecular Probes Invitrogen ) . A minimum of 2000 cells were analysed for each sample . FlowJo ( Treestar ) was used to calculate the geometric mean fluorescence for each sample . GraphPad Prizm ( GraphPad Software ) was used for generating statistics and graphs . Each sample is set up in duplicate . One sample receives AP21998 or D/D solubilizer and the other does not . The percentage of cargo remaining after secretion is then calculated by taking a ratio between the two samples . To measure secretion by immunoblotting , equal numbers of C3 cells were resuspended in fresh media containing AP21998 and incubated for 80 minutes at 25°C . Secretion was halted by cooling the cells to 4°C and the media and cells collected by centrifugation . The media and cells were solubilized in Laemmli sample buffer and separated using SDS-PAGE . Proteins were transferred overnight onto PVDF membranes using wet transfer conditions . The membranes were blocked using 5% milk , 1% Tween-20 in PBS and probed with antibodies against actin ( loading control ) and growth hormone . Secondary antibodies conjugated to HRP were used to detect the primary antibodies and Supersignal West Pico Substrate ( Pierce ) used to develop the blots . Super RX Medical X-Ray Film ( Fujifilm ) was used to capture the signal and densitometry performed using ImageJ software . To evaluate knock down efficiency , C3 cells were counted and equal numbers of cells collected by centrifugation . The cells were resuspended in TX100-based extraction buffer ( 100 mM NaCl , 5 mM MgCl2 , 50 mM Tris pH 7 . 4 , 1% TX100 ) , incubated for 15 minutes on ice , centrifuged at 15 , 000 g for 15 min at 4°C . The supernatants were normalised for protein concentration using the Bradford protein assay , ( BIO-RAD ) , boiled in reducing SDS sample buffer and separated by polyacrylamide gel electrophoresis . Antibodies against actin ( loading control ) and SNAREs were used to probe the membranes . To isolate HA-tagged syntaxin 1/SNARE complexes , cells were resuspended in lysis buffer ( 100 mM NaCl , 5 mM MgCl2 , 50 mM Tris pH 7 . 4 , 0 . 5% Igepal CA-630 ) with a complete protease inhibitor tablet ( Roche ) and incubated for 30 minutes . Insoluble material was removed by centrifugation at 5 , 000 rpm for 5 minutes and then followed by centrifugation at 50 , 000 rpm for 30 minutes . The lysate was then passed through a 0 . 2 μm syringe filter . Cleared lysate was incubated for two hours with anti-HA resin ( Roche ) . Following multiple wash steps , samples were eluted twice with one column volume of 1 mg/mL HA-peptide ( Roche ) and acetone precipitated . The samples were then solubilized in Laemmli sample buffer and separated using SDS-PAGE . The gel was stained using SYPRO Ruby ( Molecular Probes Invitrogen ) and the bands visulaised using a Typhoon Trio Variable Mode Imager ( GE Healthcare ) . The bands were excised using a scalpel blade and in-gel trypsin digestion performed . Analysis was performed using an AB Sciex 4800 MALDI TOF/TOF . The instrument is configured to acquire an MS spectrum between m/z 700 and 4000 . From these MS spectra 7 peptides above a predetermined s/n threshold are selected for fragmentation . The MS spectra of intact peptides are used to determine protein identity by peptide mass fingerprinting ( PMF ) using the MASCOT search engine ( NCBInr database 20/10/2010 , 12061831 sequences ) . For further confirmation , the MSMS spectra are used to perform fragment ion searches to determine peptide sequence but if they fail to yield any identifications , it may be because peptides above the s/n threshold gave poor fragmentation patterns . C3 cells were grown on 13 mm No . 1 round coverslips ( VWR ) coated with Concanavalin-A ( Sigma-Aldrich ) and allowed to adhere over night . Cells were incubated in the presence of AP21998 or D/D solubilizer for 80 minutes . Cells were then fixed and stained as described in [62] . Coverslips were mounted using ProLong Gold ( Molecular Probes Invitrogen ) and sealed with clear nail polish . Images were captured using either a 63x or 100x oil objective on a Zeiss Axioplan fluorescence microscope ( Zeiss ) equipped with a Hamamatsu Orca-R2 C10600 camera ( Hamamatsu Photonics ) , and SEDAT quad pass filter set ( Chroma ) . The brightness and contrast of microscopy images were adjusted using ImageJ ( NIH ) . | The constitutive secretory pathway delivers newly synthesised proteins and lipids to the cell surface and is essential for cell growth and viability . This pathway is required for the secretion of molecules such as antibodies , cytokines and extracellular matrix components so has both significant physiological and commercial importance . The majority of secreted proteins begin their journey at the endoplasmic reticulum , pass through the Golgi , and are transported to the cell surface in small vesicles/tubules which fuse with the plasma membrane . Surprisingly , the molecular understanding of this fusion step is still unclear and in higher eukaryotes it is not known which SNARE proteins drive this process . To address this problem we have developed a powerful , quantitative assay for measuring secretion and used it in combination with gene depletion studies in Drosophila cells . Using this assay we identified three SNARE complexes driving the fusion of secretory vesicles with the plasma membrane and uncovered an unexpected role for the R-SNARE YKT6 in this process . Using this knowledge we have re-examined the role of SNAREs in the fusion of secretory carriers with the plasma membrane in mammalian cells and have found that YKT6 has an evolutionarily conserved role in this process . | [
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"molecu... | 2017 | VAMP3/Syb and YKT6 are required for the fusion of constitutive secretory carriers with the plasma membrane |
Nucleotide changes in the AUTS2 locus , some of which affect only noncoding regions , are associated with autism and other neurological disorders , including attention deficit hyperactivity disorder , epilepsy , dyslexia , motor delay , language delay , visual impairment , microcephaly , and alcohol consumption . In addition , AUTS2 contains the most significantly accelerated genomic region differentiating humans from Neanderthals , which is primarily composed of noncoding variants . However , the function and regulation of this gene remain largely unknown . To characterize auts2 function , we knocked it down in zebrafish , leading to a smaller head size , neuronal reduction , and decreased mobility . To characterize AUTS2 regulatory elements , we tested sequences for enhancer activity in zebrafish and mice . We identified 23 functional zebrafish enhancers , 10 of which were active in the brain . Our mouse enhancer assays characterized three mouse brain enhancers that overlap an ASD–associated deletion and four mouse enhancers that reside in regions implicated in human evolution , two of which are active in the brain . Combined , our results show that AUTS2 is important for neurodevelopment and expose candidate enhancer sequences in which nucleotide variation could lead to neurological disease and human-specific traits .
Autism spectrum disorders ( ASDs ) are common ( 1/88 in the United States ) [1] childhood neurodevelopmental disorders known as pervasive developmental disorders ( reviewed in [2] ) . ASDs are highly heritable , signifying a substantial genetic etiology [3] . A balanced translocation involving the autism susceptibility candidate 2 ( AUTS2; GenBank NM_001127231 . 1 ) gene in a pair of monozygotic twins with ASD was the first to link this gene to autism [4] ( Figure 1 ) . Following this finding , thirty-six additional unrelated individuals with ASD , intellectual disability , or developmental delay were found to have distinct heterozygous structural variants disrupting the AUTS2 region [5]–[13] , four exclusively in noncoding regions [5] , [12] ( Figure 1 ) . Additional structural variants in AUTS2 , some of which are only intronic , were also shown to be associated with attention deficit hyperactivity disorder ( ADHD ) [14] , epilepsy [12] , [15] , dyslexia [11] , motor delay , language delay , visual impairment , microcephaly and others [12] . In addition , a genome-wide association meta-analysis study identified SNP rs6943555 within the fourth intron of AUTS2 to be the most statistically significant SNP associated with alcohol consumption [16] ( Figure 1 ) . These various AUTS2-associated phenotypes suggest this gene has an important neurological function . It is worth noting though that some individuals with disrupted AUTS2 and mental retardation or autism have additional , potentially non-neuronal phenotypes , such as hypotonia , short stature , urogenital abnormalities , and skeletal abnormalities [4] , [6] . In addition to AUTS2's role in neurological disease , it was also shown to be important for human-specific evolution . The first half of AUTS2 displayed the strongest statistical signal in a genomic screen differentiating modern humans from Neanderthals [17] . This is attributed to a stretch of 293 consecutive SNPs , only two of which are coding variants: ( a G to C nonsynonymous substitution at chr7:68 , 702 , 743 ( hg18 ) only in the Han Chinese and a C to T synonymous change in chr7:68 , 702 , 866 ( hg18 ) within the Yoruba and Melanesian populations ) . Other regions identified to have the most significant human-Neanderthal sweeps also include genes that are involved in cognition and social interaction , including DYRK1A , NRG3 and CADPS2 [17] , reinforcing our interest in AUTS2's role in cognition and human-Neanderthal differences . In addition , three different evolutionary conserved noncoding intronic regions in AUTS2 ( HAR31 , HACNS174 and HACNS369 ) have been found to be significantly accelerated when compared to primates in two different studies [18] , [19] ( Figure 1 ) . Combined , these data suggest that altered regulation of AUTS2 could be associated with human specific traits . The functional role of AUTS2 is not well known , although some studies have identified a putative role in transcriptional regulation during neuronal development . The predicted AUTS2 protein contains a PY motif , a putative WW-domain-binding region [4] present in various transcription factors , implying that AUTS2 may be involved in transcriptional regulation [6] . In humans , AUTS2 is expressed in the brain , including the neocortex and prefrontal cortex [4] , [20] . AUTS2 is also highly expressed in the skeletal muscle and the kidney , and in lower levels in the placenta , lung and leukocytes [4] . In the developing mouse , Auts2 is expressed in the forebrain , midbrain , hindbrain , olfactory bulb , olfactory epithelium , eye , neural tube and limb [21] . Among the regions that Auts2 was shown to be expressed in the brain are the neuronal nuclei in the developing cerebral cortex and cerebellum [22] . In the cortical preplate , Auts2 is activated by T-box brain 1 ( Tbr1 ) [22] , [23] , a postmitotic projection neuron specific transcription factor that is critical for normal brain development . Tbr1 deficient mice display irregular laminar organization of cortical neurons [24] . Additionally , Cajal-Retzius cells in Tbr1 deficient mice have decreased levels of reelin ( Reln ) [23] , a protein that is involved in neuronal migration in the developing brain and has been reported to be expressed at decreased levels in individuals with ASD [25] . In this study , we used zebrafish morpholinos to functionally characterize auts2 . We show that knocking down this gene leads to an overall stunted developmental phenotype that includes a smaller head , body and reduced movement . Further characterization of morphant fish revealed a reduction in developing midbrain neurons and also in sensory and motor neurons . To characterize AUTS2 enhancers , we used both zebrafish and mouse transgenic enhancer assays . We identified three functional enhancers within an ASD-associated deletion and six brain enhancer in regions associated with human specific evolution . Combined , we found that AUTS2 is important for neuronal development and characterized several functional enhancers within this locus , where nucleotide changes could be associated with neurodevelopmental disease and human specific evolution .
Zebrafish can be an effective tool to study ASD [26] . Using whole mount in situ hybridization , we determined that auts2 is expressed in zebrafish at 24 hours post fertilization ( hpf ) in the forebrain , midbrain and hindbrain ( Figure S1A ) . Additionally , auts2 is expressed in the trunk ( including the spinal cord ) , with stronger expression towards the caudal peduncle . At 48 hpf , auts2 is expressed in the brain and pectoral fin and from 72–120 hpf its expression is restricted primarily to the brain . auts2 is also weakly expressed in the eye from 24–120 hpf . Overall , we observed that the zebrafish expression largely correlates with the previously characterized mouse expression [21] , [22] . We next used morpholinos ( MOs ) to knockdown auts2 in zebrafish during development . Fish injected with an auts2 translational blocking MO displayed a stunted developmental phenotype with smaller heads , eyes , body and pectoral fins ( Figure 2A and Figure S1C ) . A second auts2 MO that disrupts the splice junction between intron two and exon three exhibited similar but less severe phenotypes ( Figure S1D ) . These phenotypes appeared in 80–90% of injected fish and were rescued by co-injecting the full length human AUTS2 mRNA along with the translational blocking MO ( 68% of injected fish showed a partial to full rescue ) ( Figure S1E ) . Injection of a 5 base pair ( bp ) mismatch auts2 translational MO control did not show any phenotype ( Figure 2A and Figure S1B ) , further validating the specificity of our MOs to effectively knockdown auts2 in zebrafish . To further characterize the neurological function of auts2 , we injected the translational MO into the HuC-GFP transgenic zebrafish line [27] , where developing neurons express green fluorescent protein ( GFP ) . Compared to the 5 bp mismatch control , translational MO injected fish showed a dramatic decrease in GFP at 48 and 72 hpf in the dorsal region of the midbrain , including the optic tectum , the midbrain-hindbrain boundary ( which includes the cerebellum ) , the hindbrain and the retina ( Figure 2B ) . This phenotype was also observed by staining neurons with Nissl at 48 hpf ( Figure S2A ) . TUNEL staining of 48 hpf embryos revealed that morphant fish exhibit increased apoptosis in the midbrain in the same location where fewer neurons where observed ( Figure 2C and Figure S2B ) . Anti-proliferating Cell Nuclear Antigen ( PCNA ) staining showed increased amounts of cell proliferation in morphant fish in the forebrain , midbrain and hindbrain ( Figure 2D and Figure S2C–S2E ) . While seemingly contradictory , increased amounts of both TUNEL and PCNA positive cells has been previously shown , as cell death and proliferation could be coupled [28] , [29] . It is conceivable that the increased PCNA positive cells are the result of morphant cells failing to differentiate into mature neurons , as seen in the HuC-GFP line . These results suggest that auts2 may be involved in the production and maintenance of neurons in the zebrafish brain . Both the translational and splicing morphant fish also showed a decreased movement response when gently prodded with a pipette tip compared to controls that began at 48 hpf ( Video S1 and Video S2 ) . This phenotype was observed until 120 hours when the zebrafish were euthanized . In order to determine whether motor neuron defects could explain this phenotype , we injected the translational MOs into the Tg ( mnx1∶GFP ) zebrafish line , which expresses GFP in developing motor neurons [30] . At 48 hpf , morphant fish displayed fewer GFP labeled motor neuron cell bodies in the spinal cord . Additionally , motor neuron projections were weaker and perpendicular to the spinal cord , in contrast to the angled projections of the control injected fish ( Figure 2E ) . This phenotype was also confirmed using the znp-1 antibody to mark motor neuron axons [31] in control and morphant fish . Morphant fish consistently showed more branching of axons compared to controls ( Figure S3 ) . To assess sensory neuron defects , Rohon-Beard neurons were stained with anti-HNK-1 in control and translational MO injected fish at 48 hpf . Morphant fish displayed on average 60% fewer sensory neurons in the spinal cord ( Figure 2F ) . These results suggest that loss of auts2 in zebrafish could lead to motor and sensory neuron defects , which may play a role in their reduced movement and decreased response to touch . Due to the observations that noncoding regions in the AUTS2 locus are associated with neurological phenotypes and human-specific evolution , we set out to identify enhancers in this locus . To focus our search , we limited our candidates to be between the first exon and fifth intron , due to this region encompassing the human-Neanderthal sweep ( exon 1–4; chr7:68 , 662 , 946-69 , 274 , 862 ( hg18 ) ) [17] and several noncoding nucleotide changes that have been associated with neurological phenotypes [5] , [11] , [12] , [16] . AUTS2 enhancer candidate ( AEC ) sequences were selected based on evolutionary conservation , embryonic mouse forebrain and midbrain ChIP-seq datasets [32] and nucleotide variants that define the human-Neanderthal sweep [17] ( see methods ) . We also tested the human accelerated region ( HAR ) in intron four , HAR31 [18] , and the human accelerated conserved non-coding sequences ( HACNS ) in introns one and six , HACNS 369 and HACNS 174 respectively [19] . Using these criteria , 40 AECs were selected for zebrafish enhancer assays ( Table S1 ) . These human sequences were cloned into the E1b-GFP-Tol2 enhancer assay vector and injected into zebrafish [33] . Of the 40 candidates , 23 were found to be functional enhancers , 22 of which showed enhancer activity in locations that overlap auts2 expression in zebrafish and 10 that were active in the brain ( Table S1 and Figure S4 ) . To further characterize the regulatory elements within a 33 , 519bp deletion associated with ASD in AUTS2 intron four [5] , the three positive zebrafish enhancers in this region ( AEC27 , AEC29 , AEC32 ) were analyzed in mice using a similar transgenic assay [34] . AEC27 showed enhancer expression in the somitic muscle in zebrafish , while examination of its enhancer activity at E11 . 5 ( hs658;[34] ) found it to be active in the midbrain and neural tube ( Figure 3 ) . At E12 . 5 , AEC29 had enhancer activity in the olfactory epithelium similar to zebrafish and also displayed enhancer expression in the eye ( Figure 3 ) . AEC32 recapitulated the zebrafish enhancer expression in the midbrain and hindbrain with additional enhancer expression in the forebrain at E12 . 5 . Histological sections of AEC32 showed enhancer activity in the mouse cerebellum ( Figure 3 ) , a region thought to play a role in ASD [2] . The removal of these three brain enhancers and potentially other functional sequences in this region could contribute to the neurological phenotypes in patients with deletions in this intron . We next set out to characterize enhancers in regions implicated in human-specific evolution . Four of the sixteen positive zebrafish enhancers identified in this region ( Table S1 and Figure S4 ) were analyzed for enhancer activity in mice . These four sequences were positive mouse enhancers active in the brain , the otic vesicle , or eye ( Figure 4 and Figure S5 ) . Interestingly , two of these enhancers ( AEC10 and 21 ) show enhancer expression in the developing tectum , a region in the brain that is thought to control auditory and visual responses .
Using MOs to knockdown auts2 , we observed an overall phenotype of stunted development , making it difficult to characterize discrete phenotypes . However , using neuronal-labeled zebrafish lines and immunohistochemistry , we showed a reduction in motor and sensory neurons in the spinal cord and developing neurons in regions that include the midbrain and cerebellum . The cerebellum is involved in cognitive and emotional function and has been repeatedly implicated in ASD [2] . In addition , the cerebellum plays a major role in motor control , and it is possible that the defects detected in cerebellar neurons could partially explain the reduced movement phenotype observed in morphant fish . It is worth noting that two individuals with AUTS2 structural variants had motor delay phenotypes ( Figure 1 ) [12] . Given that the MO injected fish display additional phenotypes to the ones we focused on in this study , the effect of this gene on other tissues will need to be assessed in future experiments . Experiments such as mouse conditional knockouts should allow for a more complete understanding of AUTS2 function . Our auts2 MOs were designed to disrupt auts2 activity on chromosome 10 ( build Zv9 ) . It is worth noting , that there is also a putative , less characterized version of auts2 with an incomplete coding sequence located on zebrafish chromosome 15 ( ENSDART00000012712 ) . Knocking down this gene along with the auts2 gene that was assayed in our study may lead to more severe phenotypes . Our enhancer search focused primarily on the first five introns due to the numerous reports of cognitive-related structural variations in that region [4]–[6] , [8]– , along with the region's putative role in evolution . There could be numerous functional enhancers outside this region that we have not tested in this study . For example , there is an intragenic SNP ( rs6961611 ) associated with processing speed [35] 1 . 6 mega bases downstream of AUTS2 which could be associated with a regulatory element for this gene . While the expression of our enhancers largely recapitulated Auts2 expression , it is possible that the enhancers we identified could regulate a neighboring gene . Future experiments such as chromatin interaction analyses [36] , [37] could be able to distinguish what promoters our enhancers are interacting with . Previous work has shown that human enhancer sequences can function as active enhancers in zebrafish , even without homologous sequences in zebrafish [38]–[40] . Our results confirm these findings for some of our enhancers . For example , AEC10 , 13 and 29 , which do not have homologous sequences in zebrafish , have similar enhancer expression patterns in zebrafish and mouse ( Table S1 ) . However , AEC21 and 27 , which are conserved down to zebrafish , and AEC 24 , which is conserved down to chicken , don't have matching expression patterns in zebrafish and mice . We found three positive human enhancers in both zebrafish and mouse that reside within a 33 , 519 bp deletion detected in an individual with ASD , one of which , AEC32 , is expressed in the cerebellum . This deletion was inherited from the individual's mother who was not diagnosed with ASD [5] . ASDs are likely caused by multiple genomic aberrations in combination with environmental factors . While it is possible that in this individual , this deletion leads to ASD due to the loss of these enhancers and potentially other functional sequences , it is also possible that the loss of these enhancers is one of multiple “hits” [41] or that the deletion is not causative . With the constantly growing number of individuals with ASDs or other neurological phenotypes that have AUTS2 mutations , some of which are purely noncoding , it is likely that improper regulation of this gene is involved in the progression of these disorders . We also characterized enhancers in locations associated with other neurological phenotypes . In an 84 kb deletion in intron one of an individual with dyslexia , we identified four positive human enhancers in zebrafish ( AEC3-6 ) ( Table S1 and Figure S4 ) , one of which is expressed in the midbrain . In addition , one of the candidates that was negative for zebrafish enhancer activity ( AEC35 ) was a sequence that included the alcohol consumption associated SNP ( rs6943555 ) [16] . It is possible that zebrafish is not a good model system for this region/phenotype or that the actual functional region/variant is further away from this tag SNP . By characterizing the regulatory landscape of this region we have obtained a better understanding of the functional units within this gene , which now pose as candidates for mutation analysis in individuals with various neurological phenotypes . AUTS2 has been singled out as a gene that is rapidly evolving in humans in three different studies [17]–[19] . Using zebrafish enhancer assays , we identified sixteen different enhancers that lie within regions that were implicated in human evolution , six of which show expression in the brain . We tested four of the enhancers in mice and two of them had midbrain enhancer activity . Our enhancer results , combined with the observation that human-specific neurological disorders are associated with mutations in this gene , suggest that AUTS2 has an important role in the evolution of human cognitive traits .
Zebrafish embryos were collected from ABs or caspers [42] between 24 to 120 hpf and fixed in 4% paraformaldehyde buffered with 1× PBS ( PFA ) . The zebrafish auts2 ( Open Biosystems EDR1052-4681254 ) cDNA clone was used to generate digoxygenin labeled probes . Whole-mount in situ hybridizations were performed according to standard protocols [43] . Two morpholino ( MO ) antisense oligonucleotides targeting auts2 were designed by Gene-Tools . One MO was designed to target the translational start site of auts2 ( GTGGAGAGTGTGTCAACACTAAAAT ) . The second was designed to target the splice junction between intron 2 and exon 3 of Ensembl Transcript ENSDART00000137928 ( TCGACTACTGCTGTGAACAAAGAGA ) . A third 5 bp mismatch control for the translational MO ( GTGGACACTGTGTGAAGACAAAAAT ) was also designed . The MOs were diluted to 1 mM in deionized water and injected using standard techniques [44] into one cell-stage embryos . To rescue the morphant phenotypes , we transcribed full length human AUTS2 RNA ( Open Biosystems MHS1010-9204165 ) using the T7 message machine ( Ambion ) and co-injected it along with the translational MO at a concentration of 168 ng/ul . The HuC line was generously donated by Dr . Su Guo ( UCSF ) . The Tg ( mnx1∶GFP ) ( AB ) line ( formerly known as hb9 ) was obtained from the Zebrafish International Resource Center ( ZIRC; http://zebrafish . org/zirc/home/guide . php ) . Fish where injected with MOs as described above and annotated using the Leica M165 FC microscope . At least 50 translational MO injected fish and controls were compared in all zebrafish lines used . AB zebrafish embryos injected with the auts2 translational MO or the 5 bp control were fixed at 48 hpf in 4% PFA overnight at 4°C , then washed for 15 minutes at room temperature in PBS . Zebrafish were frozen into blocks using Tissue-Tek O . C . T . ( Sakura Finetek ) then sectioned ( 10–20 microns ) using a Leica CM1850 cryostat and stained with Nissl ( FD NeuroTechnologies ) . Morphant and control sections represent comparable planes . Staining with PCNA ( DAKO , Monoclonal Mouse PCNA clone PC10 ) was done according to the manufacturer's protocol . Cell nuclei were visualized using DAPI ( Invitrogen ) . Staining sections with TUNEL ( Roche , In Situ Cell Death Detection Kit , TMR red ) was done according to the manufacturer's protocol . Zebrafish sections were analyzed using the Leica M165 FC or the Nikon Eclipse E800 microscope . At least 25 fish were analyzed in each condition . Control and morphant pictures were taken with identical exposures and are representative of each condition . For TUNEL staining on sections , criteria for amount of cell death was based on the number of individual TUNEL positive cells identified in the midbrain and eye , indicative of cell death in those regions . For PCNA staining ( cell cycle marker ) on sections , criteria for amount of proliferation in the forebrain , midbrain and hindbrain was qualitatively evaluated due to the larger number of PCNA positive cells in morphants compared to controls . Casper zebrafish embryos injected with the auts2 translational MO or the 5 bp control were fixed at 48 hpf overnight at 4°C in 4% PFA . For TUNEL staining , embryos were transferred to methanol for 30 minutes followed by rehydration in methanol/PBST ( PBS with 0 . 1% tween ) . They were then placed in Proteinase K ( 10 µg/ml ) for 5 minutes and postfixed in 4% PFA for 20 minutes . Embryos were later placed in prechilled ethanol∶acetic acid ( 2∶1 ) at −20°C for 10 minutes and then washed in PBST for 20 minutes followed by TUNEL staining using the In Situ Cell Death Detection Kit , TMR red ( Roche ) according to the manufacturer's protocol . Sensory neurons were analyzed using anti-HNK-1 ( Sigma ) followed by the goat anti-mouse IgM HRP secondary antibody ( abcam , ab5930 ) using previously described methods [45] . HNK-1 positive cells where manually counted in 6 different control and morphant fish . Fish were analyzed using the Leica M165 FC or the Nikon Eclipse E800 microscope . At least 25 fish were analyzed in each condition . Control and morphant pictures were taken with identical exposures and are representative of each condition . For TUNEL whole mount staining , criteria for amount of cell death was based on the number of viewable individual TUNEL positive cells in the forebrain , midbrain and hindbrain . For HNK-1 staining , criteria for amount of sensory neurons was based on the number of individual HNK-1 positive cells counted in equal lengths of the trunk . Motor neuron axons were analyzed using anti-znp-1 ( Developmental Studies Hybridoma Bank ) followed by anti-mouse IgG HRP ( GE Healthcare ) using previously described methods [46] . AUTS2 enhancer candidate ( AEC ) sequences were selected based on evolutionary conservation ( sequences showing ≥70% identity for at least 100 bp between human and chicken ) , E1A binding protein p300 ( EP300 ) forebrain or hindbrain ChIP-Seq datasets [32] , and nucleotide variants that define the human-Neanderthal sweep [17] ( Table S1 ) . PCR was carried out on human genomic DNA ( Qiagen ) using primers designed to amplify the AEC sequences ( Table S1 ) . Primers were designed such that they will have additional flanking sequences to the conserved , ChIP-Seq or human-Neanderthal accelerated sequences based on previous experiments that have shown this to be a reliable method for obtaining positive enhancer activity [47] . PCR products were cloned into the E1b-GFP-Tol2 enhancer assay vector containing an E1b minimal promoter followed by GFP [33] . They were then injected following standard procedures [46] , [48] into at least 100 embryos per construct along with Tol2 mRNA [49] , to facilitate genomic integration . GFP expression was observed and annotated up to 48 hpf . An enhancer was considered positive if at least 15% of all fish surviving to 48 hpf showed a consistent expression pattern after subtracting out percentages of tissue expression in fish injected with the empty enhancer vector . Notably , the empty vector showed particularly high background for heart and somitic muscle and as described all enhancer results were obtained after deducting its expression pattern . Thus , in order to call positive somitic muscle enhancer activity , over 26% ( 24hpf ) or 40% ( 48hpf ) of alive fish needed to show positive enhancer activity . To call a positive heart enhancer , 32% ( 24hpf ) or 50% ( 48hpf ) of alive fish needed show positive heart activity . For each construct , at least 50 fish were analyzed for GFP expression at 48 hpf . For the mouse enhancer assays , the same human genomic fragment used in zebrafish was transferred into a vector containing the Hsp68 minimal promoter followed by a LacZ reporter gene [47] , [50] and sequence verified to ensure the insert matched the human reference sequence . Sequences having rare variants were changed to the reference human genomic sequence by site-directed mutagenesis ( Mutagenex or Quickchange II , Stratagene ) and sequence verified for having the reference sequence . Transgenic mice were generated by Cyagen Biosciences using standard procedures [51] . Embryos were harvested at E12 . 5 and stained for LacZ expression using standard procedures [47] . Mouse embryos selected for sectioning were placed in an overnight cryoprotection stage using 30% sucrose in PBS . Mice were frozen into blocks using Tissue-Tek O . C . T . ( Sakura Finetek ) then sectioned ( 20 microns ) using a Leica CM1850 cryostat and stained with Nuclear Fast Red Solution ( Sigma-Aldrich ) for one minute . There is no human subjects work involved in this article . All animal work was approved by the UCSF Institutional Animal Care and Use Committee ( protocol number AN084690 ) . | Autism spectrum disorders ( ASDs ) are neurodevelopmental disorders that affect 1 in 88 individuals in the United States . Many gene mutations have been associated with autism; however , they explain only a small part of the genetic cause for this disorder . One gene that has been linked to autism is AUTS2 . AUTS2 has been shown to be disrupted in more than 30 individuals with ASDs , both in coding and noncoding sequences ( regions of the gene that do not encode for protein ) . However , its function remains largely unknown . We show here that AUTS2 is important for neuronal development in zebrafish . In addition , we characterize potential AUTS2 regulatory elements ( DNA sequences that instruct genes as to where , when , and at what levels to turn on ) that reside in noncoding regions that are mutated in ASD individuals . AUTS2 was also shown to be implicated in human evolution , having several regions where its human sequence significantly changed when compared to Neanderthals and non-human primates . Here , we identified four mouse enhancers within these evolving regions , two of which are expressed in the brain . | [
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"gene... | 2013 | Function and Regulation of AUTS2, a Gene Implicated in Autism and Human Evolution |
Although balancing selection with the sickle-cell trait and other red blood cell disorders has emphasized the interaction between malaria and human genetics , no systematic approach has so far been undertaken towards a comprehensive search for human genome variants influencing malaria . By screening 2 , 551 families in rural Ghana , West Africa , 108 nuclear families were identified who were exposed to hyperendemic malaria transmission and were homozygous wild-type for the established malaria resistance factors of hemoglobin ( Hb ) S , HbC , alpha+ thalassemia , and glucose-6-phosphate-dehydrogenase deficiency . Of these families , 392 siblings aged 0 . 5–11 y were characterized for malaria susceptibility by closely monitoring parasite counts , malaria fever episodes , and anemia over 8 mo . An autosome-wide linkage analysis based on 10 , 000 single-nucleotide polymorphisms was conducted in 68 selected families including 241 siblings forming 330 sib pairs . Several regions were identified which showed evidence for linkage to the parasitological and clinical phenotypes studied , among them a prominent signal on Chromosome 10p15 obtained with malaria fever episodes ( asymptotic z score = 4 . 37 , empirical p-value = 4 . 0 × 10−5 , locus-specific heritability of 37 . 7%; 95% confidence interval , 15 . 7%–59 . 7% ) . The identification of genetic variants underlying the linkage signals may reveal as yet unrecognized pathways influencing human resistance to malaria .
Malaria caused by Plasmodium falciparum is one of the leading causes of human morbidity and mortality worldwide , predominantly affecting populations of resource-poor countries in the south [1] . Drawbacks in developing effective control measures have stressed the demand for research aiming at a better understanding of basic elements of parasite biology and disease pathology . The blood stages of the parasite comprise asexual forms , which maintain the infection and cause disease , and sexual forms , which transmit the infection [2] . Asexual blood parasite counts are the established measure of infection intensity [3] , whereby reports on substantial variations over a short period of time indicated that many measurements may be required for appropriate estimates [4] . Clinically , malaria presents as a mild form of acute febrile episodes and anemia , or as a severe form , which comprises a complex syndrome of life-threatening complications [5] . While the severe form causes an enormous humanitarian burden , it does not affect more than 1%–2% of the residents of endemic areas [6] , whereas the mild form predominates in terms of quantitative morbidity and economic reasoning [1 , 7 , 8] . While the non-specific symptoms of fever , headache , and nausea make the diagnosis of malaria fever episodes difficult to ascertain , a simple case definition proposed by the World Health Organization ( WHO ) based on fever and parasitemia is generally accepted due to its high sensitivity and specificity in endemic areas , where the vast majority of such episodes are in fact caused by malaria [9] . A second clinical feature of mild malaria is anemia . It affects an enormous number of children in endemic areas [10] and may present as a chronic , subacute , or acute , sometimes life-threatening form [5] . Its pathogenesis is considered multifactorial and may include the destruction of infected and uninfected erythrocytes and bone-marrow dysfunction , whereby the relative contributions of these factors and their roles in the various forms of malarial anemia have not yet been resolved [11] . The effect of human genetics on malaria has long been recognized when the theory of balancing selection was substantiated for thalassemias , sickle-cell anemia , and other red blood cell disorders [12] . Twin studies and heritability estimates have subsequently confirmed the influence of host genetics , which was shown to be most pronounced in children [13–15] . Candidate gene approaches have indicated a number of additional variants to be involved including those of the major histocompatibility complex and a cytokine-gene cluster on Chromosome 5q31-q33 [16] . However , no systematic analysis has been reported to address human genetics in malaria more comprehensively . Here we report on an autosome-wide linkage analysis for P . falciparum infection intensity and mild clinical malaria among African children selected not to carry any of the classic malaria resistance genes . As markers , 10 , 000 single-nucleotide polymorphisms ( SNPs ) were used .
392 siblings of 108 families resident in West Africa were followed over a period of 31 wk , which covered an entire rainy season . Prevalences of P . falciparum blood trophozoites , parasite densities , and interim or present malaria fever episodes were monitored weekly and anemia as indicated by the packed blood-cell volume ( PCV ) was determined biweekly . Compliance was as follows; 98 . 8% of 12 , 152 parasitemia assessments , 95 . 4% of 6 , 272 PCV assessments , and 98 . 5% of 12 , 152 assessments for malaria fever episodes were recorded with a maximum of data missing per planned visits of single participants of 13/31 , 6/16 , and 18/31 records , respectively . Results from regression models for analyzing the effect of age , bednet use , and intake of antimalarials on the various phenotypes are summarized in Figure 1 . Gender had no significant effect on any of the phenotypes , and therefore was not included in the final regression models used for phenotype corrections . Based on a ranking that favored high levels of parasite densities in conjunction with high intrafamilial variability ( see Materials and Methods ) , 377 individuals of 68 families were selected for genotyping , including 136 parental individuals and 241 siblings , who formed 330 sib-pairs . Applying the Affymetrix Human Mapping 10K array yielded an overall autosomal calling fraction of 94 . 5% for the raw genotypes . These were defined as SNPs for which definitive genotypes were obtained . After application of the quality control procedure , 1 , 524 autosomal markers ( 15 . 2% ) were excluded from further analysis . The remaining markers yielded a mean information content of 0 . 976 ( SD ± 0 . 029 , range 0 . 510–1 . 000 ) . The nonparametric linkage analysis ( NPL ) and Haseman-Elston multipoint linkage analysis ( HE ) were applied ( Figure 2A–2D ) . Parasite prevalence , parasite density , fever episodes , and anemia were analyzed as quantitative phenotypes . The most prominent result was a linkage signal for malaria fever episodes on Chromosome 10p15 . 3–10p14 , which reached statistical significance in both the NPL and HE analyses . NPL showed an asymptotic z score of 4 . 37 ( empirical p-value = 4 . 0 × 10−5 ) between SNP markers rs952153 and rs1964428 marking the interval of 5 . 9–12 . 0 cM and 2 . 5–3 . 5 Mb of the genetic and physical chromosomal maps , respectively ( Figure 2D ) . HE showed a maximum asymptotic logarithm of odds ( LOD ) score of 3 . 03 ( empirical p-value = 2 . 1 × 10−4 ) at marker rs1964428 corresponding to 12 . 0 cM/3 . 5 Mb ( Figure 2D ) . The locus-specific heritability was estimated to be 37 . 7% ( 95% confidence interval , 15 . 7%– 59 . 7% ) at 11 . 2 cM . The linkage region was termed PFFE-1 for P . falciparum-fever episode 1 . The signal was robust to variations in data analysis , including the use of a raw phenotype without adjustments for covariates ( z score of 4 . 52 ) , or the use of a wider definition of malaria fever episodes that included afebrile malaria episodes diagnosed by the study physicians ( z score of 4 . 04 ) . The 2 . 2 z score support interval ( corresponding to a 1-LOD support ) encompassed a 27 . 4 cM/11 . 0 Mb distance containing 71 annotated or hypothetical genes . Functional candidates include genes encoding a platelet-type phosphofructokinase ( PFKP ) also expressed in red blood cells , an inducible 6-phosphofructo-2-kinase/fructose-2 , 6-bisphosphatase ( iPFK-2/PFKFB3 ) , the alpha chain of the interleukin-15 receptor ( IL15RA ) , the alpha chain of the interleukin 2 receptor ( IL2RA ) , protein kinase C theta ( PRKCQ ) , the GATA-binding protein 3 ( GATA3 ) , and a gene similar to that of the interleukin 9 receptor precursor ( LOC439945 ) . A further region with evidence for linkage was found using parasite density as the phenotype . The NPL analysis yielded a signal on Chromosome 13q with a maximum asymptotic z score of 3 . 73 ( empirical p-value = 2 . 3 × 10−4 ) between rs2147363 at chromosomal position 55 . 0 cM/51 . 4 Mb and rs726540 at 55 . 5 cM/52 . 3 Mb ( Figure 2B ) . HE resulted in a LOD score of 1 . 19 at this position ( Figure 2B ) . The locus-specific heritability was estimated to be 33 . 7% ( 95% confidence interval , 9 . 8%–57 . 6% ) . The region was termed PFPD-2 for P . falciparum-parasite density 2 , whereby another linkage region with parasite density had previously been reported [17–19] . The 2 . 2-z support interval of PFPD-2 encompassed 24 . 2 cM/32 . 4 Mb containing 158 annotated or hypothetical genes . Possible functional candidates include genes encoding the lymphocyte cytosolic protein 1 ( LCP1 ) , S-formylglutathione hydrolase ( esterase D , ESD ) , the cysteinyl leukotriene receptor 2 ( CYSLTR2 ) , and the endothelin receptor , nonselective type , ( EDNRB ) . Furthermore , a signal on Chromosome 1p36 at 18 cM/9 Mb provided evidence for linkage with both parasite prevalence ( LOD score of 2 . 31; empirical p-value = 5 . 3 × 10−4 ) , and PCV ( LOD score of 2 . 45; empirical p-value = 3 . 9 × 10−4 ) at adjacent marker positions ( rs205474 and rs966134 , respectively; Figure 2A and 2C ) . The NPL z scores were low in both instances ( 2 . 75 and 2 . 36 , respectively ) . Finally , no evidence was obtained for linkage of parasite density or malaria fever episodes to 5q31-q33 and to the MHC region on 6q23 , respectively , which had previously been reported . In the present study , weak evidence was obtained that malarial anemia might be linked to 5q31-q33 ( z score = 2 . 7 , LOD score = 1 . 8 ) ( Figure 2C ) .
To our knowledge , this is the first genome-wide approach to identify human genetic variants influencing susceptibility and resistance to malaria . Since the seminal observations on balancing selection with inborn red blood cell disorders , malaria is a prominent element in human genetics . The importance of the classic malaria-protective red blood cell traits is in the present study highlighted by the large proportion of 86% of families found to be affected in the initial survey of our study population . These were excluded from the study in order to concentrate the search on as yet unrecognized human genetic variants [16 , 20] . As genetic influences were reported to be of particular relevance in childhood malaria [14] , we limited our study to children aged 0 . 5–11 y . Assessing the phenotype of malaria infection intensity remains a challenge because it is uncertain to which extent any limited number of parasite counts truly reflect the infection intensity [4] . In addition , infection intensities may strongly depend on exposure , which is a variable difficult to assess in field studies . In the present study , the use of bednets and window screens to reduce exposure by preventing mosquito bites was addressed by data adjustments and exclusions of families , respectively ( see Materials and Methods ) . It may be considered an advantage that the NPL and HE methods applied are based on intra-familial evaluations because malaria exposure is likely to be homogeneous within families living in the same households . As expected , antimalarial treatments had an effect on all phenotypes studied . The influence on parasite prevalences and parasite densities was found to be limited to the two subsequent assessments , therefore it was addressed by correcting the respective values of prevalences and by excluding the corresponding densities ( see Materials and Methods ) . In contrast , the influence on anemia was corrected for by adjusting the overall phenotype because epidemiological observations on the effect of drug resistance on anemia suggest possible long-term effects [22 , 23] . Concerning the number of fever episodes , no adjustments were made because they might have neutralized the essential phenotypic information due to the direct causal relationship between disease episodes and treatments . Of all covariates tested , age had the strongest effect and was included in all phenotype adjustments . In children older than 6 mo as in the present cohort , the age effect on malaria in endemic areas is dominated by the gradual development of a certain degree of adaptive immunity , termed semi-immunity . This is reflected by a successive decrease over age of the number of fever episodes , the degree of anemia , parasite densities , and , at relatively high age , parasite prevalences [24–27] . Therefore , the phenotypes addressed may be influenced by both innate resistance and adaptive immunity , whereby innate resistance may have a predominant influence in younger children and adaptive immunity in older ones . This may focus the linkage signals obtained in this study on variants that are relevant under both conditions . The phenotypes studied showed significant correlations between each other . This is in agreement with the general understanding that all signs and symptoms of malaria result from parasitemia . The explained variances in most instances were low , however , leaving room for separate genetic influences . As expected , the correlation between parasite prevalences and parasite densities was exceptionally high . Despite this , both were included as separate phenotypes because there is evidence to suggest that they are under distinct genetic influences . First , epidemiological findings including those of the present study ( unpublished data ) indicate that semi-immunity suppresses high parasite densities significantly more efficiently than low parasite densities [24] , which suggest distinct elements of adaptive immunity . More importantly , HbS has been shown to protect from high parasite density but not from parasitemia itself [28] , indicating that mechanisms of genetic resistance may affect high parasite density specifically . Evaluation of the data using established linkage methods revealed several prominent linkage signals . Interestingly , locus-specific heritability calculations performed for two of these linkage regions indicated that , in both cases , approximately 35% of the total phenotype variability was attributable to these loci in families who did not carry any of the established malaria resistance factors . These estimates allow us to postulate the effect of a major locus in both instances , which would support a recent conclusion that susceptibility and resistance to infectious diseases may be governed by single major genes rather than by a large number of genes each exerting a small influence [29] . The region showing strongest and significant linkage concerned the phenotype of malaria fever episodes ( PFFE-1 ) . Notably , the signal was found in both model-free approaches . Furthermore , it was robust to variations in phenotype definitions , which may be of particular importance because the non-specific symptoms of malaria fever episodes make the clinical diagnosis uncertain . That we found the strongest linkage signal with this particular phenotype may relate to the fact that fever regulation might be similar regardless of whether it is influenced by innate resistance or adaptive immunity , with respect to the age-dependent bias introduced into our study cohort by these two factors , as described above . The underlying genetic variant may be of more general interest because it may relate to the regulation of the systemic inflammatory response . A number of additional regions with evidence for linkage were identified which did not reach statistical significance . Therefore , they are not discussed in any detail , although experiences in other complex diseases have shown that weaker linkage signals may as well lead to the identification of relevant genetic variants [30] . The linkage regions described comprise a number of genes which may be classified as functional candidates because their products are operative in immune regulation or red blood cell metabolism . However , regarding their established functions , we consider none of them a prime candidate . No support for our data can be derived from previous linkage studies in mouse malaria . Studying parasite density in murine Plasmodium chabaudi infection , evidence has been obtained for linkage regions on Chromosomes 3 , 5 , 9 , 11 , and 17 [31] but not on Chromosome 14 , which covers the synteny of the linkage region on human 13q we obtained for P . falciparum- parasite density ( NCBI , http://www . ncbi . nlm . nih . gov/Homology/ ) . This is not unexpected because P . falciparum substantially differs from P . chabaudi in that P . falciparum-infected red blood cells adhere to the vascular endothelium [2] , which may have a strong influence on parasite biology . Further linkage studies on murine malaria are limited to the phenotype of cerebral manifestations in Plasmodium berghei infections [31] , which cannot be compared to our clinical phenotypes of uncomplicated malaria , and identified regions on Chromosomes 1 , 11 , and 17 but not on 13 and 2 , which cover the regions syntenic to PFFE-1 on 10p . To our knowledge , this is the first time that the Affymetrix HMA10k chip was used for genotyping individuals of African descent . The raw genotypes yielded a call rate of 94 . 5% , which nearly reached 95% considered sufficient for optimal assay performance [32] and was comparable to 96 . 9% reported for Caucasians [33] . This provides a basis for using the chip in African populations .
The study was conducted in the Asante Akim North District , Ashanti Region of Ghana , West Africa , a region classified as hyperendemic for malaria by a cross-sectional prevalence of 0 . 54 for P . falciparum . Ethical approval was obtained from the Committee for Research , Publications and Ethics of the School of Medical Sciences , Kwame Nkrumah University of Science and Technology , Kumasi , Ghana . All procedures were explained in the local language , and consent was obtained from both parents . Parents of 2 , 551 families were recruited who had three or more children below the age of 12 y and agreed to participate . Venous blood samples of 2 ml were obtained from both parents and preserved by addition of an equal volume of 8 M urea . The genetic variants of hemoglobin ( Hb ) S , HbC , alpha+ thalassemia deletion 3 . 7 , and glucose-6-phosphate-dehydrogenase ( G6PD ) deficiency A- , which were considered to possibly influence susceptibility to P . falciparum parasitemia and mild malaria , were determined , and 346 ( 13 . 6% ) families were identified not segregating any of the traits . Of the 346 families , a study group of 392 siblings of 108 families was selected based on ( i ) the logistic criterion that their homes clustered in 16 of the 30 villages included in the initial survey and ( ii ) that they did not live in homes equipped with window screens , which a posteriori were found to significantly reduce parasite prevalences from 0 . 54 to 0 . 35 ( p < 0 . 001 ) and other parameters of malaria infection intensity . All families belonged to the ethnic group of Akan . A subset of 377 members of 68 families were selected from the study group by excluding siblings who were absent at more than two assessments and by a ranking that favored high levels of parasite densities of P . falciparum in conjunction with high intrafamilial variability . The ranking score was based on the product of the mean of log parasite densities of P . falciparum within sib-ships multiplied by the standard deviation of log parasite densities within sibships . Families with highest scores were selected until 377 individuals were identified for genotyping . The genetic study group comprised 136 parents with 241 children , 52 . 5% boys and 47 . 5% girls , who , with a mean of 3 . 54 siblings per family , formed 330 sib pairs . Their median age was 5 y ( range 0 . 5–11 y; IQR 3–8 y ) . The children were phenotyped from May 20 to December 20 , 2002 . Weekly assessments by the visit of a trained physician included a medical history , measurement of body temperature by an infrared ear thermometer , a blood sample by finger prick or heel prick ( approximately 100 μl ) , and in case of disease symptoms , a physical examination . The installation of window screens in homes and the use of bed-nets were recorded . Weekly malaria smears were prepared at the study site , and in the laboratory they were stained with Giemsa and examined [34] . Parasite species were assessed , and parasite counts were recorded per 200 leukocytes ( if >10 parasites/200 leukocytes ) or 500 leukocytes ( if ≤10 parasites/200 leukocytes ) by two independent examiners . Parasite densities were calculated assuming a leukocyte count of 8 , 000/μl [34] . If the densities as determined in the two counts differed by a factor of three or more , a third independent count was obtained . The median parasite density of two or three counts was included in the analysis . Weekly point prevalences of malaria parasitemias showed a median of 54% ( range 40%–61% ) yielding a cumulative period prevalence of 99% . Contributing 98% of all parasitemias , P . falciparum was the predominant species; Plasmodium ovale or Plasmodium malariae were found in 19% and in 90% of these in combination with P . falciparum . For the assessments of parasite prevalences and parasite densities , only P . falciparum and only the disease-causing asexual forms were included , which showed a median point prevalence of 53 . 1% and a period prevalence of 99% . Parasite densities of P . falciparum ranged from 0–317 , 360 parasites per μl , with an overall median of 32 and a 75th percentile of 680 in 12 , 011 assessments . Anemia was on the spot assessed as PCV by capillary hematocrit centrifugation using 70 μl EDTA anticoagulated capillary tubes ( Becton Dickinson , Germany ) and mobile centrifuges . To reduce iron-deficiency as a possible confounder , prior to phenotyping , all children were treated against hookworm infection with 400 mg albendazole followed by oral iron supplementation of 2 mg/kg Fe2+ over 6 wk . The median PCV value of 16 biweekly assessments was used as the phenotype in all cases because none of the children showed evidence for any other disease causing substantial anemia . As determined by 16 biweekly PCV measurements , mild anemia defined by a PCV of <33% in the age group of 0 . 5–6 y and of <36% in the age group of 6–14 y [11] was found at 1 , 625 of 5 , 986 assessments ( 27% ) affecting 326 ( 66% ) of the children . Mild malaria attacks were defined following WHO recommendations [9] , first , by either assessing fever by a tympanic temperature of >37 . 7 °C , corresponding to a rectal temperature of >37 . 6 °C [35] , or reported fever within the previous 4 d and second , a blood smear positive for asexual forms of P . falciparum at any density . The number of fever episodes during the observation period of 31 wk was counted for each individual , whereby multiple episodes within 3 wk were considered recrudescences and counted as one episode [36] . Applying this definition resulted in the identification of 504 malaria fever episodes affecting 257 children , which corresponds to a period prevalence of 66% . In addition , 34 febrile states of other etiologies including upper respiratory tract infections , lower respiratory tract infections , pneumonia , measles , and urinary tract infections were diagnosed by the study physicians . Suspected malaria attacks were treated by a standard dose of chloroquine or amodiaquine following national guidelines , other illnesses as deemed appropriate . In greater detail , a tympanic temperature of >37 . 7 °C was measured at 365 visits . Subsequent malaria smears showed that 248 ( 68% ) of the fever attacks occurred in conjunction with P . falciparum parasitemias ( and four with other malaria parasites ) . Fever attacks reported by parents or guardians to have occurred between two examinations were recorded at 870 visits , whereby blood smears at the time of examination were positive for P . falciparum at 522 of these instances , and 133 of the attacks had reportedly been treated with antimalarials . The study physicians at 587 instances suspected malaria attacks at the time of examination , 389 ( 66% ) of which were retrospectively supported by the presence of P . falciparum parasitemia . Of 631 mild malaria attacks as defined by WHO recommendations , 57% were in agreement with malaria diagnoses made by the study physicians , whereas 3% were classified by the study physicians as a febrile illness other than malaria . Regression models were used for adjusting the raw phenotypes for influences of covariates ( Figure 1 ) . Continuous covariates were modeled using multivariable fractional polynomials [37 , 38] at the nominal 5% test level . The choice of the 5% test level has been discussed in [38] . Specifically , we looked for non-linearity by fitting a second-order fractional polynomial to the data . The best power transformation xp of covariate x is found , with the power p chosen from −2 , −1 , −0 . 5 , 0 , +0 . 5 , +1 , +2 , +3 , where x0 denotes ln x . For example , for p = 0 , 0 . 5 the model is b0 + b1 ln ( x ) + b2 √x . The set includes the straight line , i . e . , no transformation p = 1 , and the reciprocal , logarithmic , square root , square , and cubic transformations . Even though the set is small , the powers offer considerable flexibility . The test is performed by comparing the difference in model deviances with a χ2 distribution on 1 degree of freedom . The resulting p-value is approximate and is justified by statistical arguments [37] . Pearson residuals from logistic regression and ordinary residuals from linear and Tobit regressions [39] were used for further analyses . The difference between the sum of observed weekly P . falciparum parasitemias ( 1 or 0 ) minus the sum of predicted probabilities for parasitemias of an individual were calculated and used as phenotype . The predictions were derived from the study population by logistic regression models for parasitemia for each week's set of data with respect to the influences of age , time period since the last use of antimalarials , and the use of bed-nets ( Figure 1 ) . Parasite densities recorded within 2 wk following the administration of antimalarials were found to be significantly lower than parasite densities determined at other time points ( Wilcoxon rank sums tests , p < 0 . 05 ) and were therefore excluded . Since the 31 weekly parasite-density values ( or their log-transformations ) of an individual deviated from a normal distribution , quantiles were used . The 75th percentiles were chosen because they were substantially more informative than the median values , which would have been zero in nearly half the assessments . The 75th percentiles were log-transformed , whereby half the detection threshold of 16 parasites per μl was added before taking the logarithm . The phenotype was adjusted to age by calculating residuals from a Tobit regression model , whereby 74 of 392 observations were left censored ( Figure 1 ) . The median values of 16 biweekly PCV assessments were adjusted using a multiple regression model which included the variables of age , number of antimalarial treatments , and use of bednets . Age was modeled as a second-degree fractional polynomial with exponents of −2 , 2 , the number of antimalarial treatments were considered untransformed , and bednet use was coded as a dummy variable ( Figure 1 ) . The numbers of malaria fever episodes of each individual were adjusted for age and bednet use by calculating residuals from a Poisson regression model . Age was modeled as a second-degree fractional polynomial with exponents of −0 . 5 , −0 . 5 , bednet use was coded as a dummy variable . To avoid outliers for HE , phenotypic values above the 95th percentile were winsorized [40] ( Figure 1 ) . Data analysis showed that siblings of families using mosquito protection by window screens had significantly reduced parasite prevalences , parasite densities , and numbers of fever episodes . As this indicated a marked reduction in exposure , the siblings were excluded a posteriori . The use of bednets was associated with slightly increased parasite prevalences , anemia , and numbers of fever episodes . As bednet use was heterogeneous within one family , all phenotypes were corrected accordingly . Intuitively , the associations appear paradoxical but may be explained by hypothesizing that households living under particularly high exposure , which implies particular molestation by mosquito bites , might use bednets preferentially , albeit with insufficient effectiveness [21] . Venous blood samples of 2 ml were obtained at the end of the study period and preserved by the addition of urea . DNA was extracted by a standard procedure ( NucleoMag 96 Blood , Macherey-Nagel , Germany ) . HbS , HbC [41] , and alpha+ thalassemia [42] were determined as described . G6PD variants A ( A376G ) , and A- ( A376G , G202A ) [43] were typed following a PCR-flourescence-resonance-energy-transfer hybridization method [41] , whereby forward and reverse primers ( F , R ) as well as fluorescein ( FL ) or LC640red ( LC640 ) ( Roche , Germany ) labeled anchor ( A ) and sensor ( S ) oligonucleotides were used: Variant A376G was typed with F: 5′-tgtgtgtctgtctgtccgtgt-3′ , R: 5′-aacggcaagccttacatctg-3′ , A: 5′-FL-cgatgatgcagcctcctaccagcgc-3′ , S: 5′-LC640-tcaacagccacatggatgccctcc-3′ and variant G202A with F: 5′-cagctgccctgccctcag-3′ , R: 5′-cttgaagaagggctcactctgtttg-3′ , A: 5′-LC640-cagaaggccatcccggaacagcc-phosphate-3′ , S: 5′-gcatagcccacgatgaaggtgttttc-FL-3′ . Allele frequencies of HbS , HbC , alpha+ thalassemia , and G6PD A- were estimated to be 0 . 071 , 0 . 058 , 0 . 175 , and 0 . 063 , respectively . G6PD variant A , which was previously reported to show only a slightly decreased enzyme activity of 80% compared to the normal variant B ( A376 ) [44] , was retrospectively found not to show a significant influence on any of the phenotypes ( Wilcoxon rank sums tests of genotypes AA or A , AB , and BB or B on the phenotypes parasite prevalence ( p = 0 . 13 ) , parasite density ( p = 0 . 55 ) , anemia ( p = 0 . 36 ) , and fever episodes ( p = 0 . 80 ) ; therefore children being homozygous , heterozygous , or hemizygous for G6PD B and/or A , were included in the study group . Paternities and maternities were assessed by typing the short tandem repeat markers D1S2782 , D6S273 ( Genethon , www . genethon . fr ) , D5S816 , D7S2212 , D11S1984 , D17S1299 , and D17S1294 ( The Cooperative Human Linkage Center , http://lpgws . nci . nih . gov/CHLC/ ) , in ( TG ) n of CD36 [45] , and IL10G [46] , all showing heterozygosity of ≥0 . 6 in the study population . A high density SNP genome scan was performed using a whole-genome sampling analysis ( WGSA ) approach [32] with the Affymetrix GeneChip Human Mapping 10K v2 Array ( early access ) comprising 10 , 660 SNP markers with an average heterozygosity in Caucasians of 38% and a mean spacing of 258 kb/0 . 36 cM ( Affymetrix , NetAffx Annotation files , http://www . affymetrix . com ) . Mapping order and genetic distances of markers were obtained from Affymetrix , the genetic position of 86 markers was unavailable , 295 were X-linked , and 10 , 279 were from autosomes . Allele frequencies were estimated from 134 founders . The physical positions of the markers were aligned to human DNA sequence information available from NCBI/NIH ( http://www . ncbi . nlm . nih . gov/mapview/maps . cgi ) . In a first step , gender of participants was verified by investigating heterozygosity and hemizygosity , respectively , at X-linked markers . The relationships between individuals were confirmed by using Graphical Relationship Representation [47] . Genotypes incompatible with Mendelian inheritance were identified with PedCheck [48] and removed in members of the respective families . Unlikely genotypes , e . g . , double recombinants , were investigated with Merlin [49] , and apparent errors were resolved by deleting the respective genotypes of all family members . The genotypes of two participants showing <80% called genotypes were completely removed from the data . In a second step , SNP markers were excluded if they showed either ( i ) a deviation from Hardy-Weinberg equilibrium in founders at the nominal p < 0 . 001 test level , ( ii ) a genotype calling fraction of <80% , or ( iii ) a heterozygosity of <5% . A quantitative trait locus autosomal linkage analysis was performed using the model-free nonparametric linkage method ( NPL ) [50] and the Haseman-Elston method ( HE ) [51] , assuming an additive genetic model . In order to allow the simultaneous analysis of all SNP loci on a given chromosome in a multipoint approach , we adapted GENEHUNTER [52] to a 64 bit version [53] . Asymptotic z scores from NPL and LOD scores from HE are reported . Additionally , empirical p-values were determined using 100 , 000 replicates on the basis of the mean information content in the multipoint SNP analysis , which was 0 . 976 ( SD ± 0 . 029 , range 0 . 510–1 . 000 ) . Specifically , a single marker was simulated with 20 equally frequent alleles , thus yielding a heterozygosity of 0 . 95 . For the simulation , family structures and phenotypes were left unchanged . Details on this Monte-Carlo approach can be found , e . g . , in [54] . A linkage signal of p < 10−4 in either NPL or HE was considered significant [55] . In addition , we describe two linkage signals which were below the threshold of significance , one signal of p < 5·10−4 and another signal of p < 10−3 obtained at the same genomic region with two largely independent phenotypes . We calculated locus-specific heritabilities of additive effects using the regression approach of Sham and colleagues [56] . Applying this method , we estimated the mean and variance from the data and fixed the heritability to 0 . 5 . Decreasing the overall heritability led to moderate increases of the locus specific heritability . Candidate genes were defined as genes relevant to red blood cell structure , red blood cell metabolism or the inflammatory response . | In tropical Africa , virtually all children become infected with malaria parasites . Most of them experience several malaria attacks per year , and over a million die from disease complications . Sickle-cell anemia , thalassemias , and other inherited red blood cell disorders indicate that malaria has selected for human genetic variants , but no attempts have so far been reported to systematically screen the human genome for malaria-resistance factors . We describe a genome-wide linkage analysis performed in children living in rural Ghana , West Africa , including approaches to select an informative study cohort and to assess , over a period of 8 mo , individual disposition to malaria parasitemia , fever episodes , and anemia . Families carrying the known malaria-protective red blood cell disorders were excluded , infection intensities were adjusted to the use of mosquito-protection devices , and parasitological and clinical findings were corrected according to the state of partial malaria immunity , which , under constant exposure , gradually develops over the first 10 y of life . The study revealed several genomic regions showing evidence for linkage to the various malaria phenotypes recorded , among them a prominent signal on Chromosome 10 correlated to the frequency of fever episodes . Future identification of genes involved is expected to reveal previously unrecognized pathways that may protect children against malaria . | [
"Abstract",
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] | 2007 | Genome-Wide Linkage Analysis of Malaria Infection Intensity and Mild Disease |
Our recent study on the functional analysis of the Knickkopf protein from T . castaneum ( TcKnk ) , indicated a novel role for this protein in protection of chitin from degradation by chitinases . Knk is also required for the laminar organization of chitin in the procuticle . During a bioinformatics search using this protein sequence as the query , we discovered the existence of a small family of three Knk-like genes ( including the prototypical TcKnk ) in the T . castaneum genome as well as in all insects with completed genome assemblies . The two additional Knk-like genes have been named TcKnk2 and TcKnk3 . Further complexity arises as a result of alternative splicing and alternative polyadenylation of transcripts of TcKnk3 , leading to the production of three transcripts ( and by inference , three proteins ) from this gene . These transcripts are named TcKnk3-Full Length ( TcKnk3-FL ) , TcKnk3-5′ and TcKnk3-3′ . All three Knk-family genes appear to have essential and non-redundant functions . RNAi for TcKnk led to developmental arrest at every molt , while down-regulation of either TcKnk2 or one of the three TcKnk3 transcripts ( TcKnk3-3′ ) resulted in specific molting arrest only at the pharate adult stage . All three Knk genes appear to influence the total chitin content at the pharate adult stage , but to variable extents . While TcKnk contributes mostly to the stability and laminar organization of chitin in the elytral and body wall procuticles , proteins encoded by TcKnk2 and TcKnk3-3′ transcripts appear to be required for the integrity of the body wall denticles and tracheal taenidia , but not the elytral and body wall procuticles . Thus , the three members of the Knk-family of proteins perform different essential functions in cuticle formation at different developmental stages and in different parts of the insect anatomy .
Chitin , a homopolymer of β-1 , 4 linked N-acetyl glucosamine units , is an essential component of the extracellular matrix of insect cuticle [1] . Chitin , the major component of the procuticle , is synthesized by the integral membrane protein , chitin synthase-A ( Chs-A ) and is deposited outside of the cell in the form of bundles of fibers [2] , [3] . Several such bundles of nanofibers and proteins are then arranged in the form of a chitin/protein sheet , or lamina [4] , [5] . In some parts of the procuticle , successive layers of chitin sheets are deposited in such a way that the horizontal axes of adjacent laminae follow a helical path [4] . Several groups of cuticle-associated proteins have been implicated in organizing the cuticle into a complex multi-layered structure with distinctly different properties in different parts of the insect's body plan [6] , [7] . The role , if any , of specific cuticular and epidermal cell plasma membrane proteins in organizing chitin laminae into such helicoidal or orthogonal bundles has not been investigated in detail . During development , in addition to cuticle growth , insects have to undergo a process of molting , during which old cuticle is replaced by a new one . Chitinases present in the molting fluid degrade chitin from the old cuticle , providing substrate for the new cuticle synthesis [8] , [9] , [10] . Our recent study with the red flour beetle , Tribolium castaneum , has shown co-localization of chitinase ( chitinase-5 ) with chitin in the newly synthesized procuticle leading to the paradoxical situation where the nascent chitin needs protection from degradation by chitinases present in the molting fluid [11] . The presence of a chitin-binding protein , Knickkopf ( Knk ) , in the developing new procuticle was shown to be important for protecting chitin from degradation by active chitinases [11] . Knk was initially identified in a search for mutants defective in cuticle integrity in developing embryos of Drosophila melanogaster [12] and molecularly characterized several years later [13] . Although the exact mechanism of protection of chitin by Knk remains unclear , its ability to bind to chitin was predicted to lead to a masking effect that prevents chitin from degradation by chitinases . Furthermore , Knk was also shown to be important for cuticle integrity and laminar organization of the embryonic procuticular chitin in D . melanogaster [14] and T . castaneum [11] , [15] . The trafficking of Knk to the procuticle requires the participation of Retroactive , a protein belonging to the Ly-6 family , whose members are involved in a variey of protein-protein interactions [15] . In the current study , we describe the identification of two paralogous Knk-like genes , TcKnk2 and TcKnk3 , in the genome of T . castaneum and determine their patterns of expression during development and in different tissues including midgut , hindgut and carcass . RNA interference ( RNAi ) studies reveal important roles of these two additional Knk-family genes in embryonic as well as post-embryonic stages of development that are distinctly different from those of Knk . The protein products of these two genes are required for the maintenance of the integrity of cuticular structures in the body wall denticles and tracheal taenidia . A bioinformatics search of genomes of other insects indicate the presence of orthologs of TcKnk2 and TcKnk3 in all other insect species examined , suggesting conserved essential functions for these orthologs during insect cuticle morphogenesis in specialized cuticular structures .
Two previously uncharacterized homologs of the TcKnk ( LOC655087 ) ( JN314843 ) gene were detected in a search of the T . castaneum genome using the NCBI TBLASTN program and TcKnk protein sequence as the query . These TcKnk-like genes , which we designate as TcKnk2 ( LOC 661990 ) ( KF475699 ) and TcKnk3 ( LOC 657143 ) ( KF475700 ) , map on linkage group 9 , at positions 11 . 0 cM and 34 . 1 cM , respectively . TcKnk maps to position 34 . 1 ( same recombinational map position as TcKnk3 ) . These two genes are closely linked on chromosome 9 and are separated by only 330 kbp of intervening sequences . Comparison of the sequence of a putative full length TcKnk2 cDNA clone with the genomic sequence for TcKnk2 indicated that it is composed of seven exons capable of encoding a protein of 70 . 56 kDa with 632 amino acids ( Fig . 1A ) . A similar strategy utilized to clone cDNAs for TcKnk3 indicated the presence of multiple transcripts for this gene . By RT-PCR using a pair of primers located at the 5′- and 3′-ends of the deduced TcKnk3 mRNA and cDNA from the pharate adult stage as template , we obtained a long cDNA fragment with a size of ∼4 kb . We cloned this cDNA fragment and randomly chose two clones for sequencing . Sequence comparisons of cDNA clones ( both had exactly the same sequence ) and the mRNA sequence predicted by the NCBI gene model for this gene ( XM_963619 with 8 exons ) indicated that they were in agreement except for the absence of a 55 nucleotide-long stretch in the last exon in these two cDNA clones . We have designated this “missing” stretch as “exon 8a” and the rest of the last exon as exon 9 . We have named the transcript lacking this exon as TcKnk3-Full Length-1 ( TcKnk3-FL-1 ) ( KF475700 ) ( Fig . 1B ) . Inspection of the sequences flanking this 55 nucleotides-long presumptive exon 8a indicated that it has the potential to be an intron because it begins with 5′-GT— and ends in —CAG-3′ as expected of typical introns . To determine whether some TcKnk3 transcripts lack this exon 8a sequence as predicted by the NCBI model , we used the same pharate adult cDNA template that was used to obtain the two long cDNA clones for additional PCR reactions using primers flanking exon 8a . We could obtain two amplified DNA fragments differing in size using these primers ( Fig . S1 ) . Sequencing of the larger fragment indicated that it had all 55 nucleotides of “exon 8a” sequences . We have named this transcript as TcKnk3-Full Length-2 ( TcKnk3-FL-2 ) ( KF475701 ) ( Fig . 1B ) . The relative abundances of these two PCR amplification products indicated that the transcripts without exon 8a predominate at the pharate adult stage ( Fig . S1 A ) . Therefore , this 55 nucleotide stretch is indeed present only in a minority of mature transcripts , at least at this stage of development . Inclusion of this intron results in the read-through of exon 8a , a shift in reading frame , and extension of the ORF to a stop codon farther down-stream , leading to a much larger protein . The predicted lengths of TcKnk3-FL-1 and TcKnk3-FL-2 are 731 and 1205 amino acids , respectively . They share the first 728 amino acids starting from the N-terminus , differing only in the C-terminal region . Fig . S2 shows an alignment of the amino acid sequences of the Knk3 proteins encoded by these two transcripts . 3′-RACE using a forward primer in exon 5 and oligo ( dT ) as the reverse primer revealed the presence of polyadenylated RNAs with two distinct sizes . One had a size of ∼4 kb consistent with that predicted from the gene model proposed in Fig . 1B , while the other was much shorter than the full length mRNAs for TcKnk3 ( with or without exon 8a ) . We have cloned the cDNA corresponding to this short transcript as described in the Materials and Methods section and named this transcript TcKnk3-5′ ( KF475702 ) . TcKnk3-5′ cDNA is 1942 nucleotides long and includes an ORF of 1209 nucleotides , which encodes a protein of 403 amino acids ( 45 . 6 kDa ) and a pI of 9 . 28 . The N-terminal 263 amino acid sequence of this encoded protein was identical to that of the full-length protein predicted by the NCBI gene model , but included an additional 141 amino acids at its C-terminus not present in the predicted products of the longer clones . Nucleotide sequence comparisons with the TcKnk3 genomic sequence indicated that TcKnk3-5′ cDNA is the result of an alternative splicing event that led to the inclusion of an exon ( named exon 5a ) and a polyadenylation site encoded within intron 5 of the NCBI gene model . Indeed , there is a polyadenylation signal 26 nucleotides upstream of the poly-A tail of this shorter 5′-transcript . The inclusion of exon 5a , which is 1156 nucleotides long , resulted in the translation of the 141 codons not present in the two full-length mRNAs ( FL-1 and FL-2 ) . This RNA contains in addition to the first 5 exons of TcKnk , an additional exon that we have designated as “exon 5a” with a 3′-UTR of 733 nucleotides ( including the stop codon but excluding the poly A tail ) ( Fig . 1B ) . To confirm the presence of the long and short transcripts identified above as well as to investigate the possibility of additional transcripts for the TcKnk3 gene due to alternative splicing and/or polyadenylation , we performed a Northern blot analysis using 32P-labeled probes derived from the 5′- and 3′- regions of the TcKnk3 gene as described in the Materials and Methods section . We anticipated that our chance to detect minor transcripts corresponding to the 5′- or 3′-regions of the gene could be enhanced by selectively down-regulating the steady state levels of one or more of the above-described transcripts using dsRNAs targeting exons either in the 5′- half or the 3′-half of this gene . Aliquots ( 10 µg ) of total RNA extracted from pharate adult insects injected with control ( TcVer ) , TcKnk3 exon 9 or TcKnk3 exon 5 dsRNA were used for Northern blot analysis ( Fig . 2 ) . Hybridization with the 32P-labeled 5′-probe ( exon1-exon5 region ) detected a ∼4 kb transcript in control RNA ( extracted from TcVer dsRNA-treated insects ) , a result consistent with the sizes of the two TcKnk3-FL cDNA clones that we have sequenced ( Fig . 1B ) . No other transcripts with smaller sizes were detected . However , in RNA extracted at the same developmental stage from animals treated with exon 9 dsRNA ( designed to suppress the full length transcript ) , the 5′-probe detected only the shorter TcKnk3-5′ transcript . The absence of the full-length ( TcKnk3-FL ) transcript in this RNA preparation confirmed that we are indeed detecting only the TcKnk3-specific transcripts and not transcript of closely related TcKnk and TcKnk2 genes . In RNA from animals treated with dsRNA for exon 5 , neither the full-length 4 kb transcript nor the TcKnk3-5′ transcript were detected as expected , but surprisingly a slightly larger band ( >2 kb ) was detected . In a second experiment with a duplicate blot of the same three RNAs , a 32P-labeled TcKnk3-3′ fragment was used as the hybridization probe . The 3′-probe also detected the TcKnk3-FL transcript in the control RNA and its level was undetected after administration of exon 9-specific dsRNA as expected . Once again , the shorter transcripts were not detected in this RNA . Surprisingly , there was a strong autoradiographic band corresponding to the >2 kb-long transcript in the blot probed with the 3′-probe . Compared to the control RNA in which the 3′-transcripts were undetectable ( lane labeled V ) , there was substantial up-regulation of the steady-state level of this transcript when the transcripts for the full-length and 5′-TcKnk3 transcripts were down-regulated by exon 5-specific dsRNA ( lane labeled E5 , Fig . 2 ) . We presume that this transcript is derived predominantly from the 3′-half of the TcKnk3 gene , as it does not hybridize with the 5′-probe . The largest ∼4 kb band ( in lane labeled V ) is presumably a mixture of molecules with or without exon 8a sequences . There were some minor bands in some samples . We presume that these represent pre-mRNA or additional alternative splicing products . Taken together , these results provide further evidence that the TcKnk3 gene has the potential to yield at least five different forms of transcripts that differ in sizes and exon composition and that their relative abundances can be altered under appropriate conditions . The identification of two additional paralogous genes encoding Knk-family proteins in the T . castaneum genome prompted us to investigate whether Knk-family genes are present in other insect orders as well . A search of sequence databases of several insects with fully sequenced genomes including those of D . melanogaster , A . gambiae , A . aegypti , C . quinquefasciatus , A . mellifera , A . pisum , N . vitripennis and P . humanus corporis indicated that orthologs of TcKnk2 and TcKnk3 are present in all these genomes ( Fig . 3 ) . The predicted sequences of the full-length Knk-family proteins from several insects were used to construct a phylogenetic tree using the neighbor-joining method [16] . This analysis indicates that the Knk-family proteins from all of these insect species neatly separate into three clades , with each clade having one Knk gene from each insect . Conservation of all three TcKnk-family proteins in different insect species indicates an essential role for these proteins presumably for the development of the chitinous exoskeleton in many insect species . However , our search failed to identify orthologs in non-insect arthropods such as the water flea and the deer tick , nor were any found in the genomes of the sea urchin and nematode , even though all of them do have orthologs of TcKnk [11] . Both DmKnk and its ortholog , TcKnk , are predicted to encode putative C-terminal GPI-anchored proteins containing two tandem N-terminal DM13 domains and a central dopamine β-monooxygenase N-terminal-like ( DOMON ) domain followed by a C-terminal sequence that remains uncharacterized [14] . The domain organizations of TcKnk-family of proteins were predicted using the SMART protein database . Domain analysis indicated that all three members of the TcKnk-family of genes are capable of encoding proteins with two DM13 domains and a DOMON domain ( Fig . 4 ) . These domains are followed by a middle region ( ∼100 amino acids ) of low sequence similarity followed by a C-terminal stretch of about 200 amino acids , which is highly conserved among these three Knk-family proteins ( Fig . S3 ) . This C-terminal stretch appears to be unrelated to any of the well characterized protein domains currently in the SMART database . The three proteins of the Knk family differ with respect to the presence or absence of membrane-anchoring sequences at their carboxyl termini . While TcKnk and TcKnk3-FL-2 proteins are predicted to have a GPI anchor sequence at their C-termini , TcKnk2 is predicted to have a trans-membrane segment at the C-terminal end ( Fig . 4 ) . The protein encoded by the shorter TcKnk3-5′-transcript has the two DM13 domains but is missing the DOMON domain and all of the downstream sequences . It has no predicted GPI anchor or TM segments ( Fig . 4 ) . Figure S3 shows an alignment of these three TcKnk-family full-length proteins , which emphasizes the similarities in the N-terminal and C-terminal regions and differences in the middle parts of these proteins . To determine whether there are differences in the expression patterns of TcKnk-family genes during T . castaneum development , we analyzed the steady-state levels of TcKnk2 , TcKnk3-FL-1 , TcKnk3-FL-2 , TcKnk3-5′ and TcKnk3-3′ transcripts using cDNA templates prepared from RNA extracted from embryos , young larvae , mature larvae , pharate pupae , pupae , young adults ( 0 d-old ) and mature adults ( 10 d-old ) . TcKnk2 transcripts were detected at all stages of insect development except the embryonic stage , with the highest expression levels being found in the pupal stage ( Fig . 5A ) . TcKnk3-FL as well as TcKnk3-5′ transcripts were also barely detectable in embryos but were abundant in young larvae , pharate pupae , pupae and young adults ( Fig . 5A ) . These results are almost identical to those for TcKnk expression , except that TcKnk3 transcripts peaked in young adults rather than in pupae . Transcripts for both of these genes ( as well as TcKnk ) were detected in carcass and hindgut , but not in midgut tissue , consistent with a role for these proteins in cuticle-forming tissues but not in peritrophic matrix ( PM ) -forming tissues ( Fig . 5B ) . We have shown previously that RNAi of TcKnk results in arrest of insect development at every molt [11] . To determine whether the paralogous TcKnk2 gene has any role in T . castaneum development and molting , we injected young larvae , last instar larvae and pharate pupae with two dsRNAs targeting two different regions of the TcKnk2 gene ( dsTcKnk2 ) , but the results shown are from dsRNA1 ( Table S1 ) . dsVermilion ( dsTcVer ) , a dsRNA targeted specifically against tryptophan oxygenase , a gene responsible for eye pigmentation in T . castaneum , was used as a control . About 55% of the insects subjected to TcKnk2 dsRNA treatment at the young larval , last instar larval or pharate pupal stages exhibited lethal phenotypes at the pharate adult stage . There was no evidence of developmental arrest at the larval-larval or larval-pupal molts . At the pharate adult stage , the dsRNA-treated insects exhibited a clear molting defect as a result of an inability to shed the old pupal cuticle ( Fig . 6A ) . The remaining insects metamorphosed into adults , but about 30% of the dsRNA-injected insects had a weaker phenotype . These insects exhibited a split wing phenotype as a result of an improper folding of the hindwings and elytra ( Fig . 6A ) . All adults with this hypomorphic phenotype died within 10–15 days of adult emergence , whereas the remaining adults ( ∼15% of dsRNA-treated insects ) were normal and showed no visible phenotype or mortality in comparison with control animals injected with dsRNA for TcVer . TcKnk2 transcript levels were significantly down-regulated after dsRNA TcKnk2 treatment in comparison with control dsRNA TcVer-injected insects ( Fig . 6B ) . The molting defect observed at the pharate adult stage after TcKnk2 RNAi was similar to that of TcKnk or TcChs-A RNAi phenotypes . To determine whether this is due to cross-knockdown of transcripts of TcKnk or other genes involved in chitin metabolism such as chitin synthase-A ( TcChs-A ) or chitinase-5 ( TcCht5 , which leads to developmental arrest at a slightly later pharate adult stage ) , we performed RT-PCR using cDNA prepared from RNA extracted from 3-d-old pupae ( n = 4 ) after TcKnk2 dsRNA treatment . RT-PCR using gene-specific primers confirmed specific knockdown of TcKnk2 transcripts upon TcKnk2 RNAi with no apparent decrease in the transcript levels for TcKnk , TcKnk3 , TcChs-A or TcCht5 ( Fig . 6C ) . These results suggest that the observed phenotypes are due to depletion of TcKnk2 transcripts and not the result of down regulation of transcripts for other genes of chitin metabolism studied here . The finding that there are multiple transcripts corresponding to the TcKnk3 gene presented some challenges in determining their function by RNAi . dsRNAs corresponding to regions in several exons were designed to down-regulate selected or multiple transcript ( s ) as desired . A dsRNA corresponding to the 55 bp region of the “exon 8a” was designed to down-regulate only the transcripts with this sequence . These dsRNAs were injected into insects at young larval , last instar larval and pharate pupal stages of T . castaneum development . To determine the specificity and effectiveness of RNAi , we examined the levels of TcKnk3-FL , TcKnk3-5′ and TcKnk3-3′ transcripts after each dsRNA treatment using cDNAs prepared from RNA extracted from pharate adult insects ( four days after dsRNA injections ) utilizing appropriate forward and reverse primers . Significant depletion of the targeted transcript ( s ) was observed with each dsRNA treatment ( Fig . S4 ) . While the levels of TcKnk3-FL ( with or without exon 8a ) transcripts were significantly reduced after treatment with all of the dsRNAs tested , the TcKnK3-5′ transcript was affected only by dsRNAs designed from exon 1 to exon 5 sequences but not by dsRNAs for downstream exons . dsRNA for exon 8a affected only those transcripts with exon 8a sequences , but not those without it , including the TcKnk-5′ transcript ( Fig . 7B ) . Despite substantial depletion of both TcKnk3-FL and TcKnk3-5′ transcripts by dsRNA treatment targeting exon 5 ( Fig . S4 , Table S2 ) , no visible phenotype or mortality was observed . All insects injected with this dsRNAs at any stage of development produced adults without molting defects or visible abnormalities . Unexpectedly , injection of exon 9-specific dsRNA that also led to a similar depletion of the TcKnk3-FL transcripts resulted in 100% mortality at the pharate adult stage of molting . No molting defects or abnormal phenotypes were observed during the earlier stages of development including larval-larval and larval-pupal molts . To understand the differences in the RNAi results from exon 5- versus exon 9-specific dsRNA treatments , we injected insects from different stages of development with dsRNAs specific for different exons including exon 1 ( with the 5′-UTR region ) , exons 2 and 3 ( spanning both exons ) , exon 6 , exon 8 and exon 9 . After injection of dsRNA for exon 1 or exons 2–3 , insects developed normally into adults in comparison with control dsRNA ( TcVer ) -treated insects similar to those injected with dsRNA for exon 5 . However , insects treated with either exon 6 or exon 8 dsRNA showed molting defects similar to those seen after exon 9-specific RNAi , resulting in mortalities of 100% and 82% , respectively , at the pharate adult stage of development ( Fig . 7A ) . Even the survivors ( 18% ) of the exon 8-specific dsRNA treatment exhibited a weak phenotype with split elytra as adults ( Fig . 7A ) . The failure of dsRNAs for the exons near the 5′-end of the TcKnk3 gene ( exon 1 through exon 5 ) to yield any visible alterations in phenotype in contrast with the effectiveness of the dsRNAs for the down-stream exons ( exon 6 and downwards ) indicated the possibility that the transcript derived from the 3′ region of the TcKnk3 gene , which lacked sequences corresponding to several of the exons in the 5′-half of the gene may be the only one essential for insect survival . Presumably this is the same transcript that accumulates in the pharate adult stage when the 5′ and FL-transcripts are down-regulated by treatment with dsRNAs for the exon 5 ( Fig . 2 ) . Upon RNAi with dsRNAs for downstream exons , this short transcript might have been depleted leading to the observed phenotypes . Since we found exon 8a sequences in a minority of transcripts , we also investigated whether dsRNA for this exon could yield the same phenotype as exon 8-specific dsRNA . About 65% of the insects injected with exon 8a dsRNA exhibited mortality at the pharate adult stage and 15% of the surviving adults had a weaker phenotype similar to that observed after exons 6 , 8 and 9 dsRNA treatments , indicating that transcripts with exon 8a are critically important for development and molting . RT-PCR analysis of total RNA from insects treated with dsRNA for exon 8a indicated that this treatment did not result in visible depletion of the full-length transcripts ( Fig . 7B ) . RT-PCR analysis using primer pairs flanking exon 8a specifically designed to detect transcripts with and without exon 8a indicated that exon 8a dsRNA specifically depleted only the transcripts containing exon 8a without appearing to affect those without this sequence . These data further suggest that transcripts without exon 8a ( whether full length or shorter transcripts ) are not essential for survival of the insects during the pupal-adult transformation . From all of the data from multiple dsRNA treatments , we conclude that only the TcKnk3-3′ shorter transcripts with the 55 nucleotides-long exon 8a sequences appear to be essential for survival and molting . RT-PCR reactions using RNA preparations made from insects at different developmental stages and a pair of forward and reverse primers flanking exon 8a revealed a variation in the relative abundance of transcripts , which differ in the presence or absence of exon 8a sequences . The smaller fragment ( without exon 8a ) was more abundant than the larger fragment in the RT-PCR products of RNA isolated at all pupal stages except on pupal day 2 when the relative abundance was reversed ( Fig . S1B ) . The amount of the larger fragment ( with the exon 8a sequence ) was nearly constant during the pupal stage while the smaller transcript ( without the exon 8a sequence ) underwent dramatic changes in abundance . During the mid-pupal stage ( P2 ) , transcripts with exon 8a sequences predominate . Our recent work has uncovered an important role for TcKnk in protection of procuticular chitin from chitinases [11] . TcKnk has been shown to be important both for the maintenance of chitin levels and its laminar organization in the procuticle [11] , [14] . To determine whether TcKnk2 and TcKnk3 genes have any roles in cuticular chitin maintenance , we performed total chitin content analysis of larvae treated with TcKnk2- and TcKnk3-3′ ( exon-9 ) -specific dsRNAs . Insects were collected at pharate pupal and pharate adult stages of development four to five days after dsRNA injections into either last instar larvae or pharate pupae . There was a significant decrease in chitin content after either TcKnk2 or TcKnk3-3′ dsRNA treatment at the pharate adult stage of development , but not at the pharate pupal stage , indicating an essential role for these two genes in cuticular chitin level maintenance specifically at the pharate adult stage of development ( Fig . 7C ) . However , the decrease in chitin levels observed after RNAi for these two Knk paralogs was less substantial than that observed after RNAi for TcKnk [11] . To further determine the roles , if any , of TcKnk-family genes in organization of the procuticular chitin , we performed transmission electron microscopic ( TEM ) analysis of pharate adult elytral cuticle , lateral body wall denticle cuticle and the tracheae . TEM of elytra from control ( TcVer ) -dsRNA-treated insects revealed a horizontally arranged laminar organization of the procuticular chitin , that is , parallel to the apical surface of the epidermal cells ( Fig . 8; panel E1 ) . RNAi of TcKnk resulted in the loss of laminar organization of the elytral cuticle ( Panel E2 ) . A similar loss of laminar organization of chitin was also observed under the denticle-like structures associated with specific regions of the lateral body wall , which interlock with corresponding regions on the inner side of the elytra ( compare panels D1 and D2 ) , which we denote as “Velcro” ( Arakane et al . , unpublished data ) . These Velcro-like denticles have hooked structures and that are complementary to specialized structures found in specific regions of the elytra capable of interactions similar to fibers of “Velcro” that snap them together tightly . In addition , electron dense material accumulates under these folds ( indicated by black arrows in Fig . 8 ) . The tracheal taenidial cuticle was abnormal in shape ( compare panel T1 and T2 ) . Simultaneous down-regulation of chitinase 5 transcripts failed to ameliorate these morphological abnormalities brought about by RNAi for Knk ( panels E3 , D3 and T3 ) . Thus RNAi of TcKnk affects not only the elytral and body wall cuticle as reported previously [11] , but also other cuticles such as those associated with the tracheal cuticle and body wall “Velcro denticle” cuticle . Similar TEM analyses were conducted after RNAi for TcKnk2 and TcKnk3 using abdominal sections of insects with the majority phenotype , i . e . insects that failed to expand their elytra and failed to contact their abdomen . However , no significant difference in the laminar architecture of elytral procuticular chitin in comparison with control insects was seen after TcKnk2 and TcKnk3-3′ dsRNA treatments ( Fig . 8; compare panels E1 , E4 and E6 ) . Similarly , the laminar architecture of the body wall cuticle was also unchanged . In insects treated with TcKnk2 dsRNA , a rather subtle phenotype is observed in the denticle-like structures associated with “Velcro denticles” ( Fig . 8; panel D4 ) . These “Velcro denticles” can be normally divided into two regions , the basal flat and the upper protruding region . The basal region procuticle is arranged in a laminar fashion ( indicated by bracket in Fig . 8 ) , while in the protruding bulge regions procuticular chitin does not adopt a preferred organization . In TcKnk2 dsRNA-treated insects , an amorphous electron-dense material occasionally accumulates within the protruding denticle region ( Fig . 8; panel D4 , black arrows ) . This phenotype is reminiscent of that observed in insects injected with dsRNA for TcKnk ( Fig . 8; panel D2 ) . However , simultaneous knockdown of TcKnk2 with TcCht5 transcripts rescued the Velcro-denticle phenotype with the disappearance of the electron dense materials ( Fig . 8; panel D5 ) . TcKnk3-3′ dsRNA-treated insects did not exhibit any obvious phenotype in the denticles ( Fig . 8; panel D6 and D7 ) . dsRNA treatment for TcKnk2 perturbed the organization of the taenidial cuticle and showed accumulation of electron dense material within the taenidial procuticle ( Fig . 8 , panel T4 black arrow head ) . Similar results were also seen after RNAi for TcKnk3-3′ ( Fig . 8 , panel T6 , black arrow head ) . Upon simultaneous down regulation of TcKnk2 and TcCht5 , or TcKnk3-3′ and TcCht5 , very little electron-dense material was observed in the respective taenidial procuticle and the tracheal shape was also recovered , indicating rescue of the phenotype ( Fig . 8 , panel T5 and T7 ) . Taken together , our ultrastructural analysis indicates that like TcKnk , both TcKnk2 and TcKnk3 are involved in protection of chitin in the “Velcro denticles” and tracheae from chitinase . It is interesting to observe that TcKnk2 and TcKnk3-3′ dsRNA treatment defects are associated with structures in which chitin is normally not organized in a laminar fashion . The lethal phenotypes observed at the pharate adult stage of molting after TcKnk2 and TcKnk3-3′ RNAi could result from loss of integrity of body wall denticles and a failure to develop tracheal taenidia normally . These results indicate significant roles for TcKnk-like proteins in chitin maintenance and cuticle integrity in the body wall denticles and tracheae .
Even though the knickkopf mutation was described as early as 1984 [12] , its molecular characterization was not accomplished until much later , when Ostrowski et al . , [13] identified the gene associated with the knk mutation characterized by the “broken-head” and the “blimp” phenotype at the embryonic larval stage . This gene was also shown to be essential for organization of the filamentous chitin structures during tracheal tubule growth and cuticle differentiation [5] . Our recent study has demonstrated an important role for TcKnk in protecting the newly synthesized procuticular chitin from degradation by active chitinases by co-localizing with chitin in the procuticle [11] . Additionally , TcKnk was shown to bind to chitin and to be important for the laminar organization of procuticular chitin in elytral and body wall cuticles of T . castaneum . During a bioinformatics search of the genomes of insect species that have been fully sequenced and annotated , we came across two other paralogs of the TcKnk gene , which have not been studied so far in any insect species . Domain analysis of TcKnk-like proteins revealed a similar domain organization compared to TcKnk . Like TcKnk , TcKnk2 and TcKnk3 also have two N-terminal DM13 domains , one DOMON domain in the middle and a C-terminal domain that is highly conserved in all three members of this family of Knk proteins . Although the functions of the DM13 and DOMON domains are unclear , they have been predicted to have important roles in redox or electron transfer reactions [17] . The DOMON domain is also predicted to bind with heme or sugars [18] . In a recent work it was demonstrated that residues in the Drosophila Knk DOMON domain predicted to be important for substrate binding are essential for viability [19] . Biochemical studies showed that TcKnk extracted or released from cells has a very strong affinity for colloidal chitin [11] . The widespread occurrence and retention of orthologs of TcKnk2 and TcKnk3 genes in other insect orders belonging to hemipteran , dipteran , lepidopteran and hymenopteran lineages indicates that duplication of the ancestral Knk gene from which these paralogs were derived must have occurred before the branching of these orders and that these Knk-like genes probably perform essential functions . While they are present in several insect orders , their absence in the deer tick , water flea and nematodes , which have only one Knk homolog , suggests that the ancient Knk gene has undergone gene duplication in the progenitor of those insect orders and that the insect paralogs assumed specialized functions in different cuticle-forming tissues . RNAi studies for TcKnk2 and TcKnk3 confirmed the importance of these genes for cuticle morphogenesis apparently not fulfilled by Knk alone . TcKnk2 dsRNA treatment at larval stages led to molting arrest at the pharate adult stage of development in a majority of the animals . TcKnk3-3′ transcript ( but not the full length or 5′-transcripts ) also appears to be essential for molting and survival during the pupal adult transformation . The finding that molting defects were observed after administration of dsRNAs for TcKnk2 and TcKnk3-3′ , even though these dsRNAs did not result in depletion of the TcKnk transcripts , reveals specialized functions for TcKnk2 and TcKnk3 during cuticle morphogenesis , which are not redundant with those of TcKnk . Unlike TcKnk , which is required for every molt , TcKnk2 and TcKnk3 have essential roles only during morphogenesis to the adult stage . Earlier molts do not seem to be affected . TcKnk has been shown to be important for arranging the chitin into laminae in the elytral and adult abdominal body wall cuticles in T . castaneum and in the larval cuticle in D . melanogaster [11] , [14] . We report here that TcKnk is also required for maintenance of the normal shape of the denticles and the tracheal taenidiae . While TcKnk affects the total chitin content dramatically both at the pharate pupal and pharate adult stages [11] , RNAi of the two paralogous TcKnk-like genes are not manifested in altered chitin content at the pharate pupal stage , consistent with the absence of observable effects on molting or morphology at the larval-to-pupal molt . Even though there was a statistically significant reduction in total chitin content after RNAi for TcKnk2 as well as TcKnk3-3′ at the pharate adult stage , we could not detect changes in the laminar architecture of the elytral procuticle or body wall cuticle as has been demonstrated following TcKnk RNAi [11] . Therefore , we conclude that these two paralogous Knk genes are not required for organization of the elytral cuticle or the body wall cuticle . On the other hand , TEM analysis of denticles in the lateral body wall and tracheae of TcKnk2 and TcKnk3-3′ dsRNA-treated pharate adult insects showed accumulation of electron-dense material ( probably proteins ) in the chitin matrix of Velcro-denticle bulges and the taenidia , suggesting disorganized procuticles in these structures . Therefore , TcKnk-like genes may have a secondary role in cuticular chitin maintenance and organization in specialized cuticle-forming tissues such as those involved in the formation of the denticles and tracheal taenidia . The accumulation of electron-dense material in tracheae after TcKnk2 and TcKnk3 dsRNA treatments indicates a possible role for TcKnk-like proteins in influencing proper distribution of putative cuticular proteins into the tracheal cuticle . In a minority of insects after RNAi for TcKnk2 and TcKnk3 , wrinkled elytra , dimpled pronotum and wing defects were observed at the macroscopic level in spite of the presence of normal laminar architecture at the TEM level ( Fig . 7 ) . There are many potential causes of gross elytral malformations beyond disruption of laminar architecture as we have reported previously in insects after RNAi of genes for neuropeptides and their receptors [20] . Even something so simple as an adhering fragment of pupal exuvium that impedes complete adult emergence and subsequent expansion of elytra can result in elytral malformations [21] . Only Knk and Knk3-FL-2 are predicted to have a PI-PLC-cleavable GPI anchor and , therefore , are expected to be released to the procuticle . We have previously demonstrated that the Knk protein is indeed cleaved by PI-PLC and is associated with chitin throughout the elytral and body wall procuticles and that this association is essential for protection of chitin from molting fluid chitinases [11] . Because TcKnk3-3′ transcripts containing exon 8a sequences are also predicted to encode a protein containing the cleavable GPI-anchor , Knk3-protein may also associate with chitin in specialized cuticles such as body wall “Velcro denticles” and the taenidia . The reduction in chitin and the restoration of the chitin content following knock-down of the major chitinase , Cht5 , is consistent with such a role for the Knk3-3′ transcript-derived protein . On the other hand , Knk2 has a C-terminal trans-membrane domain and topological predictions indicate that the rest of the protein will be exposed to the extracellular side . The restoration of chitin levels in insects following down-regulation of both TcKnk2 and TcCht5 transcripts suggests that Knk2 protein may also protect chitin . Whether this protection requires release of this protein from the membrane is unresolved . The detection of multiple alternatively spliced and polyadenylated variants for the transcripts of TcKnk3 , named TcKnk3-FL ( with and without exon 8a ) , TcKnk3-5′ and TcKnk3-3′ ( with and without exon 8a ) complicated our functional analysis of the transcripts for this gene . Our most important finding was that RNAi of TcKnk3 transcripts using exon 8a dsRNA resulted in molting arrest and lethality at the pharate adult stage . RT-PCR analysis indicated that this dsRNA treatment did not affect the levels of transcripts without exon8a . Thus it appeared that either full-length transcripts with exon 8a and/or truncated transcripts with exon 8a may be the only transcripts essential for survival and molting at the pharate adult stage . On the other hand , a nearly complete loss of the TcKnk3-FL transcript ( along with the TcKnk3-5′-transcripts ) , brought about using dsRNA for exon 5 ( or upstream exons ) of this gene , did not result in mortality or any visible phenotypes indicating that none of the longer transcripts ( and the TcKnk3-5′-transcripts ) and their translation products are essential for survival and/or molting . Therefore , the lethality associated with the loss of transcripts with the exon 8a sequences must be entirely due to short transcripts ( i . e . lacking exons 1 through 5 ) . Unfortunately we were unable to determine the precise start point of this short transcript because the only region where we could design the reverse primer for the 5′-RACE is the 55 nucleotide-long exon 8a . 5′-RACE reactions using two reverse primers from exon 8a- and from exon 9 failed to identify the transcription start point even when we used total RNA depleted of TcKnK3 full length and 5′-transcripts ( using dsRNA for exon 5 ) as the template . We believe that this transcript starts downstream of exon 5 based on the RNAi results described earlier . Other strategies for identifying the start site of TcKnk3-3′ transcripts using forward primers designed in intron 5 also were unsuccessful . The inclusion of exon 8a results in a shift of the open reading frame . As shown in Fig . 1 , the protein is altered to have a unique C-terminus that includes a GPI anchor . Transcripts without exon 8a predominate at most developmental stages ( see Fig . S1 ) . The exception is during the mid-pupal stage when the isoform of the Knk3 protein encoded using exon 8a is expected to peak . This is on or around pupal day 2–3 , which just precedes the time point when developmental arrest occurs using exon 8a dsRNA . Interestingly , TcKnk3 orthologs of other insect species are predicted to encode proteins that are highly similar to this long additional stretch of >400 amino acids found in TcKnk3 protein derived from read-through of exon 8a . We have carried out a careful analysis of preview data from the developmental stage time course transcriptional profiling with RNA-seq in D . melanogaster ( modENCODE Project led by Sue Celniker [22] ) publicly available in Flybase ( http://flybase . org/cgi-bin/gbrowse/dmelrnaseq/ ) . RNA Seq data of the DmKnk3 ortholog , Skeletor R-E , supports the notion that a similarly placed 55 nucleotide-long exon equivalent to TcKnk3 exon 8 in this gene leads to variation in the relative abundance of transcripts with and without this putative alternatively spliced exon in this dipteran species as well . It should be pointed out that the truncated TcKnk3 protein derived from the shorter transcript with exon 8a sequences will be missing the two N-terminal DM13 domains but still have the DOMON domain , exon 6 through 8 derived sequences and additional exon 9-encoded sequences at the C-terminus derived from read-through of exon 8a . This read-through will not occur in transcripts that are devoid of exon 8a because the ribosomes encounter an in-frame stop codon very close to the beginning of exon 9 . Since this longer protein is predicted to have a GPI anchor , it may be destined for transport to the plasma membrane . At present , we have been unable to identify whether it has an N-terminal signal peptide sequence to allow this protein to enter the ER . Absence of an antibody specific for this protein also precludes determination of the precise cellular location of this protein at present . Since the TcKnk3-FL-1 protein is the predominant form at most developmental stages and yet appears to be dispensable for survival , the sequences present in the C-terminal part of the TcKnk3 protein and/or the presence of the C-terminal GPI anchor may be critically important for metamorphosis at the pharate adult stage . Of the three members of the Knk gene family , only Knk3 is known to have the potential to give rise to alternatively spliced transcripts . There is no experimental evidence for additional transcripts for TcKnk and TcKnk2 genes based on 5′-RACE , 3′-RACE or RT- PCR . The finding that TcKnk3-5′ and TcKnk3-3′ transcripts appear only when the full-length transcript is down-regulated ( Fig . 2 ) raises the interesting possibility that there may be physiologically important regulatory mechanisms that control the appearance and relative amounts of these alternatively spliced transcripts . The much higher levels of accumulation of the TcKnk3-3′ transcript compared to the dsRNA TcVer-treated control , when dsRNA for exon 5 is administered to the larvae , is consistent with a regulatory mechanism that controls the position of the transcription start either upstream of exon 1 or downstream of exon 5 . Indeed a similar situation has been reported in the case of D . melanogaster ortholog of TcKnk3 gene ( named Skeletor ) , which is known to yield multiple transcripts [23] . In this study , we have demonstrated that additional complexity arises by the inclusion or exclusion of exon 8a sequences , which changes the reading frame allowing the production of a longer protein with additional C-terminal sequences . The changes in the relative amounts of transcripts with and without exon 8a during various stages of development and especially during the pupal stages indicates additional control at the level of splicing of pre-mRNA presumably through an RNA-binding protein . A similar alternative splicing mechanism that alters the ratios of alternatively spliced transcripts during the pupal stage occurs in the case of insect chitin synthase-A genes ( Tribolium castaneum CHS-A exon 8b and Manduca sexta CHS-A exon 18b ) in which transcripts with alternative exon b are known to accumulate during pupal stages and especially in the tracheae [21] , [24] , [25] . It is likely that specific splice forms of TcKnk3 accumulate in tissues making specialized cuticles such as velcro denticles and taenidia of tracheae . The finding that RNAi of either TcKnk2 or TcKnk3-3′ does not affect the laminar organization of chitin in the elytral and body wall cuticle is consistent with this idea of specialization among different paralogous members of the Knk family . In the body wall cuticle that underlies the denticles , the laminar organization of chitin also appears unaffected after RNAi for TcKnk2 and TcKnk3-3′ . However , the bulged region of velcro denticles exhibit accumulation of electron dense material ( presumably cuticular proteins ) in these specialized cuticle-forming structures . The restoration of normal ultrastructure of the denticles and the taenidia following RNAi for both TcKnk2 ( or TcKnk3 ) along with TcCht5 indicates that these two orthologous proteins also act in a manner similar to TcKnk in binding and protecting chitin . In these specialized structures , in addition to the parallel layers of chitin laminae , there are additional layers of chitin fibers whose orientation follows the shapes of denticles or taenidia . Knk2 ( predicted to be membrane-bound ) and the Knk3-3′ protein , which is predicted to have a cleavable GPI anchor , may be essential for forming these specialized cuticular structures . The level of expression in these tissues , their precise locations , and how Knk and the Knk-like proteins interact with each other will be interesting points of future studies .
T . castaneum GA-1 strain was used for all experiments . Insects were reared at 30°C in wheat flour containing 5% brewer's yeast under standard conditions as described previously [26] . A genome-wide TBLASTN search using the amino acid sequence of T . castaneum Knk ( TcKnk ) as the query was carried out at NCBI ( http://www . ncbi . nlm . nih . gov/ ) and BeetleBase ( http://beetlebase . org/ ) . This resulted in identification of two genes , which we have named T . castaneum Knk-2 ( TcKnk2 ) and T . castaneum Knk-3 ( TcKnk3 ) . Using the amino acid sequences of TcKnk2 and TcKnk3 as queries , orthologs for TcKnk-like genes were detected in all of the fully sequenced insect genomes . A second round of “BLAST” searches with the amino acid sequences of these Knk-like proteins from insects failed to identify additional Knk-like genes in insect genomes . The complete coding sequences of TcKnk2 and TcKnk3 were amplified using gene-specific primers ( Table S1 ) and cDNA prepared from RNA extracted from whole insects at the pharate adult stage of beetle development . 5′- and 3′-RACE reactions were performed to determine the upstream and downstream untranslated regions for both of these genes . The sequences of the full-length transcripts for TcKnk2 and TcKnk3 were deduced by combining the data from the above-mentioned experiments . Using a pair of forward and reverse primers derived from the 5′ and 3′-ends of the predicted mRNA , full-length cDNAs were amplified by PCR and cloned into pGEMT vector ( Promega ) . Cloning of cDNAs for TcKnk3 using the same techniques resulted in isolation of several alternatively spliced variants , which are described in the Results section . Sequencing of all cDNA clones was carried out at the DNA sequencing facility at Kansas State University . Multiple sequence alignment of TcKnk-family proteins from insects was carried out using the ClustalW software prior to phylogenetic analysis . MEGA 4 . 0 [16] was used to construct the consensus phylogenetic tree , using the neighbor-joining method . To evaluate the branch strength of the phylogenetic tree , bootstrap analysis of 5 , 000 replications was performed . To determine the developmental stage-specific expression profiles , total RNA was extracted from embryos , young larvae , last instar larvae , pharate pupae , pupae , young adults ( 0 d-old ) and mature adults ( 10 d-old ) using the RNeasy Mini kit ( Qiagen ) . For determination of tissue specificity of expression , total RNA was also isolated from midgut , hindgut and carcass ( whole body minus gut ) of last instar feeding stage larvae ( n = 10 ) according to the manufacturer's instructions . The Superscript III first–strand synthesis system for RT-PCR ( Invitrogen ) was used to synthesize first-strand cDNA according to the manufacturer's instructions . Gene-specific primers were used to detect each transcript from the prepared sets of cDNAs ( Table S1 ) . A TcRpS6 ( T . castaneum ribosomal protein S6 ) cDNA fragment was amplified using a pair of primers and served as an internal loading control for RT-PCR [27] . Two regions from two different parts of TcKnk2 gene with the greatest sequence divergence were selected as targets for RNAi ( Table S1 ) . A total of nine dsRNAs were designed for achieving RNAi of one ( or more ) of the three different transcripts of TcKnk3 by targeting different exons/introns ( exons 1 , 2 , 3 , 5 , 6 , 7-5′-terminal , 7-3′-terminal , 8 , 9 and exon 8a ) . Pairs of forward and reverse primers corresponding to these regions with additional T7 promoter sequences at the 5′-ends were synthesized ( Table S1 ) and used for the preparation of dsRNAs using an Ampliscribe T7-Flash Transcription Kit ( Epicentre Technologies ) as described previously [21] . A dsRNA for the gene responsible for eye pigmentation named T . castaneum Vermilion ( TcVer ) was used as a control for monitoring non-specific effects of dsRNA administration and for assessing the efficiency of RNAi . The purified dsRNAs were injected into penultimate instar larvae , last instar larvae and pharate pupae ( 200 ng per insect , n = 40 ) . After 4–5 d , total RNA was extracted from pools of five insects at either pupal d 3 ( for TcKnk2 ) or the pharate adult stage ( for TcKnk3 ) for measuring transcript levels by RT-PCR using gene-specific primer-pairs . The remaining insects were observed daily for any visible abnormalities and mortality . Total RNA was extracted from pharate adult insects ( n = 4 ) treated with TcVer , TcKnk3-exon9- and TcKnk3-exon5-specific dsRNAs at the prepupal stage . Ten µg RNA samples were subjected to gel electrophoresis and transferred onto a nitrocellulose membrane . 32P-labeled TcKnk3-5′- and 3′-terminal DNA probes were designed using the primers listed below . 32P-labeled TcKnk3-5′-terminal probe was prepared from a 551 bp DNA fragment amplified by using exon 1-specific forward primer ( 5′-ATGGGCCCCATCGTTGCATT-3′ ) and exon 5-specific reverse primer ( 5′-GCGAAATTCTGGGTGGGTCG- 3′ ) . TcKnk3-3′-terminal probe was prepared from a 1 , 370 bp DNA fragment obtained by using an exon 8-specific forward primer ( 5′-CACGACAAGTGCGACGAGCA-3′ ) and exon 9-specific reverse primer ( 5′-GCCGCGGAACTTATCAAAGC- 3′ ) . Duplicate blots were hybridized with 32P-labeled TcKnk3-5′- or 3′-terminal probe at 65°C overnight . High stringency hybridization and washing conditions were employed . Transcripts of differing sizes were then detected after autoradiography using an imaging plate and Typhoon scanner . TcKnk2- or TcKnk3 ( exon-9 ) -specific dsRNAs were injected into last instar larvae and pharate pupae ( n = 20 ) . Four to five days after injections , insects were collected at pharate pupal and pharate adult stages of development . Three days after administration of dsRNA for TcKnk3 to 20 female adult beetles , mating was carried out with an equal number of untreated adult males and batches of ∼200 embryos were collected every three days . Total chitin content analysis of whole insects ( n = 5 ) collected at the indicated stages was performed as described previously [28] . GraphPad Prism software was used to plot the graphs and for data analysis . For TEM analysis , insects were injected with TcKnk2- or TcKnk3 ( exon-9 ) -specific dsRNAs at pharate pupal stages of development . Five days later , pharate adult insects were collected and fixed overnight at room temperature using a fixative containing 2% para-formaldehyde and 2% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) . Samples were sectioned to obtain 70 nm thin sections , processed and observed under TEM for final imaging as described previously [11] . | We have identified two additional members of the family of Knickkopf ( Knk ) -like proteins in the genome of the red flour beetle as well as in all insect species with completely sequenced genomes . The previously characterized member of this family , TcKnk , protects chitin in the newly forming cuticle ( exoskeleton ) from degradation by the chitinase enzyme present in the molting fluid . Knk is also required for the laminar organization of the chitin polymer in the cuticle . The two newly identified members of this family , TcKnk2 and TcKnk3 , have distinctly different but related functions . They are essential for adult morphogenesis including specialized “Velcro-like” cuticular denticles found in the lateral body wall , as well as the proper development of the tracheal lining . The TcKnk3 gene gives rise to multiple transcripts as a result of alternative polyadenylation and/or splicing , but only one of these transcripts is essential for adult development . | [
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] | 2014 | Functional Specialization Among Members Of Knickkopf Family Of Proteins In Insect Cuticle Organization |
Studying the complex relationship between transcription , translation and protein degradation is essential to our understanding of biological processes in health and disease . The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates . In this work , we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle through side-by-side analysis of mRNA , translation and protein levels . Our analysis sheds new light on the significant contribution of both protein synthesis and degradation to the variance in protein expression . Furthermore , we find that translation regulation plays an important role at S-phase , while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability . Specific molecular functions are found to be co-regulated and share similar patterns of mRNA , translation and protein expression along the cell cycle . Notably , these include genes and entire pathways not previously implicated in cell cycle progression , demonstrating the potential of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling . Through this three-level analysis , we characterize different mechanisms of gene expression , discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression .
The flow of genetic information from DNA to protein entails multiple regulated steps that include mRNA transcription , processing and export , followed by translation into proteins that undergo folding , post-translational modification and eventually degradation . Steady-state protein abundance therefore reflects a dynamic net outcome of this long series of processes . Microarray and RNA sequencing ( RNA-seq ) measurements of mRNA levels are often used to profile gene expression as a proxy of function , assuming that protein and mRNA expression are highly concordant . However , advances in proteomic techniques that allow system-wide comparison of proteomes and transcriptomes have revealed that the relationship between mRNA and protein levels is much more complex than previously expected , resulting in limited correlations ( reviewed in [1] ) . This is thought to reflect the widespread effects of post-transcriptional mechanisms e . g . translational control and protein degradation . Therefore , a multi-level analysis that explores not only steady-state mRNA and protein levels but also translation and degradation rates can shed new light on the complex nature of gene expression . Several methods exist for measuring translation at a system wide level . Most recently , we introduced a mass-spectrometric ( MS ) method called PUromycin-associated Nascent CHain Proteomics ( PUNCH-P ) [2] , which quantifies the amount of nascent polypeptide chains associated with translating ribosomes . This allows direct measurement of translation products with high temporal resolution , without the potentially confounding effects of lengthy in-vivo labeling using modified amino acids , as required for techniques such as pulsed stable isotope labeling in cell culture ( pSILAC ) [3] , quantitative non-canonical amino acid tagging ( QuaNCAT ) [4] and stochastic orthogonal recoding of translation with chemoselective modification ( SORT-M ) [5] . Alternatively , translation can also be monitored by RNA-seq of ribosome-associated mRNAs or ribosome-protected mRNA fragments using methods such as TRAP [6] or ribosome profiling [7] , which generate a prediction of newly-synthesized proteins based on mRNA templates . In recent years , the relationship between transcription , translation and protein degradation has been investigated in mammalian models such as LPS-stimulated dendritic cells , resting fibroblasts , differentiating monocytes and lymphoblastoid cell lines [8–11] , and several other studies have generated matching mRNA/protein time series [12–14] ( for a recent review of published multi-omic datasets see [15] ) . Here we report a combined analysis of gene expression along the mammalian cell cycle , which requires tight regulation to ensure accurate and well-coordinated DNA replication and cell division . It has been shown that specific phases of the cell cycle involve wide-spread dynamic reprogramming of gene expression at multiple discrete levels , including transcription and mRNA degradation [16] , translation [17 , 18] , post-translational modification [19 , 20] and protein degradation , primarily through the ubiquitin-proteasome pathway [21 , 22] . Regardless of whether mRNA levels , translation or protein abundance were monitored , most of these studies found that approximately 5–10% of measured gene products oscillate across the cell cycle . However , no publication has provided a detailed account of the complex interplay between mRNA , translation and protein levels . In this work we combined microarray , PUNCH-P and total proteome measurements in a HeLa cell cycle model to characterize the dynamic relationship between the transcriptome , translatome and proteome , uncovering complex regulatory patterns .
Aiming to better understand how transcription , translation and protein degradation change along the cell cycle , we combined triplicate measurements of the transcriptome ( Affymetrix microarray [23] ) , translatome ( PUNCH-P [2] ) and proteome ( MS; this study ) from synchronized HeLa S3 cells . For all three measurements , cells were synchronized to G1 , S and G2/M phases of the cell cycle using double thymidine block , which induces an arrest at the G1/S checkpoint and thus establishes a common baseline for synchronous progression [24] . Synchronized populations were then harvested at specific time points without further addition of drugs or agents that can affect gene expression and confound comparisons between the different phases [24] ( for analysis of synchronization efficiency , see S1 Fig ) . mRNA and protein were experimentally normalized by analyzing the same amounts of biological material at each phase of the cell cycle . Translation measurements were normalized by the amount of translating ribosomes at each phase and therefore reflect relative changes in synthesis of specific proteins , rather than the absolute fluctuations in protein synthesis that occurs as cells progress through mitosis [25 , 26] . To allow direct comparison of absolute abundance between the different types of gene products , we used robust multi-array average ( RMA ) for mRNA levels and intensity-based absolute quantification ( iBAQ ) for both translatome and protein abundance . The combined dataset of mRNA , translation and protein levels consisted of nearly 7 , 000 gene products that were detected in the transcriptome as well as the translatome and/or proteome ( S1 Table ) . First , to confirm that relative protein stability can be inferred from our data without directly measuring degradation rates , we used published protein half-life measurements from cycling HeLa cells [27] and generated a color-coded scatterplot , in which each protein is colored based on its previously-reported stability ( S2 Fig ) . Proteins with long half-lives were indeed found to cluster predominantly above the regression line , indicating steady-state accumulation , whereas proteins with short half-lives were found mostly below the line , indicating rapid degradation . 1D functional enrichment analysis [28] based on relative protein stability score , i . e . the ratio of steady-state abundance to translation level of each protein , found that relatively stable proteins were enriched for processes e . g . glycolysis , amino acid biosynthesis and oxidation-reduction , while labile proteins were enriched for transcription , membrane association , cell adhesion and regulation of cell cycle ( S2 Table ) . These findings are consistent with previous estimates generated using pSILAC [9] . Next , we examined whether typical cell cycle markers share a common pattern of mRNA , translation and protein expression . To that end , we selected genes known to peak sequentially from G1 to mitosis at the mRNA and/or protein level . These include Uracil-DNA glycosylase ( UNG ) , which peaks prior to the G1/S checkpoint [29]; Proliferating cell nuclear antigen ( PCNA ) and Cyclin A2 ( CCNA2 ) , which peaks in early and late S-phase , respectively [30 , 31]; Cyclin B1 ( CCNB1 ) , which peaks in G2/M [32]; and Aurora kinase A ( AURKA ) , which peaks during mitosis [33] ( Fig 1A ) . For each marker , we plotted the proportion of its mRNA , translation and protein measurements at G1 , S and G2/M , with peak level defined as 100% . Similar patterns were observed at the three levels of expression for all markers ( Fig 1B ) , except for UNG , whose mRNA and translation levels peaked during G1 while protein abundance peaked at S-phase . This difference in pattern may reflect a lag time between accumulation of mRNA and its encoded protein , which was observed for many gene products [13 , 21 , 34] . It may also be attributed to the rapid proteasomal degradation of UNG outside of S-phase , a mechanism thought to prevent accumulation of UNG protein prior to the initiation of DNA replication [35] . To further validate that the published microarrays reflect the same cell synchrony as the samples used for the translatome and proteome analysis , we generated mRNA measurements using real-time PCR ( qPCR ) and compared them to the microarray profiles . Analysis of the same cell cycle markers showed comparable expression patterns between the published microarray data and our qPCR results ( S3 Fig ) , confirming similar cell cycle periodicity despite the independent measurements . As multiple copies of protein are produced from a single mRNA molecule , we expected translation products to have a larger dynamic range as compared to mRNAs . Indeed , translation products spanned almost 5 orders of magnitude , compared to less than 4 for mRNA and 6 for steady-state protein levels ( Fig 2A ) , suggesting that regulation of both translation and protein degradation contributes to the diversity in protein abundance . Since the dynamic range of microarrays is lower than that of RNA-seq [36] , this comparison may underestimate the true dynamic range of the transcriptome . However , the translatome and steady-state proteome were both generated using similar MS conditions and the differences in their range are therefore presumed to be biologically relevant and not merely a technical artifact . Next , we calculated individual Spearman’s rank correlations for mRNA , translation and protein levels at each cell cycle phase . Hierarchical clustering of the correlation coefficients showed that each level of expression best correlated with itself across the three phases , confirming that the bulk of gene products remains relatively invariant along the cell cycle ( Fig 2B ) . mRNA levels and protein abundance show significant but limited positive correlation ( rs = 0 . 46–0 . 48 ) throughout the cell cycle , consistent with previously reported values for similar comparisons in mammalian cells [9 , 21 , 37 , 38] . The correlations between translation and protein levels were higher ( rs = 0 . 66–0 . 67 , Fig 2B ) throughout the cell cycle , reflecting the combined contribution of translation rates and mRNA levels . To verify that the higher correlation between the translatome and the proteome is not overestimated due to the technical similarities between measurement techniques , i . e . MS-based analysis and use of iBAQ to determine absolute protein abundance , we employed an alternative absolute quantification method based on the summed intensities of the top 3 peptides per protein ( TOP3 [39] ) . This was not found to have a significant impact on the correlations ( S4 Fig ) , suggesting that the high translatome-proteome correlation is not merely due to the use of a similar quantification method . Even if the true correlation is lower , this comparison indicates that post-translational processes e . g . protein degradation have a considerable contribution to the variance in steady-state protein levels . Furthermore , to control for the technical variability that is inherent to each measurement platform , we followed the procedure described by Csardi et al [40] to generate corrected Spearman’s correlations . With our data , because the intra-platform correlations were high ( rs = 0 . 95–0 . 99 ) , this correction did not have a significant effect ( S5 Fig ) . Overall , these results suggest that mRNA , translation and protein degradation all play a significant role in modulating steady-state protein abundances . To determine whether known cell cycle genes share distinct distribution patterns , we filtered our data for cell cycle functions based on Gene Ontology Biological Process ( GOBP ) . Overall , gene products with cell cycle annotations had a similar distribution and only marginally higher correlations as compared to the entire dataset , at all phases ( Fig 2C and 2D ) . While this may indicate that cell cycle gene products do not share common modes of regulation at the level of translation or protein degradation , it may also reflect confounding inter-platform effects resulting from the different measurement techniques . Our downstream analyses aimed to minimize these effects . To minimize potential biases related to platform-specific effects or differences in dynamic range between mRNA , translation and protein measurements , we compared the change in gene expression along the cell cycle within each platform . We calculated fold-change ( FC ) ratios of mRNA , translation and protein levels for each pair of consecutive cell cycle phases , reflecting the transition from G1 to S-phase , S-phase to mitosis , and mitosis to G1 ( labeled G1-to-SFC , S-to-G2/MFC and G2/M-to-G1FC , respectively ) . Similar to the range of absolute expression measurements , the range of fold-change ratios across the cell cycle was also higher for translation compared to mRNA and for protein compared to translation ( Fig 3A ) , suggesting that regulation of translation and protein stability contributes not only to the diversity of steady-state protein abundance but also to the magnitude of changes upon transition between phases . This analysis of fold-changes eliminates much of the inter-platform variance , but is still influenced by the dynamic range of the measurement technique , which was shown to be relatively low for microarrays . To ensure that mRNA-level regulation is not underestimated , we performed Z-score transformation of each sample , unifying the dynamic range of all fold changes . However , while this normalization aims to reduce artifacts associated with the experimental methodology , it assumes that the true biological range of mRNA and proteins is identical and therefore carries an inherent risk of overestimating the contribution of mRNA while underestimating the impact of translational regulation and protein degradation . In contrast to absolute measurements that best correlated within each level of expression , Z-transformed fold-change ratios reveal higher similarity within each cell cycle phase and between the different levels of expression . Highest correlations were observed between mRNA and translation fold-changes , particularly as cells progress in and out of mitosis ( Fig 3B , S-to-G2/MFC and G2/M-to-G1FC ) . While changes in mRNA levels were associated with concordant changes in their translation , such changes were not readily reflected in protein abundance , as evidenced by the surprisingly low correlations between translation and protein fold-changes ( Fig 3B ) . In this dynamic system of the cell cycle , it is possible that changes in steady-state protein abundance lag behind mRNA translation and therefore escape detection . Compared to the entire dataset , GOBP cell cycle genes showed considerably higher correlations in all pairwise comparisons , with maximum correlations of rs = 0 . 67 and rs = 0 . 66 for mRNA and translation fold-change ratios upon entry to and exit from mitosis , respectively . In other words , the expression of most genes with annotated cell cycle functions changes concordantly at both the mRNA and translation levels , primarily during mitosis , without notable differences in translation efficiency of individual mRNAs . These gene products seem to be highly specific to mitosis , as the same subset of genes is first up-regulated when cells progress into mitosis and then down-regulated upon transition to G1 ( S6 Fig ) . This group consists of many gene products with known mitotic functions e . g . cyclins , mitotic kinases , kinesins , spindle assembly and chromosome segregation proteins ( S3 Table ) . As cell cycle patterns were readily detected in the fold-change but not absolute expression datasets , we next sought to explore the interplay between changes in mRNA levels , translation and protein abundance for all gene products that fluctuate along the cell cycle , regardless of prior cell cycle annotation . For this purpose , we filtered our dataset for gene products that showed statistically significant changes ( one-sample T-test of Z-transformed fold-changes , FDR<0 . 05 ) in any of the cell-cycle phases , at the level of mRNA , translation or protein or any combination thereof . Filtration of the data resulted in a subset of n = 2 , 323 significantly changing proteins ( S4 Table ) . We then performed k-means clustering of gene products into ten clusters with distinct patterns of expression . The first five clusters showed the highest fold-changes ( Fig 4 , clusters A-E ) , while the other five showed more subtle differences ( S7 Fig , clusters F-J ) . These clusters reveal three main modes of interplay between the regulatory levels: concordant , lagging or one-level cycling of the transcriptome , translatome or proteome . For example , while clusters A and E both show peak protein expression during mitosis , this results from different modes of regulation . Cluster A is characterized by concordant increases in mRNA , translation and protein during mitosis ( 47 genes; Fig 4A and 4B ) ; it is enriched for annotations such as cell division , cytokinesis , spindle and microtubule-based movement ( Fig 4C ) , and consists of genes with known mitotic functions , including cyclins and kinases , mitotic checkpoint ( MCC ) proteins , kinesins and ubiquitin modifiers ( e . g . CCNB1 and CCNB2 , PLK1 , BUBR1 , CDC20 , KIF11 and UBE2C ) . On the other hand , cluster E ( 220 genes; Fig 4A and 4B ) consists of proteins that peak during mitosis and then drop back down upon transition to either G1 or S-phases , but these fluctuate almost entirely at the steady-state protein level , suggesting post-translational control of protein stability . This cluster is not enriched for classical mitotic processes , but rather for tRNA synthetases and metabolic functions e . g . carbohydrate and amino acid metabolism ( e . g . PGK1 , IDH1 , MDH1 , PHGDH , PFKM , TALDO1 ) . Interestingly , genes included in cluster C ( 106 genes; Fig 4A and 4B ) oscillate primarily at the level of translation , peaking upon transition to S-phase and declining upon entry to mitosis . Some of these genes also show mild transcript fluctuations , peaking at either G1 or S-phase . This cluster is enriched for S-phase related functions e . g . telomere maintenance , chromosome organization and DNA replication ( e . g . RFC4 , PARP2 , POLA1 , FANCI ) . In contrast , cluster D ( 231 genes; Fig 4A and 4B ) includes gene products such as the known S-phase marker Geminin ( GMNN ) , whose mRNA and translation levels increase at G1 while their steady-state protein abundance lags behind and peaks at S-phase . This cluster is enriched for mitochondria and endoplasmic reticulum related processes ( for the complete list of enriched terms , see S5 Table ) . To confirm that the cycling patterns observed here were not artificially introduced by the Z-score normalization , we performed a similar analysis using the non-normalized fold-change data . We filtered for gene products with a minimum 1 . 5 fold-change in at least 2 out of 3 measurements of mRNA , translation or protein , to emphasize the effect of fold-changes rather than statistical significance . Unsupervised hierarchical clustering of these filtered fold-change ratios ( n = 832 , S6 Table ) revealed two main clusters of peak S-phase or peak mitosis expression , with cycling patterns strikingly similar to those generated by the clustering of Z-scored data ( S8 Fig ) . However , one pattern was significantly more prevalent in this type of analysis; about 40% of gene products ( n = 339 ) showed increased protein accumulation during mitosis , compared to only 10% ( n = 220 ) in the Z-scored dataset ( cluster E ) . Considering that the dynamic range of the translatome and proteome is not biased due to inter-platform differences ( both are MS-based ) , this analysis further supports a robust role for regulation of post-translational protein stability along the cell cycle . Next , we took a closer look at the pattern of expression in cluster C . The gene products in this cluster show a considerable boost in translation levels during S-phase , but the increase in protein synthesis is not reflected by steady-state protein accumulation . Histones , a major gene group in this cluster , are known to be produced during DNA replication to coat the newly-synthesized chromatin . Histone expression is tightly regulated at multiple levels , including transcription , mRNA stability and translation , to ensure accurate duplication and prevent accumulation of excess unincorporated histones that can be toxic for the cells ( reviewed in [41] ) . While the mRNA levels of all core histones ( H2A , H2B , H3 and H4 ) drastically increased at G1 , their translation levels lagged behind and increased only at S-phase ( Fig 5A ) . This is not surprising , because translation of histone mRNAs requires the stem-loop binding protein ( SLBP ) , which is only present at S-phase ( our data and [42] ) . Nevertheless , while histone mRNA and translation levels fluctuate dramatically along the cell cycle , steady-state protein levels remain relatively static ( Fig 5A ) . We validated these findings by experimentally measuring mRNA and protein dynamics of histone H3 in synchronized cells . Although qPCR showed that H3 mRNA accumulates during S-phase and is subsequently degraded ( Fig 5B ) , immunoblot analysis showed that its protein levels remain stable as cells progress from S-phase to mitosis , as evidenced by the increase in H3 phosphorylation ( Fig 5C ) . While increased protein degradation can explain this pattern , we hypothesize that given the known roles of histones in S-phase , the increase in protein synthesis may be obscured by dilution effects related to cell growth . As cells progress from G1 to mitosis , the content of the cytoplasm is doubled in preparation for cell division , with growth rates increasing exponentially during S-phase [43] ( Fig 5D , gray line ) . Because MS analyses are routinely normalized to the overall amount of protein in each sample , this increase in protein mass per cell is not reflected in steady-state protein measurements . Therefore , accumulation of proteins synthesized predominantly during S-phase may be masked by the overall increase in protein mass ( Fig 5D , red line ) . Still , not all gene products involved in DNA replication share the same pattern . Others show a simultaneous increase in mRNA and translation levels during G1 , leading to detectable protein accumulation by S-phase ( Fig 5E ) . These include DNA replication and damage response factors e . g . licensing factor MCM2 , Claspin ( CLSPN ) , Denticleless ( DTL ) and PCNA , as well as FAM111B whose function is unknown but may be related to DNA replication , as predicted by its pattern of expression . Taken together , these observations suggest that the specific time a protein is synthesized may determine whether or not it would appear to oscillate at the steady-state proteome level . Furthermore , the different expression patterns of histones at the mRNA , translation and protein levels are a good example for the important information that can be gained by combining multi-omic measurements . In addition to histones , many other gene products showed an increase in protein abundance upon transition to S-phase . Among those , we identified a group of genes involved in mitochondrial translation . To determine if this is a general pattern for the mitochondrial translation machinery , we followed the oscillations of all genes encoding for mitochondrial ribosomal proteins and tRNA synthetases in our unfiltered Z-transformed dataset . Indeed , we detected an S-phase specific increase in protein abundance for these genes , which was associated with mild increases in translation at G1 ( S9A and S9B Fig ) , suggestive of delayed accumulation kinetics . In contrast , genes encoding for cytoplasmic ribosomal proteins and tRNA synthetases showed a different pattern; their relative abundance peaked during mitosis and decreased during S-phase ( S9C and S9D Fig ) . Combining datasets that measure different levels of gene expression under similar experimental conditions can lend further support to the discovery of novel gene products with potential cell cycle functions . For example , a similar pattern of change at two or more levels of expression can allow higher confidence in assigning cell cycle periodicity to specific gene products , even if the change in expression is mild . Furthermore , gene products that share a common pattern of expression with known cell cycle regulators may function in the same pathways . To probe our dataset for functional interactions , we used the STRING database and generated 2 separate protein association maps for gene products from clusters C and E ( Fig 4 ) , which are dominated by increased S-phase translation and M-phase protein accumulation , respectively . While the vast majority of gene products in cluster C have an annotated role in DNA replication or damage response ( Fig 6A and S10 Fig ) , gene products in cluster E are involved in a variety of processes e . g . translation and ribosome biogenesis , carbohydrate and amino acid metabolism , transport and migration ( Fig 6B and S10 Fig ) . These include gene products not previously shown to have periodic expression or cell cycle specific functions . For example , translation of the ubiquitin E3 ligase NEDD4L was found to peak during S-phase ( Fig 6C , green ) while motor protein KIF21A accumulates at the protein level during mitosis ( Fig 6D , yellow ) . To experimentally validate the potential of our approach to identify novel cycling gene products , we performed immunoblot and qPCR analyses of several candidates found to be predominantly regulated at the steady-state protein level during mitosis . For this purpose , we synchronized HeLa cells by double thymidine block and harvested the synchronized populations at 2 , 4 , 6 , 8 , 10 and 12 hours after release from the second block . Antibodies and oligonucleotides specific for Pyruvate kinase ( PKM ) , D-3-phosphoglycerate dehydrogenase ( PHGDH ) , Isocitrate dehydrogenase ( IDH1 ) and DNA topoisomerase 2-beta ( TOP2B ) were used to measure protein and mRNA , in parallel to the cell cycle markers CCNA2 , CCNB1 and AURKA . This analysis showed that , as revealed by the proteomic data , protein amounts of these genes peak during G2/M ( S11A Fig ) while their mRNA levels remain constant throughout the cell cycle ( S11B Fig ) . This oscillatory pattern may suggest a previously unknown role for these proteins in cell cycle progression . These results suggest a novel link between cell cycle progression and metabolism that may be necessary to provide the energy and the necessary building blocks for cell growth .
In this work , we present the first integrative analysis of the intricate relationship between mRNA , translation and protein levels along the mammalian cell cycle . Consistent with previous reports [9 , 10] , our data show that both mRNA and translation levels explain a large proportion of the variation in protein abundance ( R2 = 0 . 23 and 0 . 45 , respectively ) , supporting a substantial role for translation regulation in modulating steady-state protein amounts throughout the cell cycle . This is in contrast with a recent work that reported a much higher contribution of mRNA levels to both absolute protein abundance as well as changes in protein levels upon LPS stimulation of dendritic cells [8] . However , even if our analyses underestimate the contribution of mRNA levels due to methodological errors or noise , as previously suggested [44] , the imperfect correlations between translation and protein levels indicate that post-translational regulation of protein abundance contributes significantly to the diversity of the proteome . To minimize potential biases due to inter-platform variance , we analyzed the fold-change differences between each pair of consecutive cell cycle phases . While this eliminates much of the variance , differences in dynamic range between platforms may still affect the outcome of such analyses . The extent of the true dynamic range of mRNA and protein in biological systems is still largely controversial; for example , RNA-seq exhibits a wider dynamic range as compared to microarrays , but this depends on the analytical depth or number of reads [36 , 45] . Similarly , the dynamic range of the proteome depends on technical parameters e . g . measurement time and analytical depth . Direct comparisons of RNA-seq and deep proteomic analysis have shown that for the same genes , the spread of the proteome is wider than that of the corresponding mRNA [38] . However , true ranges can only be determined by absolute quantification of proteins and mRNAs . In the current work , to avoid underestimation of transcriptional control due to the lower dynamic range of microarrays , we used Z-score transformation . While this approach is a commonly-accepted standard in gene expression studies , it may also lead to overcorrection and thus underestimation of the impact of translation or protein degradation . To address this possibility , we confirmed our main conclusions using the raw untransformed dataset . Analysis of Z-transformed differences revealed that translation levels tend to change concordantly with mRNA levels , particularly as cells progress through mitosis , indicating that the mitotic gene expression program is largely dominated by changes at the mRNA level . This conclusion is supported by a previous study showing that translational control plays a less pervasive role during mitosis as compared to G1- and S-phases [18] . However , this normalization may have led to underestimation of the effect of inhibition of translation elongation during mitosis , which we have previously shown [24 , 26] . Even after normalization , not all cycling gene products are regulated at the mRNA level; expression of specific subsets of genes appears to fluctuate entirely at the level of translation and/or protein degradation . Furthermore , in both the Z-transformed and untransformed datasets , we observed that a large proportion of changes in mRNA or translation levels are not reflected in the steady-state proteome . This may be explained by delayed protein accumulation , which is further confounded by dilution effects related to cell growth , as exemplified by histone expression dynamics ( Fig 5 ) . Another possible explanation for this disagreement is rapid degradation of newly-synthesized proteins . Due to the rapid life cycle of mRNA and the experimental design of PUNCH-P , both the transcriptome and the translatome represent a snapshot of gene expression , while proteome measurements reflect the accumulation of proteins with time . Therefore , stable proteins may be seen to accumulate without increased synthesis , while labile proteins may be highly synthesized but still escape detection at the steady-state proteome level . For example , the p53 tumor suppressor gene , which is known to be transcribed , translated and rapidly degraded in HeLa cells [46 , 47] , was indeed detected only at the mRNA and translation levels . Additionally , other gene products encoding for labile proteins e . g . Hypoxia-inducible factor 1 ( HIF1A ) [48] , Cyclin E2 ( CCNE2 ) [49] and Securin ( PTTG1 ) [50] completely escape detection in the steady-state proteome , but show periodic expression at the level of mRNA and/or translation . Finally , we cannot rule out the possibility that patterns not observed in the proteome may emerge by sampling a larger number of time points along the cell cycle . Clustering of significantly-changing gene products reveals distinct patterns of expression , as outlined in Fig 7 . Concordant increases in mRNA , translation and protein are characteristic of genes with mitotic functions ( cluster A ) , while accumulation of protein without underlying changes in mRNA or translation is characteristic of major biosynthetic pathways e . g . carbohydrate and amino acid metabolism ( cluster E ) . S-phase , on the other hand , is dominated by two main patterns of gene expression; delayed accumulation of proteins that are synthesized throughout G1 , e . g . mitochondrial genes ( cluster D ) , and increased translation of proteins involved in DNA replication ( cluster C ) , which is buffered by the increase in cell size and therefore remains undetectable at the steady-state protein level . Another unusual buffering effect is observed for genes with increased mRNA levels during mitosis ( cluster B ) , which is associated with a minor effect on translation and no apparent effect on protein levels . This cluster consists of e . g . lamins and cell adhesion genes and may either represent non-functional cycling of mRNA or indicate that these changes in expression can be observed at the translation and protein levels only by increasing the temporal resolution of sampling . While most genes with known mitotic functions are regulated at the mRNA level , with consequent effects on translation levels and protein abundance , there are hundreds of other gene products that peak during mitosis but are mostly controlled by differential protein stability . This pattern is characteristic of major metabolic pathways e . g . carbohydrate and amino acid biosynthesis ( Fig 6B ) , whose protein products are reduced at S-phase or elevated upon entry to mitosis without detectable changes in mRNA or translation levels . A similar pattern was also detected for the cytoplasmic translation machinery , including ribosomal proteins and tRNA synthetases ( S9 Fig ) . It is known that ribosomal proteins are synthesized in excess to ensure that efficient ribogenesis is never limited by the available supply of protein components [51] . This is balanced by simultaneous proteasomal degradation to prevent accumulation of free ribosomal proteins that causes ribosomal stress and cell cycle arrest [52 , 53] . Therefore , reduced stability at S-phase may represent increased degradation of unincorporated ribosomal proteins to prevent ribosomal stress and allow unhindered progression through the G1/S checkpoint . Because degradation of ribosomal proteins occurs predominantly in the nucleus [51] , this process may be inhibited after nuclear envelope breakdown , resulting in re-stabilization of ribosomal proteins during mitosis as suggested by our data . Furthermore , our findings are supported by another study that showed increased protein expression of tRNA synthetases during mitosis in yeast [54] . Interestingly however , this pattern of expression is not shared by the mitochondrial translation machinery . Synthesis of mitochondrial ribosomal proteins and tRNA synthetases peaks at G1 , followed by steady-state accumulation of protein at S-phase . This increase in mitochondrial components , which presumably reflects proliferation of mitochondria prior to doubling of the cytoplasmic proteome , is consistent with the high energetic demands associated with DNA replication and cell division , as well as the need to guarantee redistribution of sufficient number of mitochondria between the daughter cells . Remarkably , ribosomal and mitochondrial proteins were recently found to be the two main groups regulated at the level of protein synthesis and degradation in LPS-stimulated dendritic cells [8] . As described above , we observed surprising oscillations in expression of cytoplasmic translation machinery components , including periodic expression of mRNA binding proteins and molecular chaperones . It is tempting to speculate that such oscillations represent adaptations of the protein synthesis machinery for optimal translation of specific mRNAs . In agreement with this concept , it has been shown that cell cycle-regulated genes in both yeast and human cells have different codon preferences , with a bias towards non-optimal codons [54 , 55] . Here we found a subset of gene products whose translation levels increased during S-phase with little to no change in mRNA levels ( Cluster C ) . These include DNA replication and damage response factors ( FANCI , XRCC1 , PARP2 , DNA polymerases , RFC proteins ) as well as kinases and transcription modulators ( CDK2 , NFKB2 , KLF5 ) . Interestingly , synthesis of DNA damage response proteins in yeast was shown to involve codon adaptation and tRNA modifications [56] . It is possible that similar mechanisms are at work in mammals , conferring S-phase specific translation regulation as reflected in our data . Similarity in expression patterns often reflect shared functions . Therefore , combining methods that represent distinct steps of gene expression may lend greater power to detecting unique patterns and thus assigning related functions . This is true for gene products that show either concordant or discordant changes at the different levels of expression but share similar patterns with genes of known function . One specific example is FAM111B , an under-characterized gene associated with a human skin and lung disorder [57] , whose expression pattern is remarkably similar to that of genes with functions in DNA replication ( Fig 5E ) . Based on this expression signature , we propose that FAM111B should be studied for a possible role in DNA replication or another S-phase specific function . Interestingly , its paralog FAM111A was recently identified to be involved in DNA replication [58] , but shows a distinct cycling pattern in our data ( Cluster C , S4 Table ) . In summary , our work provides information on gene products and pathways that were not previously shown to have periodic expression , and it is tempting to speculate that these play yet unknown roles in progression or regulation of the mammalian cell cycle . Taken together with the observation that some patterns are observed only at the level of mRNA , translation or protein , this highlights the importance and utility of combining expression data from different platforms and measurement techniques , to generate unique insights into the complex nature of gene expression regulation .
HeLa S3 cells were grown in DMEM ( Invitrogen ) supplemented with 10% fetal calf serum , 2 mM L-glutamine , and 100 U/mL penicillin/streptomycin ( Biological Industries ) . For double-thymidine block , cells were treated with 2 mM thymidine for 19 h , released from G1/S block in fresh DMEM for 9 h , treated again with 2 mM thymidine for 18 h , released in fresh DMEM , and harvested at different time points , as indicated . Whole cell extracts were separated by 8% SDS-PAGE , transferred to polyvinylidene difluoride ( PVDF ) membrane and probed with anti–AURKA ( C4718T , 1:1000 , Cell Signaling Technology ) ; anti-CCNA2 ( H432 , 1:1000 , Santa Cruz Biotechnology ) ; anti-CCNB1 ( ab72 , 1:1500 , Abcam ) ; anti-IDH1 ( 8137S , 1:1000 , Cell Signaling Technology ) ; anti-PHGDH ( 13428S , 1:1000 , Cell Signaling Technology ) ; anti-PKM ( 3190S , 1:1000 , Cell Signaling Technology ) ; anti-TOP2B ( HPA024120 , 1:250 , Prestige antibodies , Sigma ) ; anti-TUB ( T6074 , 1:400000 , Sigma ) at 4°C , overnight . 12 . 5% gels were used similarly and probed with anti-Total H3 ( ab1791 , 1:5000 , Abcam ) ; anti-phosphoH3 ( ab5176 , 1:1000 , Abcam ) . Secondary antibodies , donkey-anti-mouse ( 715-035-151 , 1:10000 , Jackson ImmunoResearch Laboratories ) or anti-rabbit ( 711-035-152 , 1:10000 , Jackson ImmunoResearch Laboratories ) were conjugated to horseradish peroxidase antibodies and incubated for 1 h at room temperature . Blots were visualized with the Pierce ECL Western blotting substrate ( 34087 , Thermo Scientific ) according to manufacturer’s instructions . Total RNA was isolated from cell pellets with TRIzol ( 15566026 , Life Technologies ) and 1 μg of RNA was subjected to reverse transcriptase-PCR with random hexamers using the Verso cDNA synthesis kit ( AB–1453/A , Thermo scientific ) . Real time quantitative-PCR ( RTqPCR ) was performed using the PerfeCTa SYBR Green FastMix , ROX ( 95073–12 , Quanta Biosciences ) . The following primers synthesized from Integrated DNA Technologies were used and expression levels were normalized to GAPDH expression . GAPDH Fwd ( 5’-GCACCGTCAAGGCTGAGAAC–3’ ) , GAPDH Rev ( 5’-ATGGTGGTGAAGACGCCAGT–3’ ) ; HIST1H3 Fwd ( 5’-AGGACTTTAAGACCGACCT–3’ ) , HIST1H3 Rev ( 5’-ATGATGGTGACCCGTTTG–3’ ) ; AURKA Fwd ( 5’-AGGACCTGTTAAGGCTACA–3’ ) , AURKA Rev ( 5’-GAGCCTGGCCACTATTTAC–3’ ) ; CCNA2 Fwd ( 5’-TAGATGCTGACCCATACCTC–3’ ) , CCNA2 Rev ( 5’-GATTCAGGCCAGCTTTGTC–3’ ) ; CCNB1 Fwd ( 5’-GCACCAAATCAGACAGATGG–3’ ) , CCNB1 Rev ( 5’-CGACATCAACCTCTCCAATC–3’ ) ; UNG Fwd ( 5’-TCTCCCTTGCCTTTATGGTG–3’ ) , UNG Rev ( 5’-CACCCCAACATCTGTCACTG–3’ ) ; PCNA Fwd ( 5’-GTGAACCTCACCAGTATGTC–3’ ) , PCNA Rev ( 5’-CCAAGGTATCCGCGTTATC–3’ ) ; PKM Fwd ( 5’-CATTCATCCGCAAGGCATC–3’ ) , PKM Rev ( 5’-TCATCAAACCTCCGAACCC–3’ ) ; PHGDH Fwd ( 5’-TTGCTGTTCAGTTCGTGGAC–3’ ) , PHGDH Rev ( 5’-GAGCTTCTGCCAGACCAATC–3’ ) ; IDH1 Fwd ( 5’-ACAGGAGACGTCCACCAATC–3’ ) , IDH1 Rev ( 5’-TTGCAAAGAAGGCAAGCTCT–3’ ) ; TOP2B Fwd ( 5’-GGTACTGGATGGGCTTGTA–3’ ) , TOP2B Rev ( 5’-GTTTGGAAGCATGGGATGAG–3’ ) . Cell pellets were lysed in Urea buffer containing 6 M urea/2 M thiourea in 100 mM Tris-HCl ( pH 8 . 5 ) at room temperature . Protein concentrations were determined using the Bradford Protein assay ( Bio-Rad ) . Equal amounts of protein ( 10 μg ) from each sample lysate were reduced with 1 mM dithiothreitol ( DTT ) and alkylated with 5 mM Iodoacetamide ( IAA ) . Protein digestion was performed for three hours with endoprotease LysC ( Wako chemicals; 1:100 enzyme to protein ratio ) followed by an overnight digestion with sequencing grade modified Trypsin ( Promega; 1:50 enzyme to protein ratio ) at room temperature . Peptides were acidified with TFA and purified on C18 stageTips [59] . Eluted peptides were separated using EASY-nLC–1000 HPLC system ( Thermo Scientific ) coupled to the Q Exactive Plus MS ( Thermo Scientific ) through an EASY-spray ionization source . Peptides were separated on a 50 cm long PepMap EASY-spray column ( Thermo Scientific ) using four-hour gradients of water:acetonitrile ( with 0 . 1% formic acid ) . MS analysis was performed in a data dependent mode using a top 10 method for MS/MS acquisition . Analysis was performed with the Maxquant Software [60] ( version 1 . 5 . 2 . 10 ) and MS/MS spectra were searched against the UniprotKB database ( Nov2014 ) with the Andromeda search engine [61] . The label free quantification algorithm was used for data normalization [62] with minimum ratio count set to 2 . FDR was set to 1% at both the peptide-spectrum match and protein levels . FDR was determined by the target-decoy approach . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium [63] via the PRIDE partner repository with the dataset identifier PXD002802 . Microarray dataset GSE26922 ( Affymetrix Human Gene 1 . 0 ST Array ) was downloaded from Gene Expression Omnibus ( GEO ) , with RMA-normalized expression values for timepoints 2 , 8 and 12h . Translation and protein measurements ( log2 ) were from previous work ( timepoints 2 , 8 . 5 and 14h ) [2] and this study ( timepoints 2 , 8 . 5 and 14h ) , respectively . To align the three datasets , an ID conversion list was generated using BioMart with the following attributes: ( a ) Affymetrix Microarray HuGene 1 . 0 st v1 probeset ID ( s ) and ( b ) UniProt/SwissProt Accession , based on Homo sapiens genes ( GRCh37 . p13 ) . The three datasets were combined using Perseus into a protein-centric matrix , with each row representing a protein group . This was done as follows: first , translation levels and protein abundance measurements were combined based on Uniprot IDs to generate a union of all proteins detected in at least one of the two dataset; then mRNAs levels were added using the maximum value option to select only the most abundant transcripts coding for indistinguishable protein groups . For comparison of absolute abundance , the iBAQ algorithm [9] was used to normalize translation and protein levels . Alternative absolute quantification based on the Top three proteins was calculated from the MaxQuant peptide table , using R . For fold-change comparisons , translation and protein levels were normalized using the LFQ algorithm with a minimum of 2 ratio counts , to achieve higher quantitative accuracy . Means were calculated from a minimum of 2 replicates . Fold-change values for mRNA , translation and protein were calculated by subtracting the mean logarithmized value from the preceding cell cycle phase . GOBP cell cycle filtering consisted of the following categories: Cell cycle , Cell cycle arrest , Cell cycle checkpoint , Cell cycle cytokinesis , Cell cycle phase , Cell cycle process and Cell division . For histone gene expression , due to multiplicity of genes coding for the same protein products , only transcripts showing highest fold-change ratios were selected for further analysis . Statistical analysis was done with Perseus using one-sample Student’s t-test ( FDR<0 . 05 ) , to extract the z-transformed values that significantly differ from zero in each sample . Data are presented as means ± SD , where indicated . Spearman’s correlations were determined separately for each replicate and averaged . Corrected Spearman’s correlations were calculated according to Csardi et al . [40] . Generic k-means clustering ( Fig 4 , S7 Fig ) was performed on Z-transformed logarithmized fold-change ratios . Hierarchical clustering for S8 Fig was performed on untransformed logarithmized fold-change ratios using correlation distances and with column tree order preserved . Enrichment analysis was performed using Fisher’s exact test with an FDR value of 0 . 02 . Protein networks were constructed in the STRING database with interaction confidence > 0 . 5 and visualized using Cytoscape . | How the genetic program of a cell unfolds to execute complex functions depends on a dynamic interplay between multiple steps that include transcription of DNA into mRNA , translation of mRNA into protein and post-translational degradation of mature proteins . Profiling of gene expression is traditionally based on measurements of steady-state mRNA levels , but recent studies have shown that mRNA and protein levels are highly discordant , suggesting that post-transcriptional mechanisms play a dominant role in modulating protein abundance . Here we combine measurements of mRNA , translation and protein across the mammalian cell cycle to uncover the hidden complexity of cell cycle regulation . Using this approach , we gain insights into the dynamics of protein synthesis and degradation and identify new genes and functions that cycle through cell division by periodic changes in translation or degradation rates . Integrative multi-omic analyses combining information on the transcriptome , translatome and proteome hold great promise for providing transformative biological insights in a variety of model systems . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | Uncovering Hidden Layers of Cell Cycle Regulation through Integrative Multi-omic Analysis |
Infectious diseases are a leading threat to public health . Accurate and timely monitoring of disease risk and progress can reduce their impact . Mentioning a disease in social networks is correlated with physician visits by patients , and can be used to estimate disease activity . Dengue is the fastest growing mosquito-borne viral disease , with an estimated annual incidence of 390 million infections , of which 96 million manifest clinically . Dengue burden is likely to increase in the future owing to trends toward increased urbanization , scarce water supplies and , possibly , environmental change . The epidemiological dynamic of Dengue is complex and difficult to predict , partly due to costly and slow surveillance systems . In this study , we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics , both at country and city level at a weekly basis . Here , we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast , in comparison with other available web-based data , Google Trends and Wikipedia access logs . Also , we studied the factors that might influence the goodness-of-fit of the model . We built a simple model based on tweets that was able to ‘nowcast’ , i . e . estimate disease numbers in the same week , but also ‘forecast’ disease in future weeks . At the country level , tweets are strongly associated with Dengue cases , and can estimate present and future Dengue cases until 8 weeks in advance . At city level , tweets are also useful for estimating Dengue activity . Our model can be applied successfully to small and less developed cities , suggesting a robust construction , even though it may be influenced by the incidence of the disease , the activity of Twitter locally , and social factors , including human development index and internet access . Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost . Tweets are able to successfully nowcast , i . e . estimate Dengue in the present week , but also forecast , i . e . predict Dengue at until 8 weeks in the future , both at country and city level with high estimation capacity .
Infectious diseases are a leading threat to public health , economic stability , and other key social structures [1] . Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and incidence of the disease [2 , 3 , 4] . Early detection of disease activity and rapid responses can reduce the impact of diseases [5] . The interdisciplinary field of computational social science aims to quantify real-world social phenomena using large datasets known as ‘big data’ , based on data from social networks , such as Twitter and Facebook , to describe behavioral patterns in novel contexts [6 , 7] . The relative frequency of mentioning a disease in certain social networks , as in Twitter and others , is highly correlated with patients visits to doctors , making it possible to accurately estimate disease activity in each region of a country , with a small reporting lag [3 , 5] . Also , web search query data from Google and Wikipedia are capable of tracking disease activity and are available in near real-time [2 , 8] . Twitter is a unique social media channel , since users inform and discuss , through their short 140-character messages or ‘tweets’ , about the most diverse topics , including health conditions [3 , 9 , 10] . This free social networking service has more than 190 million users registered worldwide and processes about 55 million tweets per day , with the possibility of sentiment analysis and location selection [3 , 9 , 10 , 11] . According to the US Bureau ( 2014 ) , 45% of the Brazilian households have access to internet and 48% of the population are users of social media products , with an average of 3 hours per day spent in this activity . In Brazil , the main social media of preference is Facebook ( 94% ) , followed by Google Plus ( 75% ) , Twitter ( 56% ) and LinkedIn ( 54% ) [12] . Important to notice that Twitter usage ( 86% ) , as of other social media products , is mostly via mobile phone , which in Brazil has 134% of coverage , meaning an average of 1 . 34 cell phone subscriptions per person [12] . Dengue is an important public health burden , likely to increase in the future due to increased urbanization , scarce water supplies and environmental change [13] . Dengue is ubiquitous throughout the tropics and a fast spreading viral mosquito-borne disease [14] , with an incidence increase of 30 times during the last 50 years [15] . The World Health Organization ( WHO ) estimates 50 to 100 million new infections per year in 100 different countries , with half the world’s population , or 3 . 5 billion people at risk [15] , but more recent studies estimate the total incidence to be 390 million Dengue infections per year , of which 96 million manifest clinically [14] . Currently , there is no specific antiviral treatment to reduce severe illness or an effective vaccine to induce strong protection from infection [16] . The epidemiology of Dengue in Brazil is characterized by increasing geographical spread , as well as the total incidence of reported cases [17] . The epidemiological dynamics of Dengue disease is complex , and difficult to predict , partly due to the weaknesses of passive surveillance systems [13 , 17] . The majority of infections are clinically non-specific , consequently , Dengue disease is often underdiagnosed [13] , but these patients are also infectious to mosquitoes and contribute for the transmission of the disease [18] . Bureaucracy and lack of resources have interfered with timely detection and reporting of Dengue cases in many endemic countries , including Brazil , where reporting delay is estimated to be of 3 to 4 weeks [3 , 19] . Traditional , laboratory and clinically based diagnostic techniques are accurate but costly and slow . Alternative approaches to surveillance aim to capture health-seeking behavior at earlier stages of disease progression , specially capturing those with mild clinic manifestation population who do not seek medical care formally [2 , 6] . Some studies indicate that digital media reports reflect national epidemiological trends , acting as proxy for surveillance to provide early warning and situation awareness of emerging infectious diseases and Dengue [4 , 20] . While traditional Dengue surveillance data suffer from substantial delay , web-based data can fill in the gap providing a near real-time source of information [8 , 21 , 22] . Previous studies have shown that Twitter is a real-time source of information on Dengue symptoms activity in a population , and shows strong correlation with the number of notified cases [3 , 23] . Some advocate that Twitter-based surveillance efforts may provide an important and cost-effective supplement to traditional disease-surveillance systems [10 , 11] . Besides Twitter , web search query data , such as Google Trends , were also found to be capable of tracking Dengue activity . Proper combination of these two sources of information may provide timely information to public health officials and contribute to real-time predictive models [8 , 23] In this study , we aimed at investigating if tweets with personal indication of Dengue content could be integrated with clinical Dengue data to produce an accurate model for the early detection and monitoring of Dengue epidemics at country and city level . For comparison , we also report other available web-based data , Google Trends and Wikipedia . Also , we studied the factors that might influence the goodness-of-fit of the proposed model . We concluded that a simple model using tweets is able to successfully nowcast , i . e . estimate Dengue in the present week , and forecast , i . e . predict Dengue until 8 weeks in the future , both at country and city level with good estimation capacity . Our model can be applied successfully to smaller and less developed cities , even though it may be influenced by the incidence of the disease , the activity of Twitter locally , and social factors , including human development index and internet access .
We compared the time series of web-based data indicating Dengue activity with real observed Dengue cases in Brazil country level ( Fig 1 ) , between September , 2012 and October , 2016 . Dengue cases occurred continuously with a high weekly variation , with a minimum of 694 cases per week . The highest incidence of Dengue was observed in the months of March and April of each year , reaching 106 , 558 cases per week ( Fig 1 ) . Tweets , Google Trends ( GT ) and Wikipedia access logs showed strong and positive association with the observed Dengue cases ( Fig 1A ) . Tweets showed high variation , with an average of 1 , 213 tweets per week , ranging from 125 to 6 , 984 ( Fig 1B ) . Tweets presented a high positive association with Dengue cases ( r = 0 . 87 , p<0 . 001 ) , especially in 2013 and 2014 ( Fig 1A and 1B ) . In the last trimester of 2015 ( October to December ) , there was increased tweet activity not associated with Dengue . The relative GT index was 17 . 51 on average , varying from 4 to 100 ( Fig 1C ) . GT showed the stronger linear association with Dengue cases ( r = 0 . 92 , p<0 . 001 ) , compared to tweets ( Fig 1A ) . Wikipedia logs presented the smallest linear association ( r = 0 . 71 , p<0 , 01 ) with Dengue cases , but could also be considered high ( Fig 1A ) . The values started at 517 , and achieved 35 , 250 logs per week , with mean value of 6 , 481 ( Fig 1D ) . Unfortunately , Wikipedia data was only available until December , 2015 . There is important Wikipedia activity during non-epidemic periods not associated with real Dengue cases . Tweets with Dengue content were used to estimate weekly Dengue cases occurrence at country level , in Brazil ( Fig 2 , Table 2 ) . Our selected model ( Table 2 ) has tweets as covariate , as well as a temporal structure to account for the seasonality and annual cyclic characteristics of this disease . We can observe that the model with tweets plus a temporal structure presented a better Dengue estimation capacity than a model with either variables alone ( Table 2 , S2 Fig ) . We compared the selected tweets model with models including also “Dengue cases” as covariate . Three weeks is the usual time period for data from Dengue cases to become available [19] , therefore we decided to include Dengue with three weeks lag ( t-3 ) as explanatory variable for Dengue from week t . The latter model presented the best fit to observed data , with high explained deviance , low AIC , and reduced mean relative error ( Table 2 , S2 Fig ) . Otherwise , here the model with tweets and temporal structure was selected for further analyses , because it has the estimate capacity very similar to the model with Dengue as covariate , but is easy to apply and is also useful at city level ( Table 2 ) . Our selected model indicates that tweets are a positive predictor for Dengue cases ( Fig 2A ) , with an almost linear effect until 2 , 000 tweets , that stabilizes above this value . As expected , the relationship between Dengue and tweets is influenced by the week of the year ( Fig 2B ) , since disease transmission is highly seasonal ( Fig 1 ) . Estimated Dengue cases showed a good fit to the observed data ( Fig 2C ) , presenting a mean relative error of 0 . 345 , and 93 . 7% of deviance explained by the model ( Table 2 ) . In order to validate the estimation capacity of our model , we applied out-of-sample analysis with tweets data not previously used by our model for adjustment . Our model could successfully estimate Dengue cases in this scenario , with the capacity for explaining the deviance of Dengue of 93 , 2% ( Fig 2C ) . Dengue forecasting , i . e . the prediction of the number of Dengue cases occurring in future weeks ( up to 8 weeks ) , was also investigated ( Fig 3 , Table 3 ) . The quality of the forecast varies with the week of prediction , as we can observe by the deviance explained index and the mean relative error of the prediction in relation to observed cases ( Table 3 ) . We also showed that tweets are performing better in estimating Dengue cases in the present week , “nowcast” , since people may tweet about the disease during its occurrence . Forecasting was possible with an increasing error with the increase in forecast weeks , but good approximation to real disease occurrence , as indicated by fitted and observed lines in the time series for four different epidemic years ( Fig 3 , Table 3 ) . Tweets were obtained from 283 different cities distributed all over Brazil , including all 5 regions and 26 states ( Fig 4 , S1 Table ) . We observe that cities with higher Twitter activity are mostly clustered at the southeastern region of the country ( Fig 4A ) . These cities overlap with the region with the highest incidence of Dengue cases ( Fig 4B ) . We also analyzed the contribution of tweets to estimate Dengue at city level . For the majority of cities , we observed a high positive linear association between Dengue cases and tweets , with 67% of them with association above 50% ( S1 Table ) . Our tweets model ( Table 2 ) could successfully fit and estimate Dengue cases in 199 cities of a total of 283 , since some cities had too few data for model estimate convergence . Model goodness-of-fit was high for most cities , with Dengue deviance explained above 60% in 88% of cities analyzed ( Table 4 , S1 Table ) . Cities with high Dengue estimation quality of our model are distributed around the country but mostly concentrated at the southeastern region ( Fig 5 ) . We selected cities in different regions of the country to further investigate and validate the model application for Dengue estimation at city level ( Fig 6 , S1 Table ) . We selected the following cities: Belo Horizonte ( Fig 6A ) , Fortaleza ( Fig 6B ) , Manaus ( Fig 6C ) , Porto Alegre ( Fig 6D ) , Rio de Janeiro ( Fig 6E ) , and São Paulo ( Fig 6F ) . In all cities , tweets successfully estimated Dengue cases , as shown by the approximation of observed Dengue cases and its predicted values by the model , and by the high values of deviance explained ( ranging from 76 . 1% to 90 . 3% ) ( Fig 6 , S1 Table ) . The model was also able to fit Dengue cases in cities with lower linear correlation indexes , as Fortaleza and Sao Paulo ( Fig 6B and 6F , S1 Table ) . Important to notice that here we evaluated the Dengue estimation capacity of the same tweets model applied to country level , but each city would have improved results with models considering specific characteristics of each individual city . Tweets with Dengue content and their association with Dengue cases may be influenced by different factors . We divided the 283 cities into two groups , according to the quality of their Dengue estimation by the model: high quality group included cities ( 161 ) with model explained deviance equal or higher than 60% , and low quality group cities ( 122 ) with model explained deviance smaller than 60% , or zero ( model did not converge ) . Cities with high quality of Dengue estimation based on the tweets model have a higher population , more Dengue cases and tweets activity ( Table 5 ) . They also had higher human development indices: mean ( IDHM ) , education ( IDHME ) and income ( IDHMI ) . Only longevity index ( IDML ) was not different between groups ( Table 5 ) . As expected , cities with good fit by the model were those with high coverage of houses with access to a personal computer and internet ( Table 5 ) . Otherwise , considering a linear regression association between the variables analyzed here ( Table 5 ) and the Dengue estimate explained deviance by the model , we can observe that Dengue estimation capacity of tweets is strongly associated with Dengue incidence , but are not or weakly associated with population and development indexes ( Fig 7 ) .
In this study , we analyzed the potential of Twitter data for estimating and forecasting Dengue cases . Here we show that tweets are strongly associated with Dengue cases , and contribute not only for estimating , but also for forecasting Dengue activity up to 8 weeks in the future . Tweets , Google Trends and Wikipedia access logs with Dengue content show a strong and positive association with officially registered Dengue cases in Brazil . However , during the last trimester of 2015 , there was an important increase of tweets activity that was not associated with Dengue cases . This increase may be associated with an increase in Dengue tweeting activity that may have been caused by media news and the onset of the Zika epidemic in the country . The Zika virus is transmitted by the same vector as Dengue , the mosquito Aedes aegypti , and the disease's first symptoms are very similar , but serious complications include Guillain-Barré syndrome , and congenital infections can occur which may lead to microcephaly and maculopathy [33] . Zika , which was widely spread in the Pacific islands , was introduced in Brazil in 2014–2015 and caused a widespread epidemic in Latin America [33 , 34] . Google Trends , similar to Twitter , increased at the last trimester of 2015 , indicating probably higher public concern with both diseases . Twitter is a real-time source of information on Dengue symptoms activity in a population , and was shown to have strong correlation with the number of notified cases [21] , however , that association may be stronger during the increasing and decreasing phases , than during the disease peaks . Twitter , as a social network , may indicate the need for the Dengue patient to notify the disease to colleagues , therefore being a good estimator of disease occurrence . Otherwise , the other web-based data available for Dengue and evaluated here , GT and Wikipedia , are based on search queries , which would indicate a potential interest or curiosity over the disease , being more subjected to marketing campaigns and confusion with other diseases . Models built on the fraction of Google search volume for Dengue-related queries were previously shown to adequately estimate true Dengue activity in different seasons [8 , 22] . Here we confirm the high association between Google Trends and Dengue disease , also useful for disease surveillance and prevention . Wikipedia data suffer from a variety of instabilities that need to be understood and compensated for [2] . Language as a location proxy can only be used in some cases , since it is impossible to be used at finer scale , or even to indicate exactly the country , an important limitation . Overall , our feeling is that all three sources of data are probably useful to estimate Dengue at country-wide level . However , amongst these three web-based data , tweets with personal experience provide a strong association with real disease with potential to be an important explanatory variable for Dengue estimation models both in country and city level . The epidemiology of Dengue fever is highly seasonal , with multi-annual fluctuations , caused by the irregular circulation of its four serotypes , and the interplay between environmental drivers [35 , 36] . We built a simple model based on tweets together with a temporal structure that could successfully be used to estimate Dengue activity at country level , with 93 . 7% of explained deviance . The capacity of tweets to nowcast , i . e . predict the present events as they occur , may be already enough to provide a time advantage to understanding Dengue situation moment . Twitter was also useful in similar way for tracking and forecasting behavior in the influenza-like illness , as a measure of public interest or concern [11] . Dengue forecast was also possible using the model with tweets as covariate , with up to 8 weeks or 2 months of forecasting window . This result suggests that Twitter data can be used in the development of a proactive surveillance program and help health managers to better directed their resources for disease prevention . One advantage of Twitter is that it can be geolocated at city level , which is a useful spatial resolution for surveillance . This feature strongly differentiates it from other available web-based data , such as Google Trends and Wikipedia [3 , 9] . While GT are available per state [22] , the Wikipedia logs can only be aggregated per language [2] . Cities with higher tweets activity are those with higher Dengue incidence . Both Dengue occurrence and Twitter use are usually associated with cities with higher concentration of population or urbanization [3 , 14] . Similarly , GT and Dengue cases correlate better in states with higher Dengue incidence [22] . The Twitter data has also some limitations to be considered . Not everyone who submits a tweet with Dengue content is actually ill , but just interested or curious about it . Good surveillance will depend on a sufficient volume of interest to generate signals and compensate noise [8] . Therefore , a main challenge remains at areas with smaller population of Twitter users [4] . The tweets model performed better in areas with high Dengue incidence , but its performance was only weakly associated with population size and development index . This may suggest a robust model that can successfully be applied to smaller and less developed cities , which would improve the application effectiveness of the model as a surveillance tool . One advantage of including tweets into forecast models is to improve real-time estimations of Dengue incidence , overcoming difficulties of traditional Dengue surveillance systems that rely solely on case report data . Twitter captures information from individuals , especially at earlier stages of illness , who may search health information on the internet before or even instead of making medical visits , and publish this knowledge to seek help and comfort from friends . Tweets-based models may actually be even more useful in endemic regions of the world where the traditional surveillance system is too weak and slow to react to disease notification . Here we show that the high Dengue estimation capacity of tweets model is influenced by human development indices and internet access . Important to observe that mean , education and income development indices which is associated with more houses with access to a personal computer and internet are also associated with tweets incidence . Otherwise , longevity development index is less associated with tweets incidence and activity , suggesting that young and adults may be the majority of users of this data . The accuracy of Google Trends was not found to be strongly influenced by socio-economic factors , particularly because it relies on internet searches , which may be robust enough to capture population-level disease dynamics [22] . Despite these social limitations , it is clear that tweets-based surveillance provides adequate citywide and countrywide Dengue estimates . Social factors , however , may limit the value of using tweets to examine epidemics within a city . At this stage , freely available tweet data are not sufficient to provide accurate determination of space within a specific city . The capacity of tweets to estimate Dengue cases represents a valuable complement to assist traditional Dengue surveillance . A novel data source , like Twitter , could complement traditional surveillance at low-cost , since it is passive , free , and requires minimal resources to run [3 , 11] . These data can help reduce some of the many gaps that exist in Dengue surveillance methods , such as low sensitivity and accuracy , and timeliness [13 , 14 , 19] . Improving Dengue surveillance in a cost-effective way remains a major obstacle . In Brazil , the underreporting is about 50% , but can reach values as high as 90% , and the reporting delay is estimated to be approximately 3 to 4 weeks [19] . The main added benefit in monitoring social media behavior through tweets is the potential for early warning . Detecting and confirming results of prevention and control measures is possible at the interface between computer science , epidemiology , and medicine [4] . Our study therefore demonstrates that tweets are a web-based data that strongly associate with Dengue cases and have the potential to successfully estimate Dengue cases . Tweets are an easy to use , cost-effectiveness , useful and robust tool for estimating Dengue cases , both at country and city level , and for Dengue forecasting until 8 weeks in the future . | Dengue is a fast-growing mosquito-borne viral disease , with an estimated annual incidence of 390 million infections , of which 96 million manifest clinically . Dengue burden is likely to increase in the future . Mentioning a disease in social networks is correlated with physician visits by patients , and can be used to estimate disease activity . Traditional , biologically-focused monitoring techniques , based on laboratory diagnostics , are accurate but costly and slow . Alternative approaches for surveillance aim to capture health-seeking behavior at earlier stages of disease progression , specially capturing the asymptomatic and mild clinic manifestation population who do not seek medical care formally . Twitter data have potential application for Dengue surveillance , improving the estimation and prediction of the disease , in space and time , being a valuable and low-cost addition to assist traditional surveillance . We show that tweets are strongly associated with Dengue cases . Tweets are a useful tool for estimating and forecasting Dengue cases until 8 weeks in the future , both at country and city level , even in less developed areas . | [
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"(mathematics)... | 2017 | Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level |
Leptospirosis is an emerging zoonosis that is often under-recognized in children and commonly confused with dengue in tropical settings . An enhanced ability to distinguish leptospirosis from dengue in children would guide clinicians and public health personnel in the appropriate use of limited healthcare resources . We conducted a prospective , hospital-based , study of children with acute febrile illnesses and dengue in Thailand . Among the children without dengue , we identified those with leptospirosis using anti-leptospira IgM and microscopic agglutination titers in paired acute and convalescent blood samples . We then performed a case-control comparison of symptoms , signs , and clinical laboratory values between children with leptospirosis and dengue . In a semi-rural region of Thailand , leptospirosis accounted for 19% of the non-dengue acute febrile illnesses among children presenting during the rainy season . None of the children with leptospirosis were correctly diagnosed at the time of hospital discharge , and one third ( 33% ) were erroneously diagnosed as dengue or scrub typhus . A predictive model to distinguish pediatric leptospirosis from dengue was generated using three variables: the absolute neutrophil count , plasma albumin , and aspartate aminotransferase levels in the first 72 hours of illness . Unrecognized leptospirosis can be a significant cause of “dengue-like” febrile illness in children . Increased awareness of pediatric leptospirosis , and an enhanced ability to discriminate between leptospirosis and dengue early in illness , will help guide the appropriate use of healthcare resources in often resource-limited settings .
Leptospirosis is an increasingly recognized cause of acute febrile illness throughout the tropical and sub-tropical regions of the world . Spirochetal infection with Leptospira sp . typically occurs when water or soil contaminated with the urine of an infected animal comes in contact with human skin or mucous membranes [1] , [2] . The clinical manifestations of leptospirosis range from a mild self-limited febrile illness to a severe and potentially fatal illness characterized by jaundice , renal failure , thrombocytopenia , and hemorrhage ( Weil's disease ) . Early in illness , leptospirosis is often indistinguishable from other common causes of acute febrile illnesses in the tropics- e . g . dengue , malaria , scrub typhus , typhoid , and others [1] , [3] . Children in particular often bear the brunt of these tropical diseases , and pose the greatest diagnostic challenges to clinicians . In the pediatric population , leptospirosis and dengue often have similar clinical manifestations and are among the most common diagnostic dilemmas . Both typically occur during the rainy season , and rapid laboratory confirmation of the infecting pathogen is generally not available . Several studies have shown that leptospirosis is often confused with dengue and under-diagnosed in endemic regions [3]–[5] . We conducted a prospective study of acute febrile illnesses and suspected dengue virus ( DENV ) infections among children presenting to hospitals at two sites in Thailand . Children were diagnosed with an acute DENV infection based on virological and serological criteria ( see methods ) . Those without DENV infection , and no evidence of bacterial infection or malaria , were classified as having other febrile illnesses ( OFIs ) . Among the children with OFIs , we were able to test acute and convalescent blood samples for evidence of an acute leptospiral infection . Our results provide important information on the characteristics of pediatric leptospirosis in Southeast Asia and its distinguishing features from dengue in a region where both pathogens circulate .
Details of the investigational protocol have been published previously [6] . Children included in this study were seen at the Queen Sirikit Institute of Child Health in Bangkok , Thailand between 1994–1999 , and at the Kamphaeng Phet Provincial Hospital , Kamphaeng Phet , Thailand between 1994–1997 . The Queen Sirikit Institute of Child Health is a tertiary-level medical center in a large metropolitan area . The Kamphaeng Phet Provincial Hospital is a secondary-level facility in a semi-rural province 360 kilometers north of Bangkok . The investigational protocol was approved by the Institutional Review Boards of the Thai Ministry of Public Health , the Office of the U . S . Army Surgeon General , and the University of Massachusetts Medical School . Parents or guardians of all study subjects gave written informed consent . Enrollment criteria were age 6 months–14 years , a febrile illness with <72 hours of symptoms , no hypotension or shock , and no other obvious source of infection . Children were observed in hospital until at least 1 day after defervescence . All physical exam findings were recorded by one of five study physicians and experienced pediatricians . Venous blood samples were drawn daily from admission up to the day after defervescence or for a maximum of 5 consecutive days . An early convalescent blood sample was also obtained 8–13 days after enrollment . All clinical data were abstracted onto standardized case report forms . Serial daily tourniquet tests were performed in standardized fashion , as previously described [6] . Day 1 was defined as the calendar day of hospital presentation and study enrollment . A complete blood count ( T540 counter , Coulter , Hialeah , FL ) , and plasma aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , and albumin determinations ( Clinical System Analyzer , model 700 , Beckman Instruments , Brea , CA ) were obtained daily . Aliquots of plasma were stored at -70°C . DENV infections were identified by a serotype specific RT-PCR assay on study day 1 plasma samples [7] , or using previously established serologic criteria for IgM/IgG ELISAs in paired acute and convalescent samples [8] . Children with DENV infections were further classified into dengue fever ( DF ) and dengue hemorrhagic fever ( DHF ) grades , according to WHO criteria [9] . Acute febrile illnesses without evidence of DENV infection , routine bacterial infection , or malaria , were classified as OFIs . Leptospirosis diagnostic testing was performed on OFI samples . Paired acute and early convalescent plasma samples collected from OFI subjects were screened for anti-leptospira antibody using the PanBio IgM ELISA ( PanBio Inc . , Brisbane , Australia ) . Values <9 PanBio ELISA units were considered negative , 9–11 equivocal , and >11 positive , as per the manufacturer's instructions . Subjects who demonstrated a rise in IgM from negative to positive or equivocal values , and those with sustained positive or equivocal values ( at both the acute and early convalescent time points ) , were selected for additional confirmatory testing . Confirmatory microscopic agglutination testing ( MAT ) of acute and convalescent plasma samples was performed using a battery of 24 serovars from 20 serogroups selected specifically for use in Southeast Asia . The MAT was conducted in standard fashion , as previously described [10] . Serovars included in the antigen panel were L . biflexa serovar Andamana , L . interrogans serovar Australis , L . interrogans serovar Bratislava , L . santarosai serovar Borincana , L . interrogans serovar Autumnalis , L . kirschneri serovar Butembo , L . borgpetersenii serovar Ballum , L . interrogans serovar Bataviae , L . interrogans serovar Canicola , L . weilii serovar Celledoni , L . interrogans serovar Hebdomadis , L . kirschneri serovar Cynopteri , L . interrogans serovar Grippotyphosa , L . interrogans serovar Copenhageni , L . interrogans serovar Icterohaemorrhagiae , L . borgpetersenii serovar Javanica , L . santarosai serovar Georgia , L . interrogans serovar Pomona , L . interrogans serovar Pyrogenes , L . santarosai serovar Alexi , L . borgpetersenii serovar Tarassovi , L . interrogans serovar Hardjo , Leptospira santarosai serovar Shermani , L . borgpetersenii serovar Sejroe . Cases with ≥1∶800 MAT antibody titer in a single specimen [11] , or a ≥4-fold increase in MAT antibody titers between paired acute and convalescent samples , were classified as definite leptospirosis . Those with MAT antibody titers ≥1∶200 late in illness or at early convalescence , but not meeting the above criteria , were classified as probable leptospirosis . MAT antibody titers ≤1∶100 were considered negative for acute infection . MATs are not adequate for determining the infecting Leptospire serovar but can allude to serogroup . We used the Wilcoxon signed-rank and Mann-Whitney U tests for comparisons of continuous variables not normally distributed . χ2 analysis was used for comparisons among proportional data . Multiple logistic regression models were used to assess the association of leptospirosis vs . dengue with relevant clinical and demographic factors . p<0 . 05 was considered significant . All analyses were carried out using Stata SE 9 . 1 ( StataCorp , College Station , TX ) .
We enrolled 812 children in a prospective study of acute febrile illnesses and suspected DENV infection between 1994–1999 at two sites in Thailand ( Bangkok and Kamphaeng Phet ) . There were 350 children with DENV infections ( n = 232 in Bangkok , n = 118 in Kamphaeng Phet ) and 462 children with non-dengue OFIs ( n = 386 in Bangkok , n = 76 in Kamphaeng Phet ) . We tested for leptospirosis in n = 442 ( 96% ) of the children with non-dengue OFIs . At the semi-rural Kamphaeng Phet site , leptospirosis accounted for 12 of the 64 non-dengue acute febrile illnesses that were screened ( prevalence 19% ) . At the metropolitan Bangkok site , the prevalence of leptospirosis within the non-dengue febrile illnesses was only 1 . 6%- 6 out of 378 cases screened ( Figure 1 ) . Eighteen children were diagnosed with leptospirosis ( 14 definite , 4 probable ) . Their median age was 10 years ( range 4–14 years ) , and the male:female ratio was 3 . 5∶1 . All the children with leptospirosis had self-limited illnesses . 7/18 ( 39% ) received antibiotics at some point during their illness , and no serious short-term sequelae of leptospiral infection were noted . One child with leptospirosis developed primary varicella infection immediately after discharge from the hospital . A summary of the leptospirosis cases is presented in Table 1 . None of the clinical discharge diagnoses included leptospirosis . One third ( 33% ) of the leptospirosis cases were erroneously diagnosed as scrub typhus ( n = 2 , due to false positive Weil-Felix serology ) or dengue ( n = 6 ) . During the rainy season in Kamphaeng Phet , Thailand , dengue accounted for 118/194 ( 61% ) of the acute febrile illnesses that prompted study entry . Leptospirosis accounted for 12/194 ( 6% ) of overall acute febrile illnesses , and at least 16% of the non-dengue acute febrile illnesses . We therefore compared some of the epidemiological characteristics between children with dengue and leptospirosis in Kamphaeng Phet ( dengue-n = 118; leptospirosis-n = 12 ) . The age distribution of children presenting with dengue in Kamphaeng Phet was essentially normal with a median age of 9 . 4 years . The median age of the children with leptospirosis in Kamphaeng Phet was 11 . 0 years , and the distribution was bimodal with a second small peak around age 6 . 5 years ( Figure 2A ) . The male∶female ratio was 3∶1 for the leptospirosis cases and 1 . 3∶1 for the dengue cases , but this difference was not significant ( p = 0 . 12 , χ2 test ) . There was also no difference in the number of people in the household , or the number of siblings ill in the household , between children with leptospirosis and dengue ( data not shown ) . The peak occurrence of leptospirosis was in October , 2–3 months later than the peak occurrence of dengue ( Figure 2B ) . Three quarters ( 75% ) of the children with leptospirosis in Kamphaeng Phet presented after August , whereas only 31% of dengue cases did so ( p = 0 . 001 , χ2 test ) . Overall , children with leptospirosis in an endemic region presented later in the rainy season , and were slightly older or younger , than their counterparts with dengue . We next compared clinical and laboratory variables obtained in the children diagnosed with leptospirosis ( n = 18 ) to all age-and sex-matched children with DENV infections among the 812 children in the study population from both sites ( n = 214 ) . The presenting symptoms and signs in the leptospirosis and dengue groups are shown in Table 2 . Both groups had a similar prevalence of non-specific and constitutional symptoms early in illness . A minority ( ≤13% ) of the children with leptospirosis or dengue presented with hemorrhage or rash . A greater proportion of children with leptospirosis had <10 petechiae on the admission tourniquet test compared to those with dengue ( 67% vs . 36% , respectively , p = 0 . 03 , χ2 test ) . By receiver operating characteristic ( ROC ) analysis , ≤6 petechiae on the admission tourniquet test distinguished leptospirosis from dengue with sensitivity = 61% and specificity = 73% , but only provided a 16% positive predictive value ( PPV ) . On initial physical examination , a palpable liver edge may have been less frequent in the children with leptospirosis compared to those with dengue , but the difference did not reach statistical significance ( 9% vs . 38% , respectively , p = 0 . 06 ) . The white blood cell ( WBC ) count and differential on presentation were markedly different in the children with leptospirosis compared to dengue ( Table 3 ) . The mean WBC count was 10 , 531±3 , 445/mm3 with a relative neutrophil predominance ( 83±11% ) and absolute neutrophilia ( 8 , 932±3 , 570/mm3 ) in the children with leptospirosis . The mean WBC count was 5 , 492±2 , 807/mm3 in those with dengue . The percentage of lymphocytes was higher in dengue than leptospirosis ( 21±14% vs . 13±9% , respectively , p = 0 . 01 ) , but an absolute lymphopenia remained ( 958±670/mm3 ) . There were few circulating immature neutrophils ( band forms ) on admission in those with leptospirosis ( <5% in all cases ) . The percentage of atypical lymphocytes was greater in the children with dengue compared to leptospirosis ( 3 . 7±4 . 4% vs . 1 . 9±2 . 9% , dengue vs . leptospirosis , p = 0 . 01 ) . By ROC analysis , a WBC count ≥6 , 450/mm3 on admission distinguished leptospirosis from dengue with 94% sensitivity and 76% specificity . The mean platelet count on admission trended lower in the children with dengue compared to leptospirosis , but did not achieve statistical significance ( Table 3 ) . There were no differences in the admission hemoglobin or hematocrit levels . Mean plasma AST levels were slightly higher in children with dengue compared to leptospirosis . There were no significant differences in ALT levels on admission ( Table 3 ) . Mean plasma albumin levels on admission were slightly but statistically significantly lower in the leptospirosis group compared to dengue ( 4 . 5±0 . 5 gm/dl vs . 4 . 8±0 . 6 gm/dl , respectively , p = 0 . 03 ) , The age-and sex-matched cohort of children with DENV infections ultimately had DF ( n = 117 ) , DHF Grade I/II ( n = 82 ) , and DHF Grade III ( n = 15 ) . Over the course of their acute illness and hospitalization , a greater proportion of the children with dengue developed a petechial rash compared to those with leptospirosis ( data not shown ) . The platelet nadir was lower in children with dengue compared to leptospirosis ( 99 , 822±68 , 949/mm3 vs . 197 , 167±80 , 477/mm3 , dengue vs . leptospirosis , p<0 . 001 ) . Finally , the degree of hepatomegaly that developed during illness was not significantly different between the two groups ( maximum liver size detected [cm below right costal margin]-leptospirosis: 0 . 5 cm [0 . 3–3 . 4] vs . dengue 2 . 0 cm [1 . 6–2 . 0] , median [95% CI] , p = 0 . 3 ) . Despite statistically significant differences , many individual clinical or laboratory variables were poor discriminators between children with leptospirosis and dengue early in illness . We therefore included the following data obtained on hospital presentation in a logistic regression model: number of petechiae on the tourniquet test , WBC count , absolute neutrophil count ( ANC ) , atypical lymphocyte percentage , platelet count , AST , and albumin levels . A higher probability of leptospirosis compared to dengue was independently associated with higher ANC , lower albumin levels , and AST levels between 30–80 IU/ml on presentation ( Table 4 ) . The area under the ROC curve for the predictive model was 0 . 94 ( Figure 3 ) . With a probability cutoff >10% , the predictive model gave a sensitivity of 83% , specificity 90% , PPV 42% , and negative predictive value ( NPV ) 98% for leptospirosis compared to dengue . A probability cutoff >30% produced a sensitivity of 61% , specificity 96% , PPV 55% , and NPV 96% .
Leptospirosis in children is often under-diagnosed , especially in those who do not present with the severe icteric form of disease . In a semi-rural region of north central Thailand , we found that leptospirosis accounted for at least 6% of all acute undifferentiated febrile illnesses , and 19% of the non-dengue illnesses , among children presenting to hospital during the rainy season . As expected , the prevalence of leptospirosis was much lower in the metropolitan environment of Bangkok . We used the PanBio IgM ELISA to screen for leptospirosis in well-timed acute and convalescent blood samples , and performed MAT on any equivocal or positive samples . It is unlikely that a large number of leptospirosis cases were missed , since the sensitivity of the PanBio IgM ELISA as used in this study has been reported as high ( 76–90% ) [12] , [13] . Some characteristics of the children we identified with leptospirosis were similar to what has been previously reported . There was a male predominance , cases peaked during times of flooding ( i . e . late rainy season ) , and illness was generally less severe than what is typically reported in adults [14]–[17] . Our cohort of children with leptospirosis had self-limited and even milder disease than what has been reported from studies of pediatric leptospirosis in Brazil , Reunion Island , and India [14]–[16] , [18] , [19] . One reason may be differences in the predominant circulating Leptospira serovars . Serovars that have been reported to be associated with severe illness in children are Icterhemorrhagiae , Copenhageni , Canicola , and Sejroe [14] , [16] . In our cohort of Thai children with leptospirosis , we noted predominant seroreactivity to serovars Autumnalis and Andamana ( serogroups Autumnalis and Andamana , respectively ) . However , others have reported different serovar associations with disease severity [1] or none at all [20] . Another reason for the mild disease seen here could be the slightly younger age distribution of the children in our study . An age-dependent association with the severity and case-fatality rate of leptospirosis has been observed across many regions of the world [15] , [21] . Host factors that may contribute to this association could include higher organism loads with increasing age , or age-dependent changes in innate and adaptive immune responses to leptospiral infection . Finally , 39% of the children received antibiotics , but not because leptospirosis was suspected . We cannot determine if early antibiotic therapy played a role in ameliorating the severity of some pediatric leptospiral disease . In our cohort of 18 children with confirmed leptospirosis , none were diagnosed correctly by the time of hospital discharge . The most common specific alternative diagnoses were dengue and scrub typhus . Leptospirosis is often indistinguishable from dengue at the critical early stages of illness , and the two are confused commonly [3] , [4] . A lack of affordable and accurate diagnostic tests in many settings also contributes to the diagnostic confusion . Predictive models using more readily available clinical and laboratory characteristics would provide useful information to practitioners . We found that presenting symptoms within the first 3 days of illness were not helpful in distinguishing children with leptospirosis from dengue . A lower petechial count on the standardized tourniquet test was associated with leptospirosis compared to dengue in our study and among adult patients in Bangladesh [3] . However , the predictive value of this single test was poor , and the association did not remain significant in a multivariate predictive model . Conjunctival abnormalities were seen in 2/18 ( 11% ) of the children with leptospirosis in this study . Conjunctival inflammation or hemorrhage may have been confused with conjunctival suffusion ( with or without hemorrhage ) , a condition classically described in leptospirosis [22] . The children with leptospirosis also tended to have a lower degree of hepatomegaly than the age-and sex-matched dengue patients . Similar observations were reported among children with leptospirosis and dengue in Mumbai , India [19] . Unfortunately , none of the aforementioned physical signs had sufficient prevalence or discriminatory capability to be useful early in illness . We found that the most striking difference on presentation between children with leptospirosis and dengue existed in their WBC count and differential . The most significant single laboratory value independently associated with leptospirosis compared to dengue was the absolute neutrophil count ( ANC ) . Neutrophilia is often reported in leptospirosis [22] . However , the ANC has not been previously reported as a useful indicator of leptospiral infection in children [14] , [16] , [18] , or distinguishing leptospirosis from dengue in any age group [3] , [19] . The predominance of mature neutrophils and paucity of band forms on the WBC differential might also be an additional useful clue to the diagnosis of leptospirosis . We found that the combination of 3 laboratory values early in illness-ANC , albumin , and AST-provided the best ability to distinguish between leptospirosis and dengue among children with acute undifferentiated febrile illnesses presenting to the hospital . This predictive model will need to be tested and validated in a prospective fashion in order to determine its potential clinical utility . Our study was limited to symptomatic children presenting to a hospital for evaluation . Its conclusions cannot be extrapolated to the spectrum of disease that does not present to the hospital and may have different clinical and laboratory manifestations . Some potentially useful clinical variables may also not have emerged in our analysis due to the relatively small number of children with leptospirosis . With increased recognition of children with leptospirosis in Kamphaeng Phet , Thailand [23] , future comparative studies will have greater statistical and discriminatory power . In the tropics , leptospirosis can be a significant cause of “dengue-like” febrile illness among children presenting to the hospital during the rainy season . Increased awareness of pediatric leptospirosis , and an enhanced ability to discriminate between leptospirosis and dengue early in illness , will help guide the appropriate use of healthcare resources in often resource-limited settings . | Two of the most common causes of acute febrile illnesses among children in the tropics are leptospirosis and dengue . Early in illness , these two conditions are often indistinguishable and rapid laboratory confirmation of the infecting pathogen is generally not available . An enhanced ability to distinguish leptospirosis from dengue in children would guide clinicians and public health personnel in the appropriate use of limited healthcare resources . In a prospective , hospital-based , study of children with acute febrile illnesses conducted in Thailand , we compared clinical and laboratory characteristics between children with leptospirosis and dengue . Unrecognized leptospirosis was a significant cause of “dengue-like” febrile illness among children presenting to a semi-rural hospital during the rainy season . The presenting symptoms and physical examination findings were poor at discriminating between children with leptospirosis and dengue . A predictive model to distinguish pediatric leptospirosis from dengue was generated using three laboratory values on hospital admission . Our results provide important information on the characteristics of pediatric leptospirosis in Southeast Asia and its distinguishing features from dengue in a region where both pathogens circulate . | [
"Abstract",
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"Methods",
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] | [
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"diseases/bacterial",
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] | 2007 | A Comparative Study of Leptospirosis and Dengue in Thai Children |
Parasites and pollutants can both affect any living organism , and their interactions can be very important . To date , repeated studies have found that parasites and heavy metals or metalloids both have important negative effects on the health of animals , often in a synergistic manner . Here , we show for the first time that parasites can increase host resistance to metalloid arsenic , focusing on a clonal population of brine shrimp from the contaminated Odiel and Tinto estuary in SW Spain . We studied the effect of cestodes on the response of Artemia to arsenic ( acute toxicity tests , 24h LC50 ) and found that infection consistently reduced mortality across a range of arsenic concentrations . An increase from 25°C to 29°C , simulating the change in mean temperature expected under climate change , increased arsenic toxicity , but the benefits of infection persisted . Infected individuals showed higher levels of catalase and glutathione reductase activity , antioxidant enzymes with a very important role in the protection against oxidative stress . Levels of TBARS were unaffected by parasites , suggesting that infection is not associated with oxidative damage . Moreover , infected Artemia had a higher number of carotenoid-rich lipid droplets which may also protect the host through the “survival of the fattest” principle and the antioxidant potential of carotenoids . This study illustrates the need to consider the multi-stress context ( contaminants and temperature increase ) in which host-parasite interactions occur .
Many aspects of host-parasite interactions have been studied in detail , from molecular mechanisms to adaptive strategies and their ecological and evolutionary consequences ( reviewed in Schmid-Hempel 2011 [1] ) . In contrast , few studies have considered the context of multiple environmental stressors in which host-parasite interactions occur in natural conditions . Consequently there are important limitations to our understanding of the ecology and evolution of host-parasite interactions , and to our ability to reach reliable conclusions . For example , increasing numbers of taxa ( both , free living and parasites ) are exposed to pollution and impacted by climate change [2] . However , the complex relationships between these factors ( i . e . parasites , pollution and climate change ) are not well understood . Parasites and hosts can react differently to pollutants , influencing their mutual interactions . For instance , if the parasite is more susceptible than the host to the pollutant , the exposure to pollution provides the indirect benefit ( for the host ) of protecting against the parasite . Conversely , the epidemiology of the parasite may be altered negatively if the pollutant impacts the life history of the host ( e . g . reducing survival ) , thus compromising parasite transmission . Moreover , parasites and pollution can interact to affect the health of the host , the central topic of the emerging field of “Environmental Parasitology” [3] . Most studies evaluating the joint effect of parasites and pollution on the health of free-living organisms find that there are additive or synergistic effects between these stressors [4 , 5 , 6] . For example , coinfection of amphipods by acanthocephalans and microsporidians led to a reduction in antitoxic defenses when exposed to cadmium [7] . However , parasites can influence multiple facets of host phenotype [8] , including physiology , behavior and biochemistry , so they may change the host response to a pollutant in diverse and complex ways . The physiology of the parasite itself may have a direct influence , e . g . through the capacity observed in several parasite groups to bioaccumulate contaminants [9 , 10] . Therefore , more studies are required to understand the diversity of ways parasites and pollution can interact to affect the health of organisms . Of particular concern are the mechanistic ( proximate ) bases of host-parasite interactions; for example the potential of oxygen-free radicals and other reactive oxygen species ( ROS ) to induce oxidative damage in tissues and cellular components , leading to adverse health effects and diseases . The evaluation of the levels of antioxidant enzymes in infected and uninfected organisms can provide valuable information on antioxidant status and their capacity to confront multiple stress conditions . Furthermore , all these effects are likely to depend on physiological variation which is influenced by temperature and hence by climate change . The projected temperature increase this century [11] is likely to increase the toxicity of pollutants [12] as well as the transmission , distribution and abundance of many parasites [13 , 14 , 15] . To date , studies of interactions between parasites and pollutants on hosts have only been carried out at a single temperature , with the exception of one investigation [16] dealing with the effect of trematode infection and seasonal temperature on bioaccumulation of xenobiotics in freshwater clams . Brine shrimps Artemia spp . ( Branchiopoda , Anostraca ) are economically and ecologically important . They are used as model organisms in aquatic toxicology [17 , 18] and are the most important live food used in aquaculture worldwide [19] . Artemia are the dominant macroinvertebrate in hypersaline ecosystems around the world , and many waterbirds depend on them as food [20 , 21 , 22] . They control phytoplankton populations [23] and are also intermediate hosts for a rich community of avian cestodes [24] . These parasites cause strong physiological and behavioural changes in Artemia [25 , 26 , 27] which may be expected to influence their response to pollutants . We studied the Artemia parthenogenetica population from the Odiel and Tinto estuary , SW Spain , one of the most polluted estuarine systems in Western Europe [28] . A . parthenogenetica from Odiel have high levels of cestode infection [29] , especially of Flamingolepis liguloides which uses flamingos as final hosts , and Confluaria podicipina which infects grebes [30] . Arsenic is a highly toxic and bioaccumulable metalloid that originates from both anthropogenic and natural sources and causes detrimental effects in humans and wildlife [31] . Arsenic in the Odiel and Tinto estuary originates from historic and current mining activity [32 , 33 , 34] . An estimated 12 t yr-1 and 23 t yr-1 of As is transported by the Tinto and Odiel rivers ( respectively ) into the Atlantic Ocean [35] . Previous studies have found high levels of inorganic arsenic in sediments ( 85–610 ppm ) in the study area [36] , exceeding the ERM ( Effects Range Median ) for marine and estuarine sediments ( 70 ppm [37] ) , and also the Canadian sediment quality guideline ( CSQG ) value for the protection of aquatic life ( 7 . 2 ppm [38] ) . This study was designed to test the individual and combined effects of infection by cestode parasites and a 4°C temperature change on the sensitivity of Artemia to acute As exposure . We conducted toxicity tests to compare As median lethal concentrations at 24 h ( 24-h LC50 ) for infected and uninfected individuals on two separate dates one month apart , in order to test the effect of different parasite assemblages on arsenic sensitivity . Since cestode parasites in A . parthenogenetica have a strong seasonal pattern [29] , and different parasite species induce different physiological effects in their host , we chose to consider the effects of different parasite compositions ( i . e dates ) . We also evaluated the effect of parasites on the antioxidant defense mechanisms of Artemia , in order to measure the capacity of infected animals for detoxification of reactive oxygen species caused by factors such as pollution or climate change . We included the activity of four important enzymes: superoxide dismutase ( SOD ) responsible for the detoxification of the highly reactive oxygen species superoxide anions; catalase ( CAT ) which catalyses the decomposition of hydrogen peroxide in 02 and H20; glutathione peroxidase ( GPX ) which acts as a scavenger for high concentrations of hydrogen peroxide; and glutathione reductase ( GR ) implicated in the reduction of glutathione disulphide to the sulphydryl form glutathione , which is a critical molecule in combating oxidative stress . In order to evaluate the efficacy of the above enzymatic mechanisms for control of reactive oxygen species , we also measured the levels of thiobarbituric acid reactive substances ( TBARS ) , which indicates the extent of lipid peroxidation as a consequence of oxidative damage . Finally , we quantified the carotenoid-rich lipid droplets of infected and uninfected individuals . These droplets serve as intracellular lipid storage but also play a protective role in protecting cells against oxidative stress .
It was impossible to analyse oxidative stress in individuals used in acute toxicity tests because of the need to crush the specimens in order to check for parasites , so we conducted a separate sampling in May 2015 . Parasite prevalence has a seasonal pattern [29] , so in order to have a similar parasite assemblage , samples were collected in the same calendar month as those used in toxicity tests and from the same ponds of medium-high salinity ( 140–220 g/l ) . One subsample was used for oxidative stress analysis and another to characterize parasite assemblage and to quantify lipid droplets . Infected and uninfected individuals were recognised on the basis of their colour [392] . Table 3 shows the infection index of infected individuals . As expected , infection was dominated by F . liguloides , and its prevalence did not differ from the sampling of May 2014 ( Mann-Whitney U test , U = 13158 , P = 0 . 585 ) . Prevalence and abundance of minority cestode species varied among dates . The analysis of oxidative stress in infected and uninfected Artemia revealed the association of parasites with strong changes in the antioxidant capacity of the host . Infected individuals showed significantly higher levels of CAT ( Fig 5 , t = -2 . 892 , P = 0 . 02 ) and GR activities ( Fig 5 , t = -2 . 881 , P = 0 . 002 ) , whereas SOD was higher in uninfected individuals ( t = 2 . 739 , P = 0 . 03 ) . Conversely , parasites had a negligible effect on GPX ( t = -0 . 814 , P = 0 . 447 ) and TBARS ( t = -1 . 781 , P = 0 . 125 ) . We also quantified lipid droplets , which are readily visible throughout the body and appendices of adult Artemia ( Fig 2B ) . Lipid volume estimates are strongly correlated with biochemical measurements of lipids [41] , and are highly related with the ability of organisms to protect themselves against pollutants [42] . Infection with cestodes was associated with increased number of lipid droplets ( Mann-Whitney U test , U = 45 . 5 , P < 0 . 001 , n = 20; Fig 6 ) .
This study tested the effect of parasites on Artemia sensitivity to As , and explored the physiological crosstalk between the parasite and the host , measuring oxidative stress and lipid content in infected and uninfected Artemia . Our study provides the first empirical evidence that parasites can increase resistance to metal or metalloid pollution , rather than decrease it . It is also the first study to consider the influence of temperature change on parasite-pollutant interactions . In three separate acute toxicity experiments , Artemia infected with cestodes consistently showed lower sensitivity to As than uninfected individuals . The higher sensitivity of infected Artemia in April suggests that multiple infections may reduce the benefits of cestode infection to host resistance . Our results contradict the pre-existing view that pollution and parasites are stressors that both have negative effects on the health of free living organisms . This view was based on previous field and laboratory investigations ( including chronic and acute exposure to a wide variety of toxicants , in vertebrates and invertebrates , intermediate and definitive hosts , and in several groups of parasites [43 , 44 , 45] . The results of oxidative stress analysis provide a mechanistic explanation for our findings . Infected individuals exhibited much higher levels of CAT and GR , reflecting a superior ability to combat the effects of exposure to pollutants with oxidative potential , such as As . The particularly high levels of CAT in infected individuals ( nearly double that of uninfected Artemia ) is related to the increased levels of hemoglobins in F . liguloides-infected Artemia compared with uninfected individuals ( Fig 2C and 2D ) . CAT is a haematin protein complex with four porphyrin heme groups that allow the enzyme to react with hydrogen peroxide . There is a close linear relationship between CAT activity and hemoglobin concentration in human blood [46] . CAT also has one of the highest rates of all enzymes; one CAT molecule is able to catalyse the conversion of 5 million molecules of hydrogen peroxide per second to water and oxygen . Thus , even if the level of SOD was lower in infected individuals , the control mechanisms via CAT and GR seem to be sufficient to avoid the establishment of an oxidative stress condition , as indicated by the lack of changes in TBARS between infected and uninfected individuals . TBARS is a by-product of lipid peroxidation , so this result indicates that parasites are not inducing damage by oxidative stress . Our results conflict with what most studies have shown up to now ( but see for example Marcogliese et al 2005 [6] ) . Infection by parasites and pathogens of a wide range of taxa are generally associated with the inhibition or weakening of the host antioxidant system , and the concomitant increase in TBARS [47 , 48 , 49] . This enhancement of the anti-oxidative defense mechanisms is probably connected with the trophic transmission mode of cestodes that infect Artemia , which means they require “healthy” hosts in order to increase transmission success ( through predation by the definitive host ) . Decrease in antioxidant status enhances short-term survival prospects [50] , so potentiating it may be part of the transmission strategy of the parasite to increase longevity and probability of transmission ( as in the “parasite manipulation hypothesis” [51] ) . We also found that there were more lipid droplets in infected individuals , which is consistent with previous work indicating that F . liguloides increases total lipid levels [52] and that C . podicipina increases triglyceride levels in Artemia [26] ( Fig 2B ) . Lipids have a high heavy metal binding capacity , and lipid content has a significant effect on the accumulation of As in other organisms [53] . Neutral lipids such as those in lipid droplets can protect organisms against pollutants , sequestering them away from sensitive target sites [54 , 55]—a principle known as survival of the fattest [41] . Although many studies support this principle , none have addressed parasite-mediated effects . The only previous study to suggest that parasites can increase host survival under polluted conditions through a lipid-related effect was on the freshwater clam Pisidium amnicum [56] . Clams infected by digenean trematode larvae are less sensitive to pentachlorophenol , perhaps because the high lipid content of the parasite changes the internal distribution of the toxicant . Pentachlorophenol is moderately lipophilic so is expected to accumulate in adipose tissue , whereas the target sites for toxicity are the mitochondria [56] . In parasitized Artemia , additional lipids accumulate in the host , not in the parasite as in the case of Pisidium . Lipid droplets in infected Artemia are associated with carotenoids ( see Fig 2B ) and this , together with hemoglobins induced by parasites , largely explains the colour change that allowed us to separate infected individuals with the naked eye ( Fig 1 ) . Both F . liguloides and C . podicipina increase the concentration of carotenoids in infected Artemia [52] . In contrast , carotenoid concentrations in other animals are often negatively correlated with parasite load [57] and with pollutants [58] . Carotenoids are potent lipid-soluble antioxidants [59] and are able to inhibit lipid peroxidation in liposomes [60] . The accumulation of carotenoids in infected Artemia is also considered a parasite strategy to increase the probability of being predated by birds ( the final host ) by increasing visibility [61] and enhancing nutritive value [26] . Carotenoids provide protection against oxidative stress in many free living organisms [62 , 63 , 64] so they may also increase longevity in infected Artemia . Given that oxidative stress is a common marker of toxicity , not only for As in plants , invertebrates and vertebrates [65 , 66 , 67] but also for heavy metals ( e . g . lead , cadmium and mercury [68] ) , cestode parasites may protect Artemia against a broader range of pollutants . Unlike carotenoids , a positive effect of parasites on host lipid content is common in nature , e . g . in acanthocephalans infecting gammarids [69] or trematodes infecting bivalves [70] . Therefore , our finding regarding increased resistance to As in the presence of parasites may not be an isolated case , and more studies are needed to evaluate how frequently this occurs in nature . The differences in sensitivity to As observed in infected Artemia collected on different dates have several possible explanations , including a negative effect of the generally higher infection levels , or a higher pathogenicity of C . podicipina which was absent in May . Alternatively , it could be related to seasonal changes in the ages of the parasites or their hosts , or changes in the lipid or carotenoid levels in the host . Increased heavy metal accumulation with age in cysticercoids has been shown in other cestodes [71 , 72] . Previous studies of the interactions between parasites and pollutants on toxicity have focused on individual parasite species ( but see Gismondi et al . [7] ) . In nature , co-infections of different parasites are extremely common , and our study illustrates the need to consider the effects of co-infections in environmental parasitology . The toxicity of As is highly dependent on its bioavailability , which in turn is dependent on its chemical form and the capacity to be released from environmental matrices [73] . In marine environments , As occurs predominantly as the inorganic forms arsenate ( As ( V ) ) , and arsenite ( As ( III ) ) , and is significantly more bioavailable from seawater and porewater than from sediments [74] . Moreover , temperature can strongly affect the chemical behaviour of pollutants and their bioavailability [75 , 76 , 77] , but also the physiology of aquatic organisms . There are many studies of the effect of temperature on heavy metal toxicity , but we are not aware of any that integrate the influence of parasites . A temperature rise of 4°C caused a significant decrease in the LC50 for both infected and uninfected Artemia . The decrease in dissolved oxygen in hypersaline waters with increasing temperature coincides with higher respiratory demands in Artemia . Water permeability and drinking in Artemia increase markedly with temperature [78] , hence uptake of pollutants will also increase . Indeed , copper uptake in Artemia increases with temperature owing to increased activity and diffusion rate [79] . Differences in As toxicity in fish have also been associated with higher uptake at higher temperatures [80] . Parasites and pathogens are conventionally considered as detrimental for a host , but they can also have positive impacts with consequences for non-host species and even the whole ecosystem [81] . We provide evidence that parasites can also benefit their hosts by increasing resistance to pollutants in contaminated environments . Infection by parasites was associated with an improved antioxidant defense system ( CAT and GR ) without oxidative damage , as confirmed by unaffected values of TBARS . Parasites also increased the number of lipid droplets in infected individuals , which is a common phenomenon in intermediate hosts manipulated by parasites , so more studies in other host-parasite systems are needed to evaluate the wider relevance of our findings . Our results provide an important advance in our understanding of host-parasite interactions and underline the importance of considering interactions between parasites , pollutants and temperature in combination , particularly given expected climate change and the likelihood that toxicity will increase with temperature .
Naturally infected and uninfected adults of A . parthenogenetica were collected with a plankton net ( 0 . 5 mm ) within the Odiel saltpan complex ( see Sánchez et al . [21] for details of the study area ) . Sampling was carried out on two dates ( 14th of April and 15th of May 2014 ) at which the relative abundance of different cestode parasites changed considerably . On the 14th of April , Artemia were collected from three ponds with salinities of 140 , 150 and 190 g/l ( measured with a refractometer ) . On the 15th of May , samples were collected from another pond with a salinity of 200 g/l . These four ponds were all within the same stage of the solar evaporation process , were hydrologically interconnected , and had similar sediment type , depth , and invertebrate and bird communities [21] . These ponds were selected on the basis of the abundance of live Artemia . Artemia were transported to the laboratory in 25 litre containers . Infected and uninfected Artemia individuals of a similar size were then selected on the basis of their colour ( Fig 1 ) ; infected individuals are visually recognisable by the bright red colouration induced by the cestodes , whereas uninfected individuals are practically transparent [39] . Similar size ( as a proxy of age ) was selected since this may influence As concentrations or sensitivity [82 , 83] . There is no difference in growth rates between infected and uninfected shrimp [52] . Of the individuals visually allocated to the infected group prior to experiments , 98% were truly infected when inspected afterwards ( n = 989 ) . Among individuals allocated to the uninfected group , 98% were truly uninfected ( n = 988 ) . Toxicity experiments were conducted after 24 h of acclimation of the Artemia at 100 g/l salinity ( artificial salt mixture of Instant Ocean dissolved in distilled water ) . Two experiments were carried out . The first ( with Artemia collected on 14th of April ) was conducted at 25°C ( the mean annual temperature in the Odiel salt pans [21] ) . The second ( Artemia collected on 15th of May ) was conducted at both 25 and 29°C . During summer months , Artemia are often exposed to temperatures ≥ 29°C [21 , 22] and the frequency of these events is expected to increase in future . Median lethal concentration ( LC50 ) was used to quantify As toxicity in infected and uninfected adult Artemia . Arsenic , as reagent-grade sodium arsenate , NaAsO ( CAS No . 10048-95-0 ) was used to prepare a concentrated stock solution . The study design concentrations were prepared by mixing different proportions of the stock solution and saltwater ( Instant Ocean prepared with MilliQ ) . The saltwater used for the dilutions was prepared within 24 h of the start of the experiment and As added one hour before conducting the experiment ( to prevent oxygen depletion ) . Ten concentrations of As between 5 and 140 mg/l were used for the experiments at 25°C ( 0 , 5 , 20 , 35 , 50 , 65 , 85 , 95 , 110 , 125 , 140 mg/l ) , and ten between 4 and 67 mg/l were used for the experiment at 29°C ( 0 , 4 , 11 , 18 , 25 , 32 , 39 , 46 , 53 , 60 , 67 mg/l ) in order to estimate the LC50 . Experimental concentrations were adjusted to the observed mortality ( higher mortality at 29°C implied lower tested concentrations ) . These concentrations were selected after preliminary tests . Three replicates were used per concentration , with each replicate made up of a group of 10 individuals . Infected ( red ) and uninfected ( transparent ) individuals were transferred to 25 ml beakers ( 10 individuals per beaker ) and placed in climatic chambers at the chosen temperature , with a 12:12 photoperiod and without food . After a 24 h exposure period , dead individuals ( considered to be those with no movement of the appendages observed within 10 seconds ) were counted and all ( alive or dead ) individuals were mounted on slides to confirm parasitic status under the microscope . After observations of the cysticercoids ( cestode larval stage in the intermediate host ) in situ , each infected specimen was gently pressed under the coverslip . If the identification of the cysticercoids recorded was not possible at this stage , whole infected brine shrimps or isolated cysticercoids were prepared as permanent mounts in Berlese’s medium in order to facilitate observations on the morphology of rostellar hooks . Cysticercoids were identified following Georgiev et al . [30] and Vasileva et al . [84] . Prevalence ( P ) and mean abundance ( MA ) were calculated separately for the “infected group” on both dates . Artemia were sampled in May 2015 from Odiel salt ponds of intermediate-high salinity . Brine shrimps were transported to the laboratory and placed in artificial sea water ( Instant Ocean , 100 g/l ) before the experiment . A subsample was used to characterize the exact parasite composition ( n = 60 infected individuals ) and quantify the number of lipid droplets ( n = 20 infected and 20 uninfected Artemia ) . The numbers of cysticercoids , prevalence , mean abundance and mean intensity ( see Bush et al . 1997 [85] for definitions ) were calculated for each cestode species . The number of lipid droplets was estimated according to Wurtsbaugh & Gliwicz 2001 [86] . We quantified lipid levels by inspecting individuals at 30x magnification and counting the number of lipid droplets along the right side of the 5th and 6th segments of the body . The rest of the specimens were acclimated during 24h to the experimental salinity with continuous aeration and fed ad libitum with lyophilized Tetraselmis chuii algae . The toxic concentrations of 4 . 69 mg/l As was selected on the basis of preliminary LC50 tests ( the lowest concentration at which mortality was detected ) . Infected and uninfected A . parthenogenetica of the same size range were allocated to 1L glass vials ( 100 individuals per vial ) with 600 ml of experimental solution ( either control ( no As ) or 4 . 69 mg/L As ) during 24h at 25°C ( 12:12 photoperiod ) without food . After 24h exposure , individuals were gently washed in distilled water and , after removing excess water , stored at ‒80°C until biochemical analysis . All operations were performed at 4°C to prevent enzyme or tissue degradation . We performed the biochemical analysis on pools of 20 individuals per treatment . Number of replicates varied from 1 to 12 depending on Artemia availability . The different biomarkers were determined in the whole soft tissues after homogenization and centrifugation . Tissues were homogenized with an electrical homogenator ( Miccra D-1 Art Moderne Labor Technik ) in cool homogenization buffer ( Tris-HCl 100 mM , EDTA 0 . 1 mM , Triton X-100 0 . 1% , pH7 . 8 ) using 200 μl of buffer per sample ( 20 individuals ) . The sample was centrifuged at 14 , 000 rpm at 4°C for 30 minutes and supernatant stored at −80°C until enzymatic determination . We quantified five parameters as a proxy for oxidative status of Artemia: activity of four enzymes ( catalase CAT , superoxide dismutase SOD , glutathione peroxidase GPx and glutathione reductase GR ) and lipid peroxidation levels ( thiobarbituric acid reactive substances TBARS ) . Total protein content in the supernatant fluid was determined following a standard Bradford’s procedure [87] . Enzyme activity was determined colorimetrically . Concentration of each indicator was estimated following the specific procedures below . The median acute lethal concentration ( LC50 ) and 95% confidence limits were estimated and compared between infected and uninfected individuals , different temperatures and dates using Trimmed Spearman-Karber ( TSK ) analysis for lethal tests [94] . The criterion of non-overlapping 95% confidence limits was used to determine a significant difference ( p < 0 . 05 ) between LC50 values [40] . To test the effect of date , temperature , parasitic status and As concentration on mortality we performed GLZ with a Poisson error distribution , log link function and correction for overdispersion . Date , temperature and parasitic status were included as categorical factors , and As concentration as a continuous variable . Stepwise backwards removal was used to obtain a final model containing only significant factors . Differences in prevalence of different cestode species between the two sampling dates were evaluated with Z-tests . Comparisons of cestode abundance were performed with Mann-Whitney U tests . We also used Mann-Whitney tests to compare enzymatic activity and lipid peroxidation , as well as lipid droplets between infected and uninfected individuals . Statistica 12 software for Windows was used for all statistical analyses [95] . | Virtually all free-living organisms are infected by parasites . Moreover , both parasites and hosts may be exposed to increasing levels of pollution and might be affected by climate change . However , few studies have considered the environmental context in which parasites and hosts interact , and the relationships between these factors remains poorly understood . It is assumed that infection with parasites increases mortality under a cause of stress such as pollution . We studied the combined effect of arsenic ( As ) pollution , temperature increase and infection by tapeworms on the health of the economically and ecologically important brine shrimp Artemia . We found that tapeworms make Artemia more resistant to As , a major pollutant in aquatic environments , even under increased temperature conditions . These parasites increase the capacity of antioxidant enzymatic defenses , allowing infected individuals to cope better with As . Moreover , tapeworms increase fat reserves in their hosts , which may be advantageous due to the ability of lipids to sequester pollutants ( “survival of the fattest” principle ) . Although our results may be unusual , we find a clear explanation for them . This makes them of broad significance and general interest . | [
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... | 2016 | When Parasites Are Good for Health: Cestode Parasitism Increases Resistance to Arsenic in Brine Shrimps |
How long-term memories are stored is a fundamental question in neuroscience . The first molecular mechanism for long-term memory storage in the brain was recently identified as the persistent action of protein kinase Mzeta ( PKMζ ) , an autonomously active atypical protein kinase C ( PKC ) isoform critical for the maintenance of long-term potentiation ( LTP ) . PKMζ maintains aversively conditioned associations , but what general form of information the kinase encodes in the brain is unknown . We first confirmed the specificity of the action of zeta inhibitory peptide ( ZIP ) by disrupting long-term memory for active place avoidance with chelerythrine , a second inhibitor of PKMζ activity . We then examined , using ZIP , the effect of PKMζ inhibition in dorsal hippocampus ( DH ) and basolateral amygdala ( BLA ) on retention of 1-d-old information acquired in the radial arm maze , water maze , inhibitory avoidance , and contextual and cued fear conditioning paradigms . In the DH , PKMζ inhibition selectively disrupted retention of information for spatial reference , but not spatial working memory in the radial arm maze , and precise , but not coarse spatial information in the water maze . Thus retention of accurate spatial , but not procedural and contextual information required PKMζ activity . Similarly , PKMζ inhibition in the hippocampus did not affect contextual information after fear conditioning . In contrast , PKMζ inhibition in the BLA impaired retention of classical conditioned stimulus–unconditioned stimulus ( CS-US ) associations for both contextual and auditory fear , as well as instrumentally conditioned inhibitory avoidance . PKMζ inhibition had no effect on postshock freezing , indicating fear expression mediated by the BLA remained intact . Thus , persistent PKMζ activity is a general mechanism for both appetitively and aversively motivated retention of specific , accurate learned information , but is not required for processing contextual , imprecise , or procedural information .
Although the molecular mechanisms of initial memory consolidation have been extensively studied , little is known about the mechanism of persistent memory storage [1] . Recently , however , the persistent phosphorylation by the autonomously active protein kinase C ( PKC ) isoform , protein kinase Mzeta ( PKMζ ) , has been shown to be critical for the maintenance of aversive long-term memories , specifically , place avoidance in the hippocampus [2] and conditioned taste aversion in the neocortex [3] . PKMζ was initially identified as a persistently active kinase that is both necessary and sufficient for the maintenance of long-term potentiation ( LTP ) [4 , 5] . PKMζ is a persistently active kinase because of its unique structure [4] . Most PKC isoforms consist of an N-terminal regulatory domain , which contains second messenger-binding sites and an autoinhibitory pseudosubstrate sequence , and a C-terminal catalytic domain [6] . Under basal conditions , the pseudosubstrate interacts with the catalytic domain and maintains the enzyme in an autoinhibited resting state . Second messengers , such as diacylglycerol or Ca2+ , can then activate full-length PKCs by binding to the regulatory domain , causing a conformational change that releases the autoinhibition . PKMζ , in contrast , is an independent PKCζ catalytic domain , which , lacking autoinhibition from a regulatory domain , is autonomously active . In the brain , PKMζ is generated by an internal promoter within the PKCζ gene , which produces a PKMζ mRNA that encodes only the ζ catalytic domain [7] . During LTP , tetanic stimulation induces de novo synthesis of PKMζ , increasing the amount of the persistently active kinase [7 , 8] . The persistent PKMζ activity is critical for maintaining enhanced synaptic transmission , because inhibition by the cell-permeable zeta inhibitory peptide ( ZIP ) , which mimics the pseudosubstrate of the missing PKCζ regulatory domain , reverses synaptic potentiation in the hippocampus when applied up to 1 d after LTP induction [2] . The effect is specific to potentiated synapses because the same dose of ZIP does not affect baseline synaptic transmission [2] . In parallel studies , this dose of ZIP eliminated the retention of long-term memory , but not short-term memory , for place avoidance in the hippocampus [2] and disrupted the storage , but not acquisition , of conditioned taste aversion in the insular neocortex [3] . Despite affecting retention of these aversively conditioned long-term memories , PKMζ inhibition had no effect on taste familiarity , although it is not known whether memory supporting taste familiarity is stored in the insula [3] . Thus , whether the persistent activity of PKMζ maintains all information in a brain region is a critical open question . To address this issue , we studied a battery of conditioned behaviors that require either the dorsal hippocampus ( DH ) or the basolateral amygdala ( BLA ) for memory retention , as previously determined by posttraining ablation studies . To compare results across a range of types of long-term memory induced by widely different behavioral paradigms , 1 d after the completion of training to acquire long-term memory , we injected the standard dose of ZIP that locally reverses 1-d-old in vivo LTP without affecting baseline synaptic transmission [2] .
Previous studies in the DH of place avoidance memory had shown that the PKMζ inhibitor ZIP , but not the conventional and novel PKC isoform inhibitor staurosporine , which does not effectively inhibit PKMζ , caused a selective loss of long-term memory retention [2] . The only other potent PKMζ inhibitor to have been characterized is chelerythrine , a benzophenanthridine alkaloid rather than a pseudosubstrate peptide like ZIP , and a general inhibitor of the catalytic domain of PKCs that strongly inhibits PKM forms [5] . We therefore examined the effect of chelerythrine on long-term memory retention of active place avoidance . On the first trial , rats entered the shock zone within seconds , but with training , the animals learned to avoid the shock zone for several minutes ( Figure 1 ) . Twenty-two hours later , chelerythrine or vehicle was injected into both hippocampi , and retention of the avoidance memory was tested 2 h after the injection , as previously described for ZIP [2] . Rats showed excellent retention following injections of the control solution , avoiding the shock zone for several minutes ( Figure 1 ) . However , after injections of chelerythrine , the rats once again rapidly entered the shock zone within seconds ( Figure 1 ) . Chelerythrine did not prevent acquiring or expressing short-term memory for active place avoidance . Immediately after the 24-h retention test , the shock was turned on for 10 min and then turned off [2] . Turning on the shock improved avoidance in the vehicle-injected rats even further . Thus , during the long-term memory testing , the animals spent only 7 . 9 ± 0 . 7% of their time in the shock zone , significantly less than 16 . 7% , the level of chance ( t3 = 12 . 0; p = 0 . 001 ) , and then after the single training session , they spent even less time in the shock zone ( 0 . 44 ± 0 . 44%; t3 = 37; p < 0 . 0001 compared to chance ) . During the long-term memory testing , the animals injected with chelerythrine showed no long-term memory retention , as expected , spending time in the shock zone at the level of chance ( 16 . 4 ± 1 . 1% , t4 = 0 . 3; p = 0 . 7 ) , but then these animals avoided the shock zone after the single training session , spending only 9 . 3 ± 1 . 1% of the session in the shock zone after the shock was turned off ( t4 = 6 . 5; p = 0 . 003 compared to chance ) . Thus , the action of chelerythrine on both long-term and short-term memory retention was indistinguishable from the action of ZIP [2] . In subsequent experiments , we used ZIP , the more specific of the two drugs , and controlled for nonspecific effects of the peptide in each of the conditioned behaviors with the scrambled , inactive version of ZIP ( scr-ZIP ) [2] . We then examined whether appetitively conditioned spatial information was maintained by the same mechanism as aversively conditioned spatial information in the DH by examining the effect of ZIP on conditioned behavior in the eight-arm radial maze . Rats learned the task , and after six trial blocks ( 3 d ) , performance was asymptotic and optimal for an additional six trial blocks ( days 4–6; Figure 2A–2C ) . On day 7 , bilateral DH injections of the control compounds , saline or scr-ZIP , did not alter performance during testing that began 2 h later . In contrast , injections of ZIP caused the number of correct choices to drop to the level of naive rats ( Figure 2A–2C ) . The deficit could not be attributed to an increase in working memory errors ( Figure 2B; p = 0 . 33 ) , but was due to a specific increase in spatial reference memory errors ( Figure 2C; p = 0 . 001 ) . To further characterize these reference memory errors , we examined the errors made by the ZIP-injected rats when there was only one correct choice remaining , i . e . , after three of the four baited arms had been chosen . Of 21 such errors , ten were to an arm adjacent to the correct arm , and six , three , and two were to arms that were two , three , or four arms away from the correct arm , respectively . After accounting for the fact that there was only a single arm four arms away from the correct arm , and two arms for the other categories , there was a significant effect of where the errors were distributed ( F3 , 28 = 3 . 1; p = 0 . 04 ) . Thus , ZIP impaired spatial reference memory , possibly by impairing spatial accuracy , but because the rats foraged appropriately on the maze and continued to use the win-shift strategy that requires working memory , the memory for the general contextual and procedural aspects of the task appeared unaffected . In the active place avoidance task , ZIP had a persistent effect on long-term memory retention [2] . To examine whether impairment in the radial arm maze was also persistent , 2 wk after the ZIP injection , the animals were reexamined with a single training trial . The rats that had been injected with saline 2 wk earlier showed excellent memory retention ( 96 . 7 ± 6 . 5% correct choices ) , whereas the rats that had been injected with ZIP made fewer correct choices ( 59 . 1 ± 6 . 7% ) , indicating they were still impaired ( t11 = 8 . 6; p < 0 . 0001 ) . We next examined whether spatial reference memory in the water maze is also maintained by PKMζ in the hippocampus . Rats learned the location of the escape platform during 5 d of training ( Figure 2D ) . After injections with the control solutions , saline or scr-ZIP , 2 h before the probe test on day 6 , the rats repeatedly crossed the platform location and concentrated their search in the correct quadrant ( Figure 2E–2G ) . In contrast , ZIP injections diminished the accuracy of searching . Although the ZIP-injected rats concentrated their swim time in the correct quadrant of the pool ( Figure 2E and 2G; p = 0 . 15 ) , they crossed the platform location fewer times than the rats injected with saline or the control compound ( Figure 2F and 2G; p = 0 . 03 ) . These differences were not due to changes in the total distance the rats swam ( saline [sal] = 15 . 2 ± 0 . 4 m; scr-ZIP = 16 . 3 ± 0 . 5 m; and ZIP = 16 . 3 ± 0 . 5 m; F2 , 21 = 1 . 56; p = 0 . 2 ) or the average swim speed calculated every 2 s ( sal = 25 . 3 ± 0 . 7 cm/s; scr-ZIP = 27 . 2 ± 0 . 8 cm/s; and ZIP = 27 . 2 ± 0 . 9 cm/s; F2 , 21 = 1 . 56; p = 0 . 2 ) . The amnesia induced by ZIP persisted , because on day 29 , the rats that had been injected with scr-ZIP 23 d earlier , took 18 . 4 ± 3 . 5 s to find the platform on the first trial , in contrast to the rats that had previously received ZIP injections , which took significantly longer , 43 . 4 ± 9 . 4 s ( n's = 4; t7 = 2 . 6; one-tailed p = 0 . 04 ) . These results indicate that PKMζ in DH maintains the precise spatial information that is needed for accurate localization , but not the global spatial information or the contextual information that is necessary for the spatial search strategy . We then tested whether persistent PKMζ activity in the hippocampus maintains conditioned-fear responses to context . ZIP injections into the DH 22 h after context/tone-shock pairing failed to alter contextual freezing tested 1 d later ( Figure 3A; p = 0 . 86 ) . Although the DH may have a role in some tone-fear paradigms [9 , 10] , its role in long-term storage of tone-fear associations is uncertain [10–12] . ZIP in the DH also did not impair tone-associated fear tested in a novel chamber 3 d after the infusions ( unpublished data ) . In additional experiments , decreasing the number of shocks from five to one , eliminating the tone during conditioning with a single shock , and bilaterally injecting into both dorsal and ventral hippocampi 1 d after conditioning failed to reveal an effect of ZIP on contextual fear ( sal , 86 ± 11% freezing; ZIP , 83 ± 9% freezing; F1 , 14 = 0 . 32; p = 0 . 9 ) . Together , these results in the DH indicate that PKMζ selectively maintains precise learned associations for locations , but not associations to imprecise spatial or background contextual stimuli . We therefore tested whether PKMζ maintains the fear-mediated CS-US associations thought to be stored in the BLA . Rats received a single tone-shock pairing trial and 22 h later were injected with saline , scr-ZIP , or ZIP . The saline- and scr-ZIP–treated rats expressed normal conditioned fear 2 h and 24 h after the injection , but the ZIP-injected rats showed impaired conditioned freezing at both retention delays . The results of the two retention delays were indistinguishable and therefore analyzed together . The effect of ZIP was different from that of the control solutions ( Figure 3B; p = 0 . 01 ) . In separate animals ( n = 8 for each group ) , the ZIP injections into the BLA 24 h after context/tone-shock pairing attenuated both contextual freezing tested 1 d later and tone-associated fear tested in a novel chamber 3 h after the context test ( freezing to context: sal , 82 ± 4 . 5%; ZIP , 48 ± 6 . 7%; F1 , 14 =18 . 2 , p < 0 . 001; freezing to tone: sal , 61 ± 12%; ZIP , 8 ± 3%; F1 , 14 = 20 . 3 , p < 0 . 001 ) . In parallel locomotion experiments , ZIP infusion did not induce hyperactivity measured by beam crossings during 1 h ( scr-ZIP , average = 146 . 4 ± 15 . 7 , n = 4; ZIP , average = 197 . 2 ± 35 . 8 , n = 5; F1 , 7 = 1 . 7 , p = 0 . 23 ) . Thus , in contrast to the DH , ZIP injection into the BLA impairs retention of conditioned-fear behavior . Ablation of the BLA as well as the adjacent central nucleus of the amygdala attenuates freezing to the shock itself [13–15] , which may confound the interpretation of whether information is stored in the BLA , or instead , whether the BLA is required for the expression of a fear association that is stored elsewhere . We therefore tested whether injecting ZIP into the BLA affected the expression of fear immediately after a shock . ZIP or saline was infused into the BLA 5 min or 2 h prior to testing immediate postshock freezing . ZIP did not affect immediate postshock freezing at either time point ( Figure 3C; data from both time points combined , p = 0 . 46 ) . Because ZIP in the BLA did not alter the ability to express fear , but attenuated conditioned fear , we conclude that persistent PKMζ activity in the BLA maintains the information that is required for fear associations , but not the function of the BLA in expressing fear . We then tested whether other forms of memory that depend on the BLA also require persistent PKMζ activity for maintenance . Injecting ZIP into the BLA 22 h after inhibitory avoidance conditioning impaired retention of the conditioned response that was tested 2 h later ( Figure 4; p < 0 . 01 ) . Two weeks later , the impairment persisted ( latency to enter the dark compartment: n's = 4; scr-ZIP = 297 ± 104 s; ZIP = 79 ± 50 s; t7 = 2 . 5; one-tailed p < 0 . 05 ) . Thus , long-term memories for both classically conditioned fear and instrumentally conditioned inhibitory avoidance depend upon persistent PKMζ activity in the BLA .
We find that site-specific inhibition of PKMζ impairs the retention of specific , accurate associations in multiple tasks in different brain regions , regardless of positive or negative reinforcement , and thus the persistence of PKMζ activity is a general molecular mechanism for the maintenance of memory . This mechanism is specific for sustaining accurate learned associations because inhibition of the kinase did not affect the expression of imprecise , contextual , and procedural information that depends upon the functioning of the brain regions in which the associations were stored . PKMζ inhibition thus contrasts with permanent or temporary lesions , which affect both types of information . Because ZIP specifically reverses information stored in synapses by late-LTP maintenance and does not affect baseline synaptic transmission in the hippocampus [2 , 5 , 16 , 17] , these results suggest that the physiological function of late-LTP–like plasticity may also be selectively important for storing specific accurate information . Although future work will be required to examine whether PKMζ maintains late LTP in the BLA as well the hippocampus , in all the tasks , ZIP produced a persistent loss of long-term memory , consistent with previous results of an effect on memory storage [2 , 3] , although the possibility of an as yet undiscovered role in information retrieval cannot be ruled out . In our study , we first replicated the main finding of Pastalkova et al . [2] , using a second inhibitor of PKMζ , chelerythrine . We found that the drug , the only other potent inhibitor of PKMζ activity known , produces the identical rapid impairment of long-term memory retention but sparing of short-term memory . Although chelerythrine affects all PKC isoforms at high doses [5] , ( 1 ) ZIP does not affect conventional and novel PKCs [5] , and inhibition of these other PKCs does not affect memory retention [2]; and ( 2 ) the common target of the two inhibitors is PKMζ , and both agents cause the same pattern of amnesia . The possibility that ZIP might inhibit another as yet unidentified protein kinase or some other process cannot be excluded [18]; however , such an effect would require the specific sequence of amino acids in ZIP that inhibits PKMζ activity , because the effect of the scrambled version of the peptide was indistinguishable from saline in all of the behaviors examined in this study . In the DH , persistent PKMζ activity was shown to specifically maintain memories for precise spatial locations , but not imprecise spatial , contextual , or procedural information . Thus , the same injections of ZIP resulted in the loss of information supporting accurate spatial reference memory in the eight-arm radial maze task ( Figure 2C ) , but no effect on the ability to use working memory to do the win-shift foraging procedure ( Figure 2B ) . Likewise , the effect of ZIP injection in the water maze task was the elimination of information supporting accurate spatial navigation ( Figure 2F ) , whereas information needed for the general place response to search in the platform quadrant of the pool was spared ( Figure 2E and 2G ) . Thus , the general recognition of the contextual and the procedural aspects of these tasks appeared unaffected by ZIP . Indeed , the equivalent ZIP injection in the DH did not impair context-associated fear at all ( Figure 3A ) , whereas injection of the inhibitor in the BLA disrupted the conditioned response . It is possible that regions of the hippocampus not affected by the ZIP injections contributed to the sparing of contextual aspects of memory [19]; however , we consider this unlikely because simultaneous ZIP injections in dorsal and ventral hippocampi also did not affect contextual fear . Thus , long-term information stored within the DH by PKMζ activity appears to be required for fine , accurate spatial reckoning or precise discrimination between related memories of location , as between the arms in the radial maze . These findings are consistent with the complete loss of the ability to perform active place avoidance on a rotating disk following PKMζ inhibition ( Figure 1 and [2] ) . In this task , the ability to discriminate between specific memories of shock locations with respect to the room and shock locations on the rotating disk is essential for avoiding the stationary shock zone [20 , 21] . The ability of ZIP to disrupt specific types of stored information in the DH while leaving other information intact may be due to its ability to specifically reverse late-LTP maintenance in the hippocampus , but not other forms of neural plasticity that can store information [22] . For example , PKMζ activity in the DH is not required for working memory in a radial arm maze , which appears to be mediated by transient early LTP [23 , 24] that is not maintained by PKMζ [16 , 17] . Long-term memories encoding coarser-grained spatial positions or context may be mediated by forms of long-term synaptic plasticity that might not be maintained by PKMζ , such as long-term depression [17 , 22] or perhaps , changes in neural excitability [25–27] . Alternatively , memories unaffected by ZIP in the DH may be stored elsewhere but require an intact hippocampus for processing or retrieving the information rather than storing it . Indeed , the specific effect of ZIP on fine , but not coarse-grained spatial information in the DH is consistent with the recent discovery that grid cells of the medial entorhinal cortex , which is the main input to the hippocampus , provide sufficient contextual and place information for spatial navigation based on distal landmarks [28–30] . Thus extrahippocampal regions appear to encode sufficient spatial information for the rat to recognize its environment and general location . Prior work with ablation or inactivation of the hippocampus would have interrupted the projection loops of this spatial information from the superficial layers of the entorhinal cortex through the subfields of the hippocampus and back out to the deep layers of the entorhinal cortex . In contrast , transmission of information through these circuits may not have been disrupted by PKMζ inhibition because ZIP has no effect on baseline synaptic transmission in hippocampal slices or in vivo [2 , 5 , 16 , 17] . In the BLA , both specific instrumentally conditioned associations for inhibitory avoidance and classically conditioned associations for fear were impaired by PKMζ inhibition . The impairment was specific to long-term memory because the PKMζ inhibition did not induce hyperlocomotion or disrupt the expression of fear to a recent shock , as observed , for example , with ablations of the BLA [13–15 , 31] . Thus , specific fear memories are maintained in the BLA by persistent PKMζ activity , which may be distinct from the BLA's role in modulating aversively motivated information stored elsewhere [32 , 33] . Lastly , the characterization of the forms of information stored in the brain by PKMζ may have clinical implications for disorders thought to be mediated by excessive memory retention or LTP-like plasticity , such as posttraumatic stress , phobias , and addictions . Previous studies have indicated that PKMζ inhibition by ZIP in the hippocampus and neocortex erases long-term memories encoded even weeks prior to injection [2 , 3]; therefore , further study will ultimately be required to identify a method by which PKMζ inhibition might target specific memories , perhaps by examining the role of PKMζ during reconsolidation after memory reactivation [34 , 35] . As an enabling first step , however , our current findings suggest that PKMζ inhibition disrupts the retention of specific , precise information stored in a brain region , but spares the region's processing functions such as relaying information or performing computations on information stored elsewhere . Thus , not all memories and functions previously ascribed to a brain region will be lost by site-specific PKMζ inhibition , but discrete pathophysiological associations induced by both fearful and rewarding experiences may .
Each rat was deeply anesthetized ( Nembutal >50 mg/kg intraperitoneally [i . p . ] or ketamine/xylazine mixture 100/10 mg/kg i . p . ) and mounted in a stereotaxic frame to drill bilateral holes in the skull for a pair of 22-ga infusion guide cannulae . The guide tips were at least 1 mm above the infusion target . Bone screws and dental cement secured the cannulae to the skull . Each rat was handled for at least 5 d to habituate it to the experimenter prior to training . Training began at least a week after surgery . The rats were habituated to the infusion procedure by mock saline infusions at least a day before the experimental solutions were infused . After the task was well learned , each rat received a bilateral intracranial infusion ( 1 μl/side ) of one of three experimental solutions: the myristoylated peptide PKMζ inhibitor ZIP ( 10 nmol/μl saline; QCB and University Wisconsin Biotech peptide synthesis facility ) , the control myristoylated peptide , scr-ZIP , comprising a scrambled sequence of the same amino acids as ZIP ( 10 nmol/μl saline; QCB ) , or saline [2] . Memory retention was tested 2 h after the infusion , unless stated otherwise . The infusion target in the DH was 3 . 8 mm posterior , 2 . 5 mm lateral , and 3 . 5 mm ventral to bregma . In the experiment to examine the effect of simultaneous PKMζ inhibition of both the dorsal and the ventral hippocampi , the dorsal coordinates were 2 . 5 mm posterior , 2 . 4 mm lateral , and 3 . 0 mm ventral to bregma , and the ventral coordinates were 5 . 6 mm posterior , 5 . 0 mm lateral , and 6 . 0 mm ventral to bregma . The coordinates for bilateral infusion into the BLA were 2 . 8 mm posterior , 4 . 8 mm lateral , and 5 . 8 mm ventral to bregma . After behavioral testing , the rats were sacrificed by anesthetic overdose , then transcardially perfused with saline followed by 10% formalin . The brains were removed , postfixed in 10% sucrose-10% formalin solution , sectioned , then stained with cresyl violet , and examined by light microscopy to estimate the injection site ( Figure 5 ) . Active place avoidance memory is a rapidly acquired form of spatial memory , the long-term retention of which is disrupted by bilateral hippocampal inactivation [36] . The first demonstrations that PKMζ maintains long-term memory used ZIP in DH to eliminate 1-d-old place avoidance memories [2] . We used the same training protocol to test whether chelerythrine , another PKMζ inhibitor , also eliminates long-term place avoidance memory . The place avoidance procedures have been described in detail [2 , 20] . Briefly , the rat is placed on an 82-cm–diameter metal disk that is elevated 78 cm from the floor and rotates at 1 rpm within a room with numerous visual landmarks off of the disk . Prior to training , the rat is implanted with a subcutaneous shock electrode between the shoulders , through which a constant current ( 0 . 3 mA , 60 Hz , 500 ms ) electrical foot shock is delivered whenever the rat enters an unmarked shock zone . The impedance between the shock electrode and the skin is approximately 1 , 000 times less than the impedance between the rat's feet and the metal disk , which is grounded , so the major voltage drop is across the feet . The shock zone is an unmarked 60° sector that is defined by distal visual landmarks in the room . The location of the rat is determined from an overhead television camera each 33 ms by a PC-controlled tracking system ( Bio-Signal Group ) . When the system detects the rat in the shock zone , the shock is delivered and repeated every 1 , 500 ms until the rat leaves the shock zone . Place avoidance training begins with a pretraining trial . The rat is placed on the rotating disk to explore the environment with the shock turned off for 10 min . After resting in the home cage for 10 min , the rat receives eight training trials with the shock turned on . There is a 10-min rest in the home cage between trials . Twenty-two hours after training , the rat was injected in both hippocampi with either chelerythrine ( n = 5; 10 nmol in 50% DMSO-saline ) or the vehicle ( n = 4 ) , and 2 h later , retention of the 24-h place avoidance memory was tested by returning the rat to the rotating disk with the shock off . The time to first enter 9th shock zone and the percent time in the shock zone estimated retention of memory . Immediately following the retention test for long-term memory , short-term memory was assessed . Short-term memory is established by turning on the shock for 10 min , and then retention is tested during a 10-min test period with the shock off . Spatial reference memory is distinguished from spatial working memory in the eight-arm radial maze because in reference memory , information about which arm locations are consistently baited is valid across trials , whereas working memory requires spatial information for which arm locations were visited within a trial , information that is only useful for the specific trial . We used the standard four-arms baited , four-arms unbaited task variant [37] . In this task , lesions of the hippocampus increase working memory errors , but not reference memory errors [37] . This basic result [38 , 39] contrasts , however , with many studies indicating that the hippocampus is critical for spatial reference memory in water maze tasks and other tests of spatial reference memory [40] . Detailed surgical methods are reported in [20 , 21] . The rats were food deprived to 85%–90% of their free-feeding weight prior to training on the eight-arm radial maze . The maze was 220 cm in diameter with a 60-cm–diameter central platform . Each arm was 16-cm wide and radiated 80 cm from the center . The maze was wiped with 70% ethanol between trials and rotated 90° every day to discourage the use of internal maze cues . The day before formal training began , each rat received two 10-min shaping trials with all arms baited by placing approximately 0 . 05 g of a sweetened oatmeal cereal mash ( Maypo; International Home Foods ) in the sunken food well at the end of each arm . Two rats were on the maze for the first shaping trial; and 1 h later , each rat received a second shaping trail by itself . On training trials , four arms were baited , and the food cups at the ends of the unbaited arms had inaccessible mash to control for odor cues . The locations of baited and unbaited arms were constant for a subject and balanced across subjects . There were ten training trials on each day . The rat was confined to the center of the maze by a large , overturned transparent bowl prior to each trial . Once released , the rat was free to forage until it consumed all the accessible food , or until 3 min had elapsed . Entry to an arm was scored when the rat crossed the halfway point of an arm . A trial was scored for correct entries , reference memory errors ( visits to unbaited arms ) , and working memory errors ( return visits to an arm ) [41] . Training continued for 6 d ( 60 trials ) to establish a strong memory . The next day , 24 h after training ceased , a single reinforced trial was given to test memory . Two hours prior to the memory test , each rat received a bilateral DH infusion of saline , scr-ZIP , or ZIP . We used a standard version of the water maze to assess spatial reference memory . The training protocol establishes a hippocampus-dependent memory , which can be demonstrated on day 6 by an inability to localize searching for the platform on a probe trial following hippocampal inactivation [20] . The consensus is that the DH is important for spatial reference memory in the water maze , but whether the DH is crucial for storing this spatial reference memory is controversial . Although tetanic stimulation to saturate potentiation of synaptic transmission , pharmacological blockade of N-methyl-D-aspartate receptors , and permanent and functional lesions of the DH all impair learning and memory of the escape location [20 , 42 , 43] , the impairment is absent in rats that had learned the water maze procedure , but not the particular escape location , prior to the amnestic intervention [44–47] . Detailed methods were reported [20] . The rats were trained in a 1 . 83-m diameter circular pool filled with 40-cm deep 21–22 °C opaque water . The rats were trained to find a circular , 10-cm–diameter clear Perspex platform that was submerged 1 cm below the water surface halfway between the center of the pool and the wall along the 0° radius . A rat was released in pseudorandom order from one of four equally separated locations along the wall . Each release location was used once in a four-trial training block . The path of the rat was automatically tracked with an overhead television camera ( iTrack; Bio-Signal Group ) . Software ( TrackAnalysis and TrackExplorer; Bio-Signal Group ) calculated the latency to escape onto the platform , the time spent in each quadrant centered at 0° , 90° , 180° , and 270° , the time in each 11 . 5-cm–square region of the pool , and each 2 s , the rat's swim speed . Acquisition training lasted 5 d ( two blocks/day ) . On day 6 , 22 h after the last training , the rats were injected with saline , scr-ZIP , or ZIP , and 2 h after that , retention of the long-term place memory was tested by a probe trial with the escape platform removed . On this probe , the rat was placed in the center of the pool , and where it spent its time was measured for 1 min . Contextual fear . Lesions of the DH disrupt the general , contextual component of the learned fear response in contextual conditioning paradigms . We conditioned rats in a standard , combined context and tone-conditioned fear protocol in which the rat received five shocks [9] . Following this training , long-term retention of contextual , but not tone fear is impaired by posttraining DH lesions [9 , 48] . We also examined two other context-conditioning protocols in which only a single shock was administered . In one protocol , the shock was paired with a tone; in the other , the tone was not presented . The procedures have been described in detail [49] . Aluminum and Plexiglas conditioning chambers housed in sound-attenuating cabinets were used . The floor of each chamber was made of parallel rods that could deliver pole-scrambled , constant current foot shock ( 1 . 0 mA , 2 s ) . The chambers were contextually distinct . “Context A” ( used for conditioning and context retention testing ) , had working ventilation fans that produced background noise ( 65 dB ) . The chamber lights and room lights illuminated the space because the sound-attenuating chest doors were open . The chambers were cleaned with a 1% ammonium hydroxide solution , which covered the surface under the shock floor . The chest doors were closed for “Context B” ( used for tone retention testing ) , and fluorescent red light provided illumination . The ventilation fans were inactive , and the chambers were cleaned with a 1% acetic acid solution , which covered the pan below the shock floor . Movement in each chamber was monitored using load cell inputs to a 5-Hz analog-to-digital converter calibrated to activity values between 0 and 100 . Freezing behavior was automatically detected as inactivity ( activity value <10 ) during at least 1 s ( Threshold Activity software; Med-Associates ) . Each rat was placed in a conditioning chamber for training , and after 3 min , five tone ( 2 kHz , 80 dB , 10 s ) shock ( 1 . 0 mA , 2 s ) pairings were delivered ( 70-s intertrial interval ) . The tone coterminated with the shock . Twenty-two hours after training , both dorsal hippocampi were infused with the saline , scr-ZIP , or ZIP solutions . Twenty-six hours after the infusions , long-term retention of contextual fear memory was assessed by measuring freezing behavior during a 10-min extinction session in Context A . Seventy-four hours after the infusions , long-term memory for the tone-shock association was assessed by measuring freezing during an extinction test in the novel Context B . In this test , an 8-min continuous tone was presented 2 min after a rat was placed in the chamber . Tone fear . Specific associations between cues and fear are formed in the BLA [11] . We used a standard tone-fear conditioning protocol , in which a single tone cue is paired with shock . Detailed surgical procedures were described [50] . Training and testing sessions were conducted in two contextually distinct , aluminum and Plexiglas conditioning chambers , similar to the ones used for contextual fear conditioning . Freezing was automatically measured for 2 s of every 5 s for 10 min ( Med PC version 4; Med Associates ) . After 3 d of habituation to the two contexts , the rats were placed in Chamber A , and after 4 min , a 30-s , 90-dB , 5-kHz tone was played . The tone coterminated with a 1 . 5-mA , 1-s foot shock . Thirty seconds later , the rat was returned to its home cage . One hour after exposure to shock in Chamber A , the rat was placed in Chamber B for 5 min without the tone or shock . Twenty-two hours after training , the rats were infused with saline , scr-ZIP , or ZIP . Two or 24 hours later , long-term memory retention was tested by placing the rat in Chamber B , exposing the rats to the tone , and then measuring freezing in response to the tone . Six weeks after tone-shock conditioning , immediate postshock freezing was assessed in a counter-balanced subset of rats ( n = 18 ) . ZIP or saline was infused into both BLA sites , 5 min or 2 h prior to testing . An animal was then placed in Chamber A , and after 4 . 5 min , it was shocked once ( 1 . 5 mA , 1 s ) . Freezing behavior was scored during the subsequent 10 min . The BLA is important for inhibitory avoidance , because it modulates the strength of information as it is stored at extra-amygdala sites during a posttraining consolidation window lasting several hours [32 , 33] . Whether the BLA also stores associations for maintaining inhibitory avoidance beyond this time window , however , is controversial . We used a standard inhibitory avoidance protocol in which the rat is shocked once when it enters from the brightly lit side to the dark compartment of the conditioning environment [51] . Experiments were performed as previously described [51] . Briefly , the inhibitory avoidance training apparatus consisted of a rectangular box comprising two compartments , a safe ( brightly lit ) one and a shock ( dark ) one , separated by a vertically sliding door ( Med Associates ) . During training , each rat was placed in the safe compartment with its head facing away from the door . After 10 s , the door automatically opened , allowing access to the shock compartment , and latency to enter was taken as a measure of inhibitory avoidance acquisition . The door closed after the rat completely entered the shock compartment , and 2 s later , a brief foot shock ( 0 . 9 mA , 2 s ) was delivered . The rat was then removed from the apparatus and returned to its home cage . Twenty-two hours after training , the rats were infused with saline , scr-ZIP , or ZIP . Long-term inhibitory avoidance memory was then tested 2 h later , by placing the rat back in the safe compartment and measuring the latency to enter the shock compartment . A foot shock was not delivered during the retention test . For animals that did not enter the shock compartment , the test was terminated at 9 min . Group comparisons were made by ANOVA . Significance was accepted for p < 0 . 05 . When appropriate , Newman-Keuls post hoc tests were performed . Student t-tests were used to compare group performance during retention of place avoidance against the chance value . | How long-term memories are stored as physical traces in the brain is a fundamental question in neuroscience . Recently , we discovered the first molecular mechanism of long-term memory storage . We showed that unpleasant memories are stored by the persistent action of an enzyme , a form of protein kinase C , termed PKMζ , because these memories can be rapidly erased by injecting a PKMζ inhibitor into the brain . But are all forms of memory and information in the brain stored by PKMζ ? Here , we first confirmed with a second inhibitor of PKMζ that unpleasant long-term memories in the hippocampus , a region of the brain critical for storing spatial information , are rapidly erased . We then examined other memories stored in the hippocampus and the basolateral amygdala , another region critical for emotional memories . We tested memories for specific places , both unpleasant and rewarding , memories for general background information , associations between a sound and a fearful event , like that studied by Pavlov , and memories for performing a specific action . We found that PKMζ stores specific associations , both unpleasant and rewarding , for places , events , and actions , and is thus a general mechanism for memory storage in the brain . | [
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In human studies , how averaged activation in a brain region relates to human behavior has been extensively investigated . This approach has led to the finding that positive and negative facial preferences are represented by different brain regions . However , using a functional magnetic resonance imaging ( fMRI ) decoded neurofeedback ( DecNef ) method , we found that different patterns of neural activations within the cingulate cortex ( CC ) play roles in representing opposite directions of facial preference . In the present study , while neutrally preferred faces were presented , multi-voxel activation patterns in the CC that corresponded to higher ( or lower ) preference were repeatedly induced by fMRI DecNef . As a result , previously neutrally preferred faces became more ( or less ) preferred . We conclude that a different activation pattern in the CC , rather than averaged activation in a different area , represents and suffices to determine positive or negative facial preference . This new approach may reveal the importance of an activation pattern within a brain region in many cognitive functions .
A traditional approach in human studies is to examine how averaged activation in a brain region relates to behavior . Results obtained by this approach led most theories of cognitive functions in the human brain to assume that a different region or a group of regions in the human brain play a role in a different function . Although this approach has greatly advanced the understanding of neural mechanisms of human cognitive functions , it cannot effectively reveal a differential role of a pattern of activity within the same region in a different cognitive function . In animal studies , the importance of a role of activity of a certain group of neurons in a region rather than mean activity of the region has been observed [1 , 2] . Thus , to better understand neural mechanisms of human cognitive functions , it is necessary to investigate how a different pattern of activation within a region plays a different role . Facial preferences influence a wide range of social outcomes from face perception to social behavior [3–13] and , therefore , has been a subject of great interest . Theories of facial preferences have also been developed , with the general consensus that positive and negative facial preferences are represented by different brain regions including the amygdala , basal ganglia , insular cortex , occipitotemporal cortex , orbit frontal cortex , lateral prefrontal cortex , and cingulate cortex ( CC ) [14–20] . The CC has also been reported to play roles in preference to different categories including faces and daily items [21–23] . A recently developed online functional magnetic resonance imaging ( fMRI ) decoded neurofeedback ( DecNef ) has allowed us to induce a different multi-voxel pattern of activation within the same brain region [24] . In the present study , using fMRI DecNef , we tested whether a different pattern of activations within a single brain region can sufficiently change facial preferences in a positive or negative direction . In the experiment , we chose the CC as the target brain region for fMRI DecNef because the CC was found to be the best region whose multi-voxel activation patterns represent both positive and negative facial preferences in the current study ( see below ) among the regions previously implicated in facial preference [14–20] . We tested whether subjects’ preferences to neutrally preferred faces could be changed toward a positive ( or negative ) direction by fMRI DecNef , which induced multi-voxel activation patterns in the CC that correspond to higher ( or lower ) preference with presentations of the neutrally rated faces to generate a new association between the faces and manipulated preferences . As a result , the previously neutrally rated faces became significantly more ( or less ) preferred . Although subjects’ facial preferences were successfully changed , subjects remained unaware of the aim to change their facial preferences . On the contrary to the previous belief that a different brain region plays a role in positive or negative facial preference , our results are in accord with the hypothesis that it is a different activation pattern in the CC that represents and suffices to determine positive or negative facial preference .
To determine a single region that would be used as a target for fMRI DecNef , we first conducted a pilot experiment ( see Fig 1A , S1 Fig , and Pilot Experiment in Materials and Methods ) . Results of the pilot experiment showed that the CC most accurately reflects subjects’ behavioral preference ratings both in the negative and positive directions among the regions previously implicated in facial preference ( S1A Fig ) [14–20] . Thus , we determined the CC as the target region for fMRI DecNef in the main experiment . This experimental design was made with the aim to test whether induction of activation patterns in the CC , which represent higher ( or lower ) preference with presentations of neutrally preferred faces , can make these faces more ( or less ) preferred . The main experiment consisted of five stages ( see Fig 1A and Main Experiment in Materials and Methods for details ) : pre-test ( 1 d ) , fMRI decoder construction ( 1 d ) , induction ( fMRI DecNef , 3 d ) , post-test ( 20 min after the offset of the induction stage ) , and interview of subjects ( immediately after the post-test stage ) . In the pre-test stage , we measured a distribution of behavioral preference ratings to faces for each subject . In the fMRI decoder construction stage , for each subject , we constructed a preference decoder to estimate the preference ratings , which were represented by the activation patterns in the target region ( the CC ) . In the induction stage , fMRI DecNef was administered based on the preference decoder . Through fMRI DecNef , activation patterns in the CC were made to be similar to specific patterns , which represent higher ( or lower ) preference ratings , in association with presentations of neutrally rated faces . In the post-test stage , subjects’ behavioral preference ratings to the same faces as in the pre-test stage were measured so that we could test whether behavioral preference ratings to the previously neutrally rated faces were changed due to induction of the specific activation patterns in the CC . In the pre-test stage , subjects’ behavioral preference ratings to 400 face pictures were measured . In each trial ( Fig 1B ) , after the brief presentation of each face , subjects determined their preference to the face on a scale of one to ten ( one for the lowest , ten for the highest ) during a 6 s rating period . In a subsequent reporting period , they were asked to report the determined preference rating . Based on the behavioral preference ratings recorded in the pre-test stage , we selected neutrally rated faces , which would be used in the subsequent stages . For each subject , one set of 15 neutrally rated faces was randomly selected for use in the induction stage and called “induction faces . ” Another set of 15 neutrally rated faces was also randomly selected as “baseline faces” for a control set , which was not shown during the induction stage . Because both induction and baseline faces were neutrally rated in the pre-test stage , the average behavioral ratings were the same between the two sets of faces in the pre-test stage . Thus , comparison of changes in the behavioral preference ratings between the induction and baseline faces in the post-test stage would indicate whether pairings of specific activation patterns in the CC with the induction faces during the induction stage are sufficient to change the behavioral preference ratings to the induction faces . In the fMRI decoder construction stage , we constructed the preference decoder ( sparse linear regression [25] ) , which would be used during the subsequent induction stage for the target region ( the CC ) . In the fMRI decoder construction stage , subjects again conducted the preference-rating task in the fMRI scanner ( see Fig 1B and Main Experiment in Materials and Methods ) . Based on the fMRI signals measured during the rating period and corresponding behavioral preference ratings for each subject , we constructed the preference decoder to estimate the subject’s behavioral preference ratings from activation patterns in the CC . The purpose of the three-day induction stage was to associate the induction faces with specific activation patterns in the CC that represent higher ( or lower ) preference ratings through fMRI DecNef using the preference decoder . Subjects were randomly assigned to either a higher-preference ( n = 12 ) or lower-preference ( n = 12 ) group but were not informed of their assigned group . Each trial consisted of face , induction , fixation , feedback , and inter-trial periods ( Fig 1C ) . During the face period , subjects were presented with one of the induction faces . In the induction period , subjects were instructed to somehow regulate their brain activity to make the size of a solid green disk ( presented in the subsequent feedback period ) as large as possible . Subjects were encouraged to enlarge the disk size so that they would receive a payment bonus proportional to the mean disk size . Subjects were given no further instructions . The size of the disk presented in the feedback period served as a feedback signal and reflected an estimated preference rating from the CC , which was calculated by applying the preference decoder to the activation pattern of the CC obtained in the preceding induction period of the trial ( see Main Experiment in Materials and Methods for details ) . However , the computation of the disk size was opposite in its direction between the two groups , although the instructions given to the two groups were exactly the same . For the higher-preference group , the disk size was proportional to the estimated rating from the CC activation pattern . That is , if the CC activation became more similar to the patterns corresponding to higher preference , the disk size became larger . In contrast , for the lower-preference group , a lower estimated rating made the disk larger . This made the instruction and the range of feedback signals to both groups identical . Note that all other information , including the intended preference direction , the purpose of the induction stage , and the meaning of the disk size , was withheld from subjects so that knowledge of the purpose of the experiment would not influence subjects’ rating criteria in the post-test stage . To confirm that subjects’ behavioral preference ratings to the induction faces changed as a result of associations of the induction faces with CC activation patterns of higher ( higher-preference group ) or lower ( lower-preference group ) preference ratings during the induction stage , the following three criteria have to be satisfied . First , subjects’ preference ratings as behavioral measures for originally neutrally rated induction faces must be significantly higher with the higher-preference group and lower with the lower-preference group in the post-test stage than in the pre-test stage . Second , to rule out the effect of mere exposure to faces on preferences to the faces [26] , the subjects’ behavioral preference ratings must be unchanged simply by repeated exposures to the faces during the fMRI decoder construction and induction stages . Thus , a new group of subjects as a control group ( n = 6 ) underwent an experiment in which visual presentations were identical to those for the higher- and lower-preference groups while no fMRI DecNef was administered ( see Main Experiment in Materials and Methods for details ) . Third , changes in subjects’ behavioral preference ratings after the induction stage must occur specifically for the induction faces but not for the baseline faces , which were originally neutrally rated but were not used during the induction stage . To test if the results of the main experiment met these criteria , a three-way mixed-model ANOVA with factors being test stage ( pre- versus post-test stages ) , face type ( induction versus baseline faces ) , and group ( higher-preference , lower-preference versus control groups ) was applied to the behavioral preference ratings ( Fig 2 ) . The main effects of test stage ( F1 , 27 = 4 . 61 , p = 0 . 04 ) and group ( F2 , 27 = 3 . 56 , p = 0 . 04 ) were significant . Significant interactions were obtained between test stage and group ( F2 , 27 = 8 . 31 , p < 10−2 ) , between face type and group ( F2 , 27 = 10 . 84 , p < 10−3 ) , between test stage and face type ( F1 , 27 = 4 . 63 , p = 0 . 04 ) , and among the three factors ( F2 , 27 = 13 . 10 , p = 10−4 ) . Post hoc t-tests revealed that , in the post-test stage , subjects’ behavioral preference ratings to the induction faces were significantly higher for the higher-preference group ( Fig 2 , red; paired two-tailed t-test , t11 = 4 . 78 , p < 10−3; Bonferroni corrected ) and significantly lower for the lower-preference group ( Fig 2 , blue; t11 = 3 . 31 , p < 10−2 , Bonferroni corrected ) than in the pre-test stage . The results meet the first criterion . Moreover , post hoc t-tests showed no significant changes in subjects’ behavioral preference ratings between the two test stages for the control group ( Fig 2 , gray; t5 = 0 . 69 , p = 0 . 52 ) , meeting the second criterion . For all of the three groups , no significant change in subjects’ behavioral preference ratings was observed for the baseline faces , which were neutrally rated in the pre-test stage but not presented during the induction stage , meeting the third criterion ( Fig 2 , baseline faces; t11 = 1 . 15 , p = 0 . 27 for the higher-preference group; t11 = 0 . 45 , p = 0 . 66 for the lower-preference group; t5 = 0 . 72 , p = 0 . 50 for the control group ) . From all of these results , we conclude that association of originally neutrally rated faces with covert induction of activity patterns in the single brain region , the CC , led to changes in facial preference specifically for those faces and in a specific preference ( positive or negative ) direction . It is important that subjects were unaware of our manipulation and what the disk size represented , because knowledge or suspicion of what the disk size represented could have significantly influenced subjects’ rating criteria . In the interview stage , which was held right after the post-test stage , subjects from the higher- and lower-preference groups were asked whether they knew or suspected what the disk size represented and what , if anything , they tried to do to increase the disk size . None of their responses indicated even a slightest understanding of the true workings of the experiment ( see S1 Data for details ) . Subjects were then debriefed on how the disk size was computed and were asked to guess whether they had been assigned to the higher- or lower-preference group . The accuracies of their guesses were indistinguishable from chance for the higher-preference ( Chi-square test , χ = 0 . 17 , p = 0 . 68 ) and lower-preference ( χ = 0 . 00 , p = 1 . 00 ) groups ( Fig 3 ) . These results of the interview stage suggest that subjects remained unaware of what the disk size represented . That is , it was beyond subjects’ will that induction of specific activation patterns in the single region changed facial preference in a specific ( positive or negative ) direction . The present study tested whether subjects’ behavioral preference ratings to the neutrally rated faces can be altered by repetitive inductions of a certain pattern of activation within the same region . We found that the behavioral preference ratings to the neutrally rated faces were indeed changed in the post-test stage from the pre-test stage ( Fig 2 ) . If activation patterns in the CC during the induction stage resulted in the changes in the behavioral preference ratings , there should be a quantitative relationship between the induced shifts in activation patterns in the CC during the induction stage and the observed changes in subjects’ behavioral preference ratings in the post-test stage . To examine this relationship , we first quantified the activation patterns in the CC during the induction stage as described in ( 1 ) , and called the metric induced CC-activation shifts . Then , we tested the prediction that the degree of the induced CC-activation shift should be correlated with the degree of the behavioral preference rating change as described in ( 2 ) . We found significant bi-directional changes in the subjects’ behavioral preference ratings to the induction faces in the post-test stage from the pre-test stage ( Fig 2 ) . In addition , the results of the quantitative analysis ( Fig 4 and S2 Fig ) suggested that a different activation pattern induced in the CC during the induction stage determined a direction and degree of the changes in subjects’ behavioral preference ratings to the induction faces . However , is there any possibility that pairings of the induction faces with the monetary reward during the induction stage directly led to the subjects’ behavioral preference rating changes in the post-test stage without relying on the differential activation patterns induced in the CC ? This possibility arises because payment bonus ( monetary reward ) was given to subjects in proportion to the size of the green feedback disk during the induction stage . However , the following lines of evidence do not support this possibility . First , the lack of difference in the amounts of monetary reward between the higher- and lower-preference groups is inconsistent with the model that the amount of reward should simply increase the preference . Assume that monetary reward was a direct deterministic factor to make faces more preferred . Then the amount of the payment bonus given to the higher-preference group should have been larger than the lower-preference group , because the behavioral preference rating increased in the higher-preference group and decreased in the lower-preference group ( Fig 2 ) . However , there was no significant difference in the amounts of the payment bonus between the higher- and lower-preference groups ( Fig 5A; two-sample two-tailed t-test , t22 = 0 . 19 , p = 0 . 85 ) . Second , if monetary reward was a direct deterministic factor to make faces more preferred , the amount of the payment bonus should have been correlated with the degree of the behavioral preference rating change across the two groups . However , no significant correlation was found between them ( Fig 5B; r22 = 0 . 15 , p = 0 . 49 ) . Third , if hypothesis for the direct role of reward is true , then the amount of the payment bonus should have been positively correlated with the estimated preference rating by the decoder from the CC activation patterns in each of the higher- and lower-preference groups . Indeed , the estimated rating from the CC activation pattern was positively correlated with the amount of the payment bonus in the higher-preference group ( Fig 5C; red diamonds ) . However , the estimated rating was negatively correlated with the amount of payment bonus in the lower-preference group ( Fig 5C; blue circle ) . These correlations reflect the experimental procedure for the induction stage ( see Main Experiment in Materials and Methods ) . These lines of evidence clearly deny the possibility that monetary reward during the induction stage was the direct factor to induce both positive and negative preferences . They also support the conclusion that it is the differential activation patterns induced in the CC that led to the bi-directional changes in the behavioral preference ratings . The present results suggest that the different activation patterns in the CC resulted in different directions of facial preference . How can we know that the CC mainly determines the facial preference ? The size of the feedback disk provided to subjects in the induction stage was based on activation patterns only in the CC . However , this procedure alone does not assure that inductions of the preference-related activation patterns were confined to the CC . It is possible that , in concert with the successful induction of specific activation patterns in the CC , similar activation patterns occurred in some other regions during the induction stage , which might also contribute to the behavioral preference rating changes . If the activation patterns in the CC that represented higher ( or lower ) preference ratings “leaked out” and induced similar preference-related activation patterns in other regions , the activation patterns in these regions should reconstruct the estimated ratings based on the CC activation patterns on a trial-by-trial basis . To test the possibility that preference-related activation patterns in the CC leaked out of the CC to other regions , we conducted two leak analyses . In the first analysis ( see Leak analysis using an ROI-based method for details ) , we anatomically divided the whole brain into a total of 38 regions ( the CC and 37 other regions ) and compared the amounts of leakage in the 38 regions . In the second analysis ( see Leak analysis using a searchlight method for details ) , using a searchlight method [27] , we mapped the amount of leakage by moving a searchlight sphere throughout the whole brain during the fMRI decoder construction stage and induction stage and compared these maps . We describe details of these two leak analyses below . In the first leak analysis , we tested whether the estimated ratings based on the CC activation patterns were reconstructed using activation patterns measured in each of the aforementioned 37 other brain regions during the induction stage , as well as activation patterns in the CC itself as a control . We defined reconstruction performance as a correlation coefficient between the reconstructed values and the estimated ratings based on the CC activation patterns ( see Leak analysis using an ROI-based method in Materials and Methods for details ) . A high correlation coefficient would indicate that there was leakage of the CC activation patterns to the other brain regions . However , the Fisher-transformed correlation coefficients for the other 37 regions ( Fig 6 , gray ) were significantly and markedly smaller than that for the CC itself ( Fig 6 , red; paired two-tailed t-test on z-scores of the correlation coefficients after permutation [28] , t23 > 13 . 26 , p < 10−11 , Bonferroni corrected; see Permutation Test in Materials and Methods for details of calculation of the z-scores ) . If activation patterns in other brain regions had been closely linked to those in the CC , the correlation coefficients should have been as high as those in the CC . Therefore , our results indicate that it is unlikely that the activation patterns induced in the CC leaked out to other regions . In the second leak analysis , using a searchlight method ( see Leak analysis using a searchlight method for details ) , we examined how well activation patterns of voxels in a moving searchlight sphere can reconstruct the estimated ratings in the CC ( 1 ) while subjects were conducting the preference-rating task in the fMRI decoder construction stage and ( 2 ) while they were going through the induction stage . A comparison of the results between the fMRI decoder construction stage and induction stage should clarify that preference-related activation patterns were confined to the CC during the induction stage . During the fMRI decoder construction stage , the CC and several other regions showed significant and high reconstruction performances on the reconstruction of the estimated ratings based on activation patterns in the CC ( Fig 7A ) . This result demonstrated that the searchlight method possesses a sufficient power of sensitivity for detecting leaking of preference-related activation patterns out of the CC to other regions . However , during the induction stage , significant and high reconstruction performances were found only for voxels within the CC ( Fig 7B ) . Although a few regions in the prefrontal and parietal cortices showed significant reconstruction performance in reconstruction , these performances were much lower than those in the voxels within the CC . These results obtained by the searchlight analysis indicate the following two points . First , when subjects conducted the preference-rating task in the fMRI decoder construction stage , preference-related activation patterns in the CC leaked out of the CC to several other regions . Second , during the induction stage , there was little leakage of preference-related activation patterns out of the CC to other regions . These two leak analyses collectively and consistently demonstrate that the preference-related activation patterns were indeed largely confined to the CC during the induction stage . We conclude that the behavioral rating changes due to fMRI DecNef ware mainly induced by activation patterns within the CC .
To our knowledge , this is the first study in which manipulation of a different brain activation pattern in a single region successfully changed facial preferences in the positive or negative direction . Previous neuroscientific approaches have revealed that a different region or a group of regions is involved in positive or negative facial preference [14–20] and developed the general consensus that positive and negative facial preferences are represented by different brain regions . Contrary to this general consensus , the present results using fMRI DecNef [24] indicated a case in which highly selective activity patterns in the single brain region represent and suffice to determine both positive and negative facial preferences . Additionally , our control analyses provide two important insights . First , the facial preference was not necessarily increased by a larger amount of monetary reward . Especially in the lower-preference group , the higher the monetary reward was , the less liked faces were . Second , when the CC was solely manipulated by fMRI DecNef during the induction stage , the effect of the manipulation on other brain regions was minimal or negligible . Together , the present results suggest that the induced activation pattern within the CC played a crucial role in changing the facial preference . Based on the abovementioned results , we argue that a different activation pattern mainly in the CC as a single region represents positive or negative facial preference . However , one may raise the possibility that the activation patterns for positive and negative facial preference are spatially distributed in different subregions of the CC . We tested whether this is the case . We found that there is no significant difference in the extent of spatial distributions in the CC between positive and negative facial preferences ( see S3 Fig for details ) . Thus , it is unlikely that a different subregion of the CC represents each of the positive and negative facial preferences . These results are in accord with the hypothesis that positive and negative facial preferences are represented by spatially overlapped but different activation patterns in the CC . Are changed activations in the CC during the induction period specific to facial preference ? If so , activation changes during the induction period should be more related to the facial preference than any other period of a trial . To test this possibility , we analyzed activation patterns in the CC during the fixation period as well as the inter-trial period of the induction stage and compared them with activation patterns in the induction period ( see Fig 1C for time course of a trial in the induction stage ) . In particular , we applied the preference decoder to the activation patterns during the fixation as well as inter-trial periods and calculated the induced CC-activation shifts as in the induction period ( S4 Fig ) . During the induction period , the induced CC-activation shifts for the higher- and lower-preference groups were significantly different from zero ( S4A Fig ) . In contrast , during the fixation period ( S4B Fig ) or inter-trial period ( S4C Fig ) , the induced CC-activation shifts were not significantly different from zero . These results are consistent with the possibility that changed activations in the CC during the induction period were specific to facial preference rather than related to other functions or states including general emotion . One may also wonder whether the significant shifts in induced CC-activations during the induction period ( Fig 4 and S4A Fig ) were simply due to changes in the overall mean activation amplitudes in the CC rather than induced multi-voxel activation patterns associated with higher or lower facial preferences . To test this possibility , we calculated the overall mean amplitudes across voxels in the CC during the induction period for both the higher- and lower-preference groups over the three-day induction stage ( see S5 Fig for details ) and compared the mean amplitudes between the two groups . There was no significant difference in the mean amplitudes between the groups . We also found that in neither group the overall mean activation amplitude was significantly different from zero on any day of the induction stage . These results suggest that the behavioral rating changes toward higher- and lower-preference resulted from induction of certain activation patterns in the CC rather than changes in the overall mean activation amplitudes in the CC . It has been found that a number of brain regions are involved in facial preferences [14–20] . Why was the CC in particular selected as the target region for fMRI DecNef to change subjects’ facial preferences ? An important purpose of the current study was to test whether a different pattern of activity in a single brain region changes facial preference in an opposite direction . In the aforementioned pilot experiment , we found that the CC is the best region that codes both positive and negative facial preferences depending on the pattern of activation within the CC . Although the prefrontal cortex plays an important role in facial preferences in studies using a univariate analysis , it has been found that a different part of the prefrontal cortex is involved in each of the positive and negative facial preference coding [18] . Thus , the criterion for the selection of the CC was not based on whether the CC is the most important region for facial preference but rather was based on the fact that the CC was found to code both positive and negative facial preferences in the current study . In summary , the results of the present study indicate that highly selective activity patterns for higher or lower preference within the CC that were repeatedly paired with facial stimuli lead to changes in subjects’ facial preferences . Although these results do not deny important roles of other regions in facial preference , our study clearly demonstrates that inductions of different patterns of activation within the CC suffice to determine changes of facial preference in opposite directions beyond subjects’ will [29] .
Thirty-three naïve subjects ( 19 to 29 years old; 23 males and 10 females ) with normal or corrected-to-normal vision participated in the study . All experiments and data analyses were conducted in ATR . All subjects gave written informed consents . The purpose of the pilot experiment was to determine a target region of interest ( ROI ) to be used in the main experiment . Three subjects participated in the pilot experiment . The complete experiment consisted of two stages: pre-test ( 1 d ) and fMRI decoder construction ( 1 d ) . The two stages were separated by at least 24 h . Only behavioral data were collected from the pre-test stage , during which subjects’ behavioral preference ratings to face pictures were measured . Subjects performed a preference-rating task ( Fig 1B ) for a total of 400 trials across 20 runs . During each run , subjects were asked to fixate on a white bull’s-eye presented at the center of the display . A brief break period was provided after each run upon a subject’s request . We used a pool of 400 face pictures ( 200 males , 200 females , of a variety of races and ages ) collected from several open databases [30–35] . A stimulus primarily consisted of a face and usually included some body parts , including hair , a neck , and shoulders , as well as a background scene . Each face picture was 4° square in size . The order of presentation of faces was randomized for each subject . Each trial ( Fig 1B ) consisted of a face period ( 0 . 5 s ) , a rating period ( 6 s ) , and a reporting period , in the respective order . During the face period , a face picture was presented for 0 . 5 s at the center of the display . During the rating period , only a fixation point was presented at the center . Subjects were instructed to rate their preference to the previously presented face on a ten-point scale ( one for the lowest preference , ten for the highest preference ) . During the reporting period , subjects were asked to report the preference rating by pressing two buttons ( left and right ) on a keyboard using the index and middle fingers of their right hand . At the beginning of the reporting period , a random number from one to ten was selected and presented at the center of the display . Subjects were asked to adjust its value to their preference rating , pressing the left button to increment it . Values were “wrapped” so that when subjects attempted to increment past a value of ten , values would start over at one . Subjects completed the reporting period by pressing the right button . After completion of the reporting period , the next trial began . For each subject , 240 of the 400 face pictures were selected to be used for the subsequent fMRI decoder construction stage . Selections for pictures were based on subjects’ individual behavioral preference ratings in the pre-test stage: 100 highest-rated faces , 100 lowest-rated faces , and 40 neutrally rated faces . In the fMRI decoder construction stage , we measured subjects’ blood-oxygen-level dependent ( BOLD ) signal patterns ( see MRI Measurements and Parameters ) while they once again conducted the preference-rating task on the 240 face pictures selected from the pre-test stage ( the 100 highest-rated faces , 100 lowest-rated faces , and 40 neutrally rated faces ) . The measured BOLD signal patterns and behavioral preference ratings were in turn used to compute parameters for a preference decoder for each of the different ROIs ( see below ) . Task procedures were identical to those of the pre-test stage except that an inter-trial period was added to the end of each trial , in which only a white fixation point was presented at the center of the display ( Fig 1B ) . Subjects were asked to report their ratings within the reporting period ( maximum of 5 . 5 s ) . The duration of the inter-trial period varied across trials , depending on subjects’ reporting time , so that the total duration of the reporting and inter-trial periods would be equal to 5 . 5 s . Each fMRI run for the fMRI decoder construction stage consisted of 20 task trials ( one trial = 12 s ) plus a 10 s fixation period before the trials and a 2 s fixation period after the trials ( one run = 252 s ) . The fMRI data for the initial 10 s were discarded to allow the longitudinal magnetization to reach equilibrium . Subjects conducted a total of 240 trials in 12 fMRI runs . A presentation order for the abovementioned 240 face pictures was randomized for each subject . Throughout each fMRI run , subjects were asked to fixate on a white bull’s-eye presented at the center of the display . A brief break period was provided after each fMRI run upon a subject’s request . Recorded fMRI data were preprocessed using the BrainVoyager QX software [36] . All functional images underwent 3-D motion correction . No spatial or temporal smoothing was applied . Rigid-body transformations were performed to align the functional images to the structural image for each subject . A gray matter mask was used to extract BOLD signals only from gray matter voxels for further analysis . We specified seven ROIs implicated in facial preference [14–20] according to anatomical data for each subject: the cingulate cortex ( CC ) , lateral prefrontal cortex ( LPFC ) , orbitofrontal cortex ( OFC ) , occipitotemporal cortex , insular cortex , basal ganglia , and amygdala . LPFC was defined as the middle frontal gyrus plus the inferior frontal sulcus . OFC was defined as the orbital gyrus plus the orbital sulci . The occipitotemporal cortex was defined as the lateral occipitotemporal gyrus , the medial occipitotemporal gyrus , plus the occipitotemporal sulcus . The basal ganglia were defined as the caudate , the pallidum , the putamen , plus the nucleus accumbens . Voxels from the left and right hemispheres were merged for each ROI . The cortical regions were specified using an atlas on the BrainVoyager QX software [36] . A cortical surface for each subject was spatially normalized into a standard cortical surface using a cortex-based alignment method [37] . Then , the specified regions were projected into a native space for each subject . The subcortical regions were specified for each subject using an automated brain parcellation method [38] on the Freesurfer software ( http://surfer . nmr . mgh . harvard . edu ) . A time-course of BOLD signal intensities was extracted from each voxel in each ROI and shifted by 4 s to account for the hemodynamic delay using the Matlab software . A linear trend was removed from the time-course . The time-course was z-score normalized for each voxel using all time points except for those for the initial 10 s in each fMRI run . This normalization was aimed to minimize baseline differences in time-courses of BOLD signal intensities across the fMRI runs . The data samples for computing the decoder were created by averaging the BOLD signal intensities of each voxel for three volumes corresponding to the 6 s rating period . To construct a preference decoder for each ROI , we used a sparse linear regression algorithm [25] , which automatically selected the relevant voxels within an ROI for decoding . Note that the behavioral preference ratings measured in this study were non-linear . Although they ranged from one to ten , preference measurement on the Likert-type scale cannot be considered strictly linear . Thus , before applying the sparse linear regression for each ROI , the behavioral preference ratings were linearized using an arc hyperbolic tangent function . An estimated rating Rdecoded , that is , the decoder output calculated based on an activation pattern for a trial , was obtained in each ROI by Rdecoded=WvoxelT∙Avoxel+b . Here , Avoxel represents the activation pattern of voxels in the ROI for the trial . Wvoxel indicates linear weights for the voxels , which were optimized by the sparse linear regression algorithm based on fMRI data , which was used for training the decoder . b corresponds to the decoder’s constant term , which was determined for each subject as his/her average behavioral preference rating across all faces in the preference-rating task during the fMRI decoder construction stage . Avoxel and Wvoxel are denoted as n-dimensional column vectors with n as the number of voxels in each ROI . T denotes matrix transpose . The inputs to the decoder were subjects’ moment-to-moment brain activations in each ROI , whereas the outputs from the decoder represented the decoder’s best estimate of the corresponding behavioral preference ratings . Decoder performance for each ROI was defined as the correlation coefficient between actual subjects’ behavioral preference ratings in the preference-rating task and the estimated ratings calculated from activation patterns of the ROI and evaluated by a leave-one-run-out cross validation procedure . In the cross-validation procedure , the pairs of the actual subjects’ behavioral preference ratings and the activation patterns for the ROI measured on one fMRI run were treated as the test data ( 20 samples ) , whereas those measured on the remaining runs ( 220 samples ) were used for training the decoder to estimate subjects’ trial-by-trial behavioral preference ratings . Thus , 12 cross-validation sets were generated per subject . For each voxel , activation amplitudes of the training and test data were normalized by mean and variance of activation amplitudes of the training data so that mean and variance of voxel activation amplitudes corresponding to the induction period would be zero and one , respectively . The correlation coefficients for each ROI were first standardized using Fisher’s transformation , averaged over the cross-validation sets , and then averaged across subjects , as shown in S1A Fig . The result of the fMRI decoder construction stage showed that the highest decoder performance was obtained from the CC ( S1A Fig ) . Consistent with this result , previous neuroimaging studies have reported that the CC is highly involved in facial preference [15 , 18 , 39] as well as preferential decision-making in general [21–23] . Thus , we selected the CC as the target region for the main experiment . Note that the highest decoder performance was also found in the CC when we evaluated decoder performance in the same way using the fMRI signals obtained in the fMRI decoder construction stage of the main experiment ( S1B Fig ) . These results indicate robustness of the tendency in which the CC most accurately reflects subjects’ facial preference in the preference-rating task in this study . Thirty subjects participated in the main experiment . The main experiment consisted of five stages: pre-test ( 1 d ) , fMRI decoder construction ( 1 d ) , induction ( fMRI DecNef , 3 d ) , post-test ( 20 min after the induction stage ) , and interview ( immediately after the post-test stage ) stages , in this order ( Fig 1A ) . The pre-test , fMRI decoder construction , and induction stages were separated by at least 24 h . Thirty subjects in the main experiment were randomly assigned to one of the higher-preference ( n = 12 ) , lower-preference ( n = 12 ) , and control ( n = 6 ) groups . They were not informed about their assigned group . The procedures of the pre- and post-test stages in the main experiment were identical to those of the pre-test stage in the pilot experiment . As in the pilot experiment , the 100 highest-rated faces , the 100 lowest-rated faces , and 40 neutrally rated faces were selected to be used in the subsequent fMRI decoder construction stage . In addition , out of the 40 neutrally rated faces , 15 were randomly selected for use in the induction stage ( “induction faces” ) and another set of 15 was also randomly selected as a preference-matched control against the induction faces ( “baseline faces” ) . The baseline faces were not shown during the subsequent induction stage . For the higher- and lower-preference groups , the procedures of the fMRI decoder construction stage in the main experiment were identical to those in the pilot experiment ( see Pilot Experiment ) . A preference decoder for the CC was computed for each subject for use in the subsequent induction stage . To train the decoder , we used 240 data samples obtained from the 240 trials in the 12 fMRI runs . For each voxel , activation amplitudes of the training data were normalized by the mean and the variance of activation amplitudes of the training data so that the mean and the variance of voxel activation amplitudes would be zero and one , respectively . The mean ( ± s . e . m . ) numbers of voxels selected by the sparse linear regression algorithm to decode the subjects’ preference ratings were 219 . 8 ± 0 . 5 across subjects . Note that because the decoder was built based on data samples from all the trials in the fMRI decoder construction stage , a unique set of voxels was selected for each subject . For the control group , the visual presentations in the fMRI decoder construction stage were identical to those in the pilot experiment while the experiment was conducted outside the MRI scanner without fMRI measurements . In the induction stage , which consisted of three daily sessions , subjects from the higher- and lower-preference groups were instructed to regulate the activation of their brains , which were controlled by an online fMRI technique [24 , 40–43] . On each day , subjects participated in up to 12 fMRI runs . The mean ( ± s . e . m . ) number of runs per day was 10 . 8 ± 0 . 2 . Each fMRI run for the induction stage consisted of 15 trials ( one trial = 20 s ) preceded by a 30 s fixation period ( one run = 330 s ) . The fMRI data for the initial 10 s were discarded to allow the longitudinal magnetization to reach equilibrium . During each run , subjects were instructed to fixate on a white bull’s-eye presented at the center of the display . After each fMRI run , a brief break period was provided upon a subject’s request . Each trial in the induction stage ( Fig 1C ) consisted of a face period ( 0 . 5 s ) , an induction period ( 6 s ) , a fixation period ( 6 s ) , a feedback period ( 2 s ) , and an inter-trial period ( 5 . 5 s ) , in that order . During the face period , one of the 15 induction faces described above was presented for 0 . 5 s at the center of the display . The order of presentation of the 15 induction faces was randomized for each fMRI run . During the induction period , the color of the fixation point changed from white to green , and no visual stimulus except for the fixation point was presented . Subjects were instructed to regulate activation of their brain , with the goal of making the size of a solid green disk presented in the later feedback period as large as possible . The experimenters provided no further instructions or strategies . During the fixation period , subjects were asked simply to fixate on the central white point . This period was inserted between the induction period and the feedback period to compensate for the known hemodynamic delay , which we assumed lasted 4 s , during which activation patterns in the CC were calculated in time for a green disk to be shown in the subsequent feedback period . The feedback period presented the green disk for 2 s . The size of the disk was determined based on the estimated rating ( see below ) , which is the decoder output value based on the BOLD signal pattern of the CC measured in the prior induction period . The green disk was always enclosed by a larger green concentric circle ( 10° in diameter ) , which indicated the disk’s possible maximum size . The feedback period was followed by the inter-trial period , during which subjects were asked to fixate on the central white point . This period was followed by the start of the next trial . The size of the disk presented during the feedback period was based on the estimated rating from the CC activation pattern , which was computed during the fixation period , as follows . First , measured functional images underwent 3-D motion correction using the Turbo BrainVoyager software . Second , a time-course of BOLD signal intensities was extracted from each of the voxels in the CC identified in the fMRI decoder construction stage and was shifted by 4 s to account for the hemodynamic delay . Third , a linear trend was removed from the time-course for each voxel using a linear regression algorithm based on all time points except for those for the initial 10 s in each fMRI run , and the BOLD signal time-course was z-score normalized for each voxel using BOLD signal intensities measured for 20 s starting from 10 s after the onset of each fMRI run . Fourth , the data sample used to calculate the size of the disk was created by averaging the BOLD signal intensities of each voxel for three volumes corresponding to the 6 s induction period . Finally , the estimated rating was calculated from the data sample using the decoder constructed in the fMRI decoder construction stage . For the higher-preference group , the size of the disk was proportional to the estimated rating ( ranging from one to ten ) . For the lower-preference group , the size of the disk was proportional to 11 minus the estimated rating so that a lower estimated rating resulted in a larger disk . In addition to the fixed amount of the compensation for participation in the experiment , a bonus of up to 3 , 000 JPY was paid to subjects based on the mean size of the disk during each day . Induced CC-activation shifts shown in Fig 4 and S4A Fig indicate how far the activation patterns of the CC during the induction stage are from the activation patterns in the CC corresponding to the average behavioral preference rating . The induced CC-activation shift was calculated as follows . First , the estimated rating Rdecoded from an activation pattern of the CC for a trial was computed by the same method as in the pilot experiment , but only from the CC . That is , Rdecoded was computed by Rdecoded=WvoxelT∙Avoxel+b . Here , Avoxel represents the activation pattern of voxels in the CC in the induction period . Wvoxel indicates linear weights for the voxels , which had been computed for each subject in the fMRI decoder construction stage . b corresponds to the decoder’s constant term and had been determined for each subject as her/his average behavioral preference rating in the preference-rating task during the fMRI decoder construction stage . This constant term varied across subjects . Avoxel and Wvoxel are denoted as n-dimensional column vectors with n as the number of voxels in the CC . T denotes matrix transpose . The induced CC-activation shift for a trial was defined by Rdecoded−b=WvoxelT∙Avoxel . Based on the following computation , the induced CC-activation shift represents how far the activation patterns in the CC during the induction stage are away from the activation pattern in the CC corresponding to the average behavioral preference rating initially for each subject . As described above , the constant term b was determined for each subject as her/his average behavioral preference rating in the preference-rating task during the fMRI decoder construction stage . The set of 240 faces ( the 100 highest-rated faces , the 100 lowest-rated faces , and 40 neutrally rated faces ) used in the fMRI decoder construction stage was selected for each subject according to her/his behavioral preference ratings to the 400 faces presented in the pre-test stage . That is , the constant term b represents the subject’s average behavioral preference rating for the population of faces . Thus , the induced CC-activation shift , which is calculated by subtracting the constant term b from the estimated rating Rdecoded , represents how far the activation pattern in the CC is from the activation patterns in the CC corresponding to the average behavioral preference rating for each subject . It is necessary to obtain the induced CC-activation shift in order to appropriately evaluate and compare induced activation patterns in the CC across subjects and groups during the induction stage . Note that an induced CC-activation shift being “0” indicates that the activation pattern of the CC was biased in neither the high nor low preference direction . A positive ( or negative ) value of the induced CC-activation shift indicates that the activation pattern of the CC was biased toward a positive ( or negative ) preference direction , compared to the activation pattern corresponding to the average behavioral preference rating for each subject . Thus , when the mean-induced CC-activation shift is significantly higher ( or lower ) than 0 , this means that subjects accomplished significant learning to induce the preference-related activation patterns in the CC that correspond to higher ( or lower ) preference rating . With the control group , during the induction stage , the induction faces were presented in the same way as with the higher- and lower-preference groups . On the other hand , unlike with the higher- and lower-preference groups , the experiment was conducted outside the MRI scanner without fMRI measurements for the control groups . Subjects from the control group conducted a fixation task ( see below ) instead of the task given to those from the higher- and lower-preference groups during the induction stage . In the fixation task for the control group in the “induction” stage , during the 6 s “induction” period , the luminance of the central fixation point slightly decreased ( from green to dark green ) , returning to its original luminance 300 ms later . This luminance change occurred several times in an unpredictable manner during the 6 s period . Subjects from the control group were asked to count the number of luminance changes and report whether the number of the changes was even or odd by pressing one of two buttons using the index or middle finger of their right hand during the fixation period . The task difficulty was controlled by using an adaptive staircase method , so that the overall task difficulty was kept constant throughout the induction stage; the degree of fixation luminance change was slightly increased in the trial following an incorrect answer , and slightly decreased after two consecutive correct answers . Otherwise , luminance was kept around the same . The mean ( ± s . e . m . ) task accuracy for the fixation task was 67 . 4% ± 5% across subjects . The green “feedback” disk was presented during the 2 s “feedback” period . The size of the disk was determined randomly for each trial . Subjects were instructed to fixate on the center of the display during the feedback period . We tested whether preference-related activation patterns in the CC leaked out of the CC to other regions and induced similar activation patterns in these regions . We conducted the leak analysis using an ROI-based method and a searchlight method . To appropriately evaluate the statistical significance of a correlation coefficient between two vectors of values , we performed a permutation test in the following ways . ( 1 ) We permutated a relationship between the two vectors 1 , 000 times and obtained a permutation distribution of correlation coefficients between the two values . ( 2 ) We tested whether a correlation coefficient between the two values obtained from the original relationship was ranked within top 5% in the distribution . If so , the correlation coefficient was regarded as significant . In the case of between-subject statistics , we calculated a z-score of the original correlation coefficient by comparing the original correlation coefficient with the distribution obtained by the permutation [28] . Visual stimuli were presented on an LCD display ( 1 , 024 × 768 resolution , 60 Hz refresh rate ) during the pre- and post-test stages and via an LCD projector ( 1024 × 768 resolution , 60 Hz refresh rate ) during fMRI measurements in a dim room . All visual stimuli were made using the Matlab software and Psychtoolbox 3 [44] on Mac OS X . Subjects were scanned in a 3T MR scanner ( Siemens MAGNETOM Verio ) with a 12-channel head matrix coil in the ATR Brain Activation Imaging Center . FMRI signals were measured using a gradient echo-planar imaging sequence . In the fMRI experiments , 33 contiguous slices ( repetition time = 2 s , voxel size = 3 × 3 × 3 . 5 mm3 , 0 mm slice gap , field of view = 192 mm × 192 mm , echo time = 26 ms , matrix size = 64 × 64 , bandwidth = 2 , 367 Hz/pixel , phase encoding direction: from anterior to posterior , slice order: interleaved ) oriented parallel to the AC-PC plane were acquired , covering the entire brain . For an automated parcellation method [38] , T1-weighted MR images ( magnetization-prepared rapid gradient-echo or MP-RAGE; 256 slices , the number of partition = 208 , voxel size = 1 × 1 × 1 mm3 , 0 mm slice gap , repetition time = 2 , 250 ms , inversion time = 900 ms , echo time = 3 . 06 ms , flip angle = 9 deg , field of view = 256 mm , matrix size = 256 × 256 , bandwidth = 230 Hz/pixel , phase encoding direction: from anterior to posterior , partition ( 2nd phase ) encoding direction: from right to left ) were also acquired during the fMRI decoder construction stage . | Although it is well studied how averaged activation of a brain region relates to behavior , it is still unclear if specific patterns of activation within regions also relate to cognitive function . In recent years , several methods have been developed for manipulating brain activity in humans . Real-time functional magnetic resonance imaging decoded neurofeedback ( fMRI DecNef ) is a method that allows the induction of specific patterns of brain activity by measuring the current pattern , comparing this to the pattern to be induced , and giving the subjects feedback on how close the two patterns of neuronal activity are . Using fMRI DecNef , we manipulated the pattern of activation in the cingulate cortex—a part of the cerebral cortex that plays a role in preference to different categories including faces and daily items—and tested whether we could change these preferences . In the experiment , a specific activation pattern in the cingulate cortex corresponding to higher ( or lower ) preference was induced by fMRI DecNef while subjects were seeing a neutrally preferred face . As a result , these neutrally preferred faces became more ( or less ) preferred . Our finding suggests that different patterns of activation in the cingulate cortex represent , and are sufficient to determine , different emotional states . Our new approach using fMRI DecNef may reveal the importance of activation patterns within a brain region , rather than activation in a whole region , in many cognitive functions . | [
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"analys... | 2016 | Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States |
Studies in vision show that attention enhances the firing rates of cells when it is directed towards their preferred stimulus feature . However , it is unknown whether other sensory systems employ this mechanism to mediate feature selection within their modalities . Moreover , whether feature-based attention modulates the correlated activity of a population is unclear . Indeed , temporal correlation codes such as spike-synchrony and spike-count correlations ( rsc ) are believed to play a role in stimulus selection by increasing the signal and reducing the noise in a population , respectively . Here , we investigate ( 1 ) whether feature-based attention biases the correlated activity between neurons when attention is directed towards their common preferred feature , ( 2 ) the interplay between spike-synchrony and rsc during feature selection , and ( 3 ) whether feature attention effects are common across the visual and tactile systems . Single-unit recordings were made in secondary somatosensory cortex of three non-human primates while animals engaged in tactile feature ( orientation and frequency ) and visual discrimination tasks . We found that both firing rate and spike-synchrony between neurons with similar feature selectivity were enhanced when attention was directed towards their preferred feature . However , attention effects on spike-synchrony were twice as large as those on firing rate , and had a tighter relationship with behavioral performance . Further , we observed increased rsc when attention was directed towards the visual modality ( i . e . , away from touch ) . These data suggest that similar feature selection mechanisms are employed in vision and touch , and that temporal correlation codes such as spike-synchrony play a role in mediating feature selection . We posit that feature-based selection operates by implementing multiple mechanisms that reduce the overall noise levels in the neural population and synchronize activity across subpopulations that encode the relevant features of sensory stimuli .
We are constantly exposed to a diverse set of stimuli that excite all of our senses . To effectively function in this environment it is critical that we employ filtering mechanisms such as selective attention to extract the most relevant information to our goals . Specifically , attention has been shown to increase the firing rate ( FR ) and spike-spike synchrony between cells ( e . g . , spike-synchrony ) and decrease the correlated noise activity between neurons sharing similar somatotopic [1]–[3] or visual-spatial receptive fields ( RFs ) [4]–[12] . Further , in the visual system , feature-based attention can enhance and suppress neurons' FRs when the focus of attention is directed towards the cells' preferred and non-preferred stimulus feature , respectively [13] , [14] . For instance , attention towards a stimulus moving in a particular direction increases the FR of cells that are tuned for stimuli moving in that direction . This mechanism , known as the feature similarity gain model , predicts gain-related attention effects in visual cells [15] and may be a common mechanism across species ( see [16] , [17] for examples in humans ) . A recent study conducted in mouse primary visual cortex ( V1 ) found that orientation selective cells that share similar angle preferences are significantly more interconnected with each other . This study also showed a similar relationship in neurons that displayed analogous responses to different naturalistic stimuli [18] . This connectivity pattern is akin to that of V1 orientation-tuned cells in other mammals [19] , [20] , where enhanced synchronized spiking activity between neurons tuned for similar orientations was found [19] . Similarly , cells in primary and secondary somatosensory cortex ( SII ) show tuning for distinct features such as orientation and frequency [21]–[27] , and putatively , as in V1 , neurons with similar feature selectivity may be preferentially connected . We therefore questioned whether attention takes advantage of such preferential coupling in SII cortex by further modulating the correlated activity between cells tuned for the relevant modality features of a task ( e . g . , orientation versus vibratory frequency ) . We hypothesized that spike-synchrony between SII cells selective for the same feature modality would be increased when attention was directed to that modality . Further , based on results in the visual system [9]–[12] , we predicted that attention would decrease the spike-count correlation ( rsc ) between neurons across trials ( also termed noise correlations ) . A recent study found reduced rsc between cells with enhanced FRs when attention was directed towards a particular feature of the stimulus [10] . Here , we assessed whether the visual and tactile systems employ analogous mechanisms of feature selection by examining whether similar rsc effects are observed in the somatosensory system , and whether attention effects on FR are predicted by the feature similarity gain model [11] , [13] , [15] . Another goal was to examine the relationship between spike-synchrony and rsc during attention and their correlation with behavioral performance . Spike-synchrony and rsc measure correlated activity between neural populations but at different temporal scales , with spike-synchrony defined as concomitant spikes within a narrow window ( e . g . , ±2 ms ) and rsc characterized as correlated mean spiking activity across broader timescales ( >100 ms ) . Indeed , studies show that spike-synchrony and rsc can be linearly related [28] and this relationship is enhanced by the tuning similarity between cells [29] . However , these findings seem difficult to reconcile with the observations that attention reduces rsc [9] , [12] but also increases spike-synchrony [2] . One possibility is that attention modulates these correlation codes according to the feature selectivity of the population . Indeed , in one study [28] activity was recorded from medio-temporal ( MT ) neurons with similar RFs that were tuned for the same feature modality ( i . e . , motion ) . In contrast , in the visual area 4 ( V4 ) studies [9] , [12] it was investigated how attention modulates rsc across cells with the same RF , without regards to their feature modality selectivity . Thus , it is possible that reductions in rsc by attention were predominately between neurons that did not share the same feature selectivity . Our findings reveal that attention enhances both FR and spike-synchrony when it is focused towards the preferred feature modality of cells . In addition , we found that attention effects in spike-synchrony correlated well with behavior . Consistent with previous reports in vision [9] , [12] , rsc in SII cells increased when attention was directed towards the visual modality ( i . e . , away from the somatotopic RF of the neurons ) . Importantly , these results were observed across animals performing slightly different attention tasks , suggesting that these attention mechanisms are prevalent across perceptual tasks . Taken together , our data are consistent with a feature selection model that operates by reducing the background correlated noise levels in the population and selectively increasing the FR and synchronous activity between cells that encode the stimulus features relevant for the task at hand .
We analyzed the effects of attention on the FR of SII neurons . Figure 2A shows attention effects in two feature selective neurons . The two graphs to the right of each colored graph illustrate the frequency and orientation tuning curves for each neuron . The left graph illustrates a neuron selective for orientation with a preferred angle of 45° ( as shown in its orientation tuning graph ) and enhanced FR when attention was directed towards orientation compared to frequency . The right graph shows the opposite pattern for a neuron selective for frequency with preferred vibration at 10 Hz ( as shown in its frequency tuning graph ) . The results for the three animals were highly similar ( see Figures S2 and S3 and text below ) , therefore we combined their data for population analyses . The population statistics showed that 43% of all feature selective neurons ( n = 94 ) were modulated by attention , with 75% of these neurons having greater FR when attention was directed towards versus away from the cell's preferred feature . Animal 2 received the same pattern of tactile stimulation as animal 1 , but during recordings it was never cued to perform the orientation task . Animal 3 performed a match-to-sample tactile orientation task only . In animal 2 we analyzed attention effects by comparing the activity of attend frequency versus attend visual , while in animal 3 we analyzed attention effects by comparing activity between attend orientation versus attend visual . This analysis was largely performed in feature selective cells ( i . e . , orientation or frequency ) . However , we performed additional analyses to further assess the validity of these attention effects . In particular , we reasoned that if FR attention effects are indeed feature-specific , animal 2's orientation selective cells should exhibit reduced attention effects , as compared to frequency selective neurons , when attention was apportioned to frequency versus vision . We observed that only 22% ( two out of nine cells ) of animal 2's orientation selective cells had significantly greater FRs when attending towards frequency versus vision . In contrast , 38% of frequency selective cells in animal 2 had increased FRs when attention was deployed to frequency versus vision ( five out of 13 cells ) . Animal 1 displayed similar attention modulations in its feature modality selective cells , with 16% of orientation selective ( three out of 19 ) and 38% of frequency selective cells ( six out of 16 ) displaying higher FRs when attention was deployed to frequency versus vision . Similar to animal 2's hypothesis , we reasoned that non-feature selective cells in animal 3 should exhibit enhanced activity , or a null effect , when attention was directed to vision as compared to orientation . Consistent with this hypothesis , 54% of non-orientation selective cells in this animal did not exhibit attention effects , while 28% had significantly greater activity when attention was apportioned to vision as compared to orientation . The magnitude of attention effects was quantified using a feature attention index ( FAI ) [31] . This was calculated by subtracting the mean response when attention was directed away from a neuron's preferred feature modality from the mean response when attention was directed towards the preferred feature modality , and dividing the difference by the sum of these two quantities . In animal 2 the FAI was calculated by subtracting the mean response to attention towards vision from attention to frequency and dividing the difference by the sum of these quantities . In animal 3 the FAI was calculated using the same formula but substituting attention to frequency with attention to orientation . Further , because by default , non-feature selective cells do not have a preference for orientation or frequency stimuli , we devised a surrogate “preferred feature modality” to calculate the FAI in these cells . We reasoned that classifying the “preferred feature modality” of non-feature selective cells in this way would lead to a consistent pattern of feature-based attention effects that would be comparable to those observed in feature selective populations . The “preferred feature modality” in non-feature selective cells was assigned by first computing a feature modality selectivity index ( FMSI ) value for both orientation and frequency conditions , and then labeling the condition with highest FMSI as the “preferred feature modality . ” The FMSI was computed by subtracting the response to the stimulus that evoked the weakest activity ( e . g . , a 60 Hz vibration in the case of frequency; a 22 . 5° oriented stimulus in the case of orientation ) from the stimulus that elicited the strongest response within the same feature modality ( e . g . , 40 Hz vibration in frequency , or 90° in orientation ) and dividing this difference by the sum of the two quantities . This analysis was done from data collected in the feature selectivity characterization protocols . To derive the FAI for a non-feature selective neuron we subtracted the response to attention towards the “least preferred feature modality” ( i . e . , the feature with lower FMSI value ) from attention towards the “preferred feature modality” ( i . e . , the feature modality with higher FMSI value ) and dividing the difference by the sum of the two quantities . Unfortunately , this FMSI analysis could not be performed in animal 3 because the frequency selectivity protocol was not performed in this animal . In this animal we assessed FAI in non-feature selective neurons by subtracting the mean response when attention was directed towards orientation from the mean response when attention was directed towards the vision , and dividing the difference by the sum of these two quantities . The mean FAIs for feature selective and non-feature selective populations were 0 . 057 and −0 . 009 , respectively . The FR FAI values were not normally distributed , thus we conducted a Mann-Whitney U-test to test for significant differences in the effects of attention between the two cell populations . The analysis revealed a significant difference , whereby feature selective cells exhibited higher FAI ( Z = 3 . 42 , p = 0 . 0006; see Figure 2B ) . The effect size , measured as Cohen's d , was 0 . 59 . We assessed whether there was a relationship between a cells' FMSI and its FAI . To do this we sorted FAIs as a function of the difference between the highest and lowest FMSI ( i . e . , the preferred and non-preferred FMSI ) . This analysis was performed in both feature and non-feature selective populations and in animals 1 and 2 only . As described above , an FMSI could not be computed for animal 3 . Linear regressions , using FAI as the response variable did not reveal a systematic relationship in feature selective ( R2 = 0 . 02 , p>0 . 32 ) or non-feature selective populations ( R2 = 0 . 001 , p = 0 . 64 ) . These data are illustrated in the left panel of Figure S4 . We further tested whether attention effects in the FRs were stimulus-value specific . For this analysis we identified neurons whose greatest response to a stimulus value during the feature selectivity characterization protocols was tested during the attention experiment ( e . g . , 45° or 90° in an orientation selective neuron , number of cells = 130 ) , regardless of whether they were classified as feature selective by our definition . The FR response to the non-preferred stimulus ( e . g . , 135° ) was subtracted from that of the preferred stimulus ( e . g . , 45° ) and we conducted a Mann-Whitney U-test between Attention towards the preferred versus non-preferred feature modality conditions . The data revealed significantly greater attention effects on the preferred stimulus versus non-preferred stimulus value ( Z = 4 . 93 , p<0 . 0001 ) . Figure 3 shows the FRs for the preferred ( black dots ) and non-preferred ( gray dots ) stimulus value when the animal attended towards the preferred feature versus away from the preferred feature modality . The figure shows the black dots consistently above the unity line . These findings agree with those reported by [13] in the visual system , where FR attention effects on MT neurons were found to be stimulus-value specific . Taken together , these results provide evidence that the feature similarity gain model also operates in the somatosensory system , suggesting that both vision and touch employ similar gain-related mechanisms of feature selection . Spike-synchrony was defined as the number of times two neurons fired an action potential ( AP ) within ±2 ms of each other on a 1 ms sliding time scale for every trial and averaged over all trials . This technique has the advantage over other methods ( e . g . , cross-correlograms [CCGs] , which averages over all time scales ) in that it maintains the temporal structure of spike-synchrony events , thus allowing us to assess changes in synchrony across time , instead of the mean coincident spikes across the entire spike train . Spike-synchrony due to “chance” for each attention condition was estimated using the jitter-correction method [32] , and this “chance” synchrony was subtracted from the observed spike-synchrony . Only neural pairs whose jitter-corrected spike-synchrony was significantly greater than zero for at least 100 ms ( p-value level of 0 . 05 ) in at least one attention condition were analyzed for attention effects on spike-synchrony ( n = 47 out of 57 feature selective neural pairs , and n = 57 out of 65 non-feature selective neural pairs ) . We classified a feature selective neural pair as one in which two simultaneously recorded neurons had selectivity for the same feature modality ( e . g . , orientation ) . In contrast , a neural pair in which two neurons were not selective for both frequency and orientation tactile features was categorized as non-feature selective . These include neural pairs in which one neuron was selective for a particular tactile feature but the other cell was not . Similar to the FR results , we observed feature specific attention effects in spike-synchrony . Figure 4A illustrates the instantaneous spike synchrony of two feature selective neural pairs for all attention conditions . The left graph shows that attention towards frequency evoked the largest spike-synchrony for a neural pair selective for frequency . The right graph shows that attention towards oriented features yielded the highest spike-synchrony for a neural pair selective for orientation . The lower panels of Figure 4A show the instantaneous FR profiles of each neuron comprising the neural pair . While increases in FR and spike-synchrony often occurred around the same time , jitter correction methods applied to the synchrony data [32] show that attention effects on synchrony are not explained by FR modulations alone ( see below ) . We found that the mean spike-synchrony rates of feature selective neurons were 5 . 51 times greater than the spike-synchrony due to “chance” computed from the jitter method [32] . Figure S3 shows other example neural pairs from both animals illustrating similar feature selective effects . The population data revealed attention effects on spike-synchrony in 55% of all feature selective neural pairs ( 26 out of 47 ) . Of these , 77% had greater synchrony rates when attending towards versus away from the preferred feature modality , and a Pearson's chi-squared test revealed that this difference was significant ( χ2 = 7 . 53 , p = 0 . 003 ) . The degree of synchronous firing was not correlated with the anatomical distance between neural pairs ( Figure S5 ) . Attention effects on spike-synchrony were quantified using the same FAI formula for the FR data . This is illustrated in Figure 4B for feature selective and non-feature selective neural pairs . The average FAI of feature selective populations was 0 . 102 , whereas the mean FAI for non-feature selective cells was 0 . 013 . Similar to attention effects on FR , we observed that FAI values for spike-synchrony were not normally distributed . A Mann-Whitney U-test revealed greater FAI for feature selective neural pairs as compared to non-feature selective cells ( Z = 2 . 06 , p = 0 . 039 ) , with an effect size of 0 . 58 as measured by Cohen's d . We further tested whether there was a relationship between FMSI and spike-synchrony FAI . We calculated the average of the two neurons' FMSI for each feature condition , and sorted the FAI as a function of the difference between the preferred and non-preferred FMSI . This analysis was performed in feature selective and non-feature selective neurons . As described above , this analysis was only performed in animals 1 and 2 because an FMSI could not be computed for animal 3 . Linear regressions did not reveal a significant relationship in feature selective ( R2 = 0 . 032 , p = 0 . 28 ) or non-feature selective cells ( R2 = 0 , p>0 . 95 ) . These data are illustrated in the middle panel of Figure S4 . Computational and experimental studies have shown that increases in spike-synchrony can be caused by increases in neurons FR and/or slow co-variation artifacts [33]–[35] . To test against these confounds , we corrected our spike-synchrony data by employing the temporal jitter method developed by Amarasingham and colleagues [32] , which removes the effects of slow wave co-variations beyond the correlation window chosen by the experimenter ( in our case 50 ms ) . We performed a series of numerical simulations to show this method is also robust against enhancements in neurons' FR ( see Figure 4C ) . In these simulations , two independent spike trains were generated using a non-homogeneous Poisson process ( 250 trials ) , which simulated the FR profile of a neural pair . A non-homogenous Poisson rate function was used to have a better approximation of the spiking behavior of a typical neuron . The FR of each spike train was systematically modulated from 5 to 28 . 65 Hz . For each of the 250 trials the spike-synchrony between the two spike trains was calculated using a ±2 ms bin window ( the same used in the analyses of our experimental data ) , and then averaged across all trials . These procedures were repeated 5 , 000 times and averaged . As expected , spike-synchrony increased as a function of FR ( Figure 4C , brown trace ) . However , the jitter correction method removed this dependence ( mustard color trace ) . Taken together these findings indicate that our spike-synchrony results are not accounted for by increases in FR or , as shown by [32] , slowly co-varying changes in FR . rscs were computed in feature selective and non-feature selective neural pairs during the stimulus presentation and baseline period ( using the averaged activity from −500 to 0 ms prior to visual cue onset ) . We observed that rsc values were normally distributed for both non-feature selective and feature selective populations . A two-way repeated measures ANOVA with factors of attention ( orientation , frequency , and visual ) and time ( baseline versus stimulus presentation period ) on non-feature selective populations did not reveal a significant main or interaction effect for any condition ( see Figure 5A ) . In contrast , a two-way repeated measures ANOVA with factors of attention ( attention towards the preferred feature modality , attention away from the preferred feature modality , and attention to vision ) and time ( baseline versus stimulus presentation period ) on feature selective populations revealed a main effect of attention ( F ( 2 , 112 ) = 3 . 59 , p = 0 . 031 ) and a main effect of time ( F ( 1 , 56 ) = 5 . 62 , p = 0 . 021 ) ( see Figure 5B ) . Post hoc paired sample t-tests revealed that the main effect of attention was driven by higher rsc in the attend towards versus attend away from the preferred feature condition ( t ( 56 ) = 2 . 46 , p = 0 . 02 ) , as well as higher rsc in the attend visual versus attend away from the preferred feature condition ( t ( 56 ) = 2 . 23 , p = 0 . 030 ) . The main effect of time was driven by higher rsc during the stimulus presentation period ( t ( 56 ) = 2 . 37 , p = 0 . 021 ) . No other significant effects were observed . We also assessed whether there was a relationship between FMSI and attention effects on rsc . We performed the same analyses as in the spike-synchrony data . Linear regressions failed to reveal a relationship between attention effects on rsc and FMSI in feature selective ( R2 = 0 . 02 , p = 0 . 39 ) and non-feature selective neural pairs ( R2 = 0 . 02 , p = 0 . 75 ) . These data are illustrated in the right panel of Figure S4 . The effects of attention on rsc in feature selective neural pairs are not in entire agreement with the findings presented in [11] . Briefly , that study found decreased rsc in neural pairs that displayed concomitant increases in FR when attention was apportioned to a particular feature of a visual stimulus . We reasoned that because spike-synchrony is in itself a correlation mechanism , but at a faster timescale , enhancements in rsc by attention might reflect the increases in synchrony rates observed in the same neural population . To test this hypothesis we assessed whether increases in spike-synchrony were temporally correlated with enhancements in rsc . We computed the rsc and spike-synchrony rates every 100 ms during the stimulus presentation period in every feature selective neural pair . Then , we sorted rsc values as a function of the time-binned spike-synchrony rates and averaged these values across neurons . These data , illustrated in Figure 6A , show a positive relationship between jitter-corrected spike-synchrony and rsc but only when attention was directed to the preferred feature modality of the neural pair . A regression analysis showed that this relationship was well-fitted by a linear function ( F ( 5 , 41 ) = 8 . 58 , p<0 . 001 , R2 = 0 . 51 ) . These results suggest that increases in spike-synchrony underlie the enhancements in rsc but only when attention is directed toward the preferred feature of cells . We tested whether the relationship between spike-synchrony and rsc was exclusive to feature selective neurons by computing the same analysis as above in non-feature selective neural pairs . Regression analyses did not reveal a statistical effect in any attention condition ( p>0 . 05; R2 = 0 . 09 , 0 . 21 , R2 = 0 . 17 for attend-orientation , frequency , and visual , respectively , see Figure 6B ) . We performed a series of numerical simulations to determine possible neural mechanisms that may account for the correlation between spike-synchrony and rsc . We implemented scenarios where correlated spiking activity within a neural population was produced by either a source that ( 1 ) caused a coincident volley of spikes across the population or ( 2 ) co-modulated the mean FR function of all neurons , resulting in a correlated change in the probability of generating coincident spikes across the population . The former is comparable to a neural population receiving strong common monosynaptic inputs , whereby cells' membrane potentials are raised above depolarization threshold level around the same time ( i . e . , a supra-threshold influence ) . In contrast , the latter is akin to a probabilistic model that modulates the membrane potentials of cells without necessarily causing cell depolarization . This latter model is similar to those implemented in previous studies [12] , [33] . A visual illustration of the two scenarios is shown in Figure S6 . For both scenarios , we constructed two independent spike trains ( 250 trials ) generated by a non-homogenous Poisson process with a mean FR of 25 . 58 Hz , constructed to mimic the FR profile of a typical neuron responding to a stimulus . This FR profile and an example raster plot are shown in Figure S6A . In the first scenario the source was a binary waveform whose value was usually zero , but periodically jumped to one and added 0 to 10 spikes ( uniformly distributed ) in both spike trains every 400 ms ( i . e . , 2 . 5 Hz ) . In the second scenario the rates of both spike trains were multiplied by a 2 . 5 Hz sine wave with amplitude ranging from 0 . 5 to 1 . We chose a 2 . 5 Hz signal based on the findings by [12] , which showed that most of the correlated spiking activity across a neural population is captured in the ongoing oscillating activity between 0 and 5 Hz . Similar to the analysis performed on our experimental data , we sorted the rsc as a function of the jitter-corrected spike-synchrony and divided the data across ten bins . These procedures were repeated 5 , 000 times . The results from scenario 1 revealed a systematic linear relation analogous to that observed in our experimental dataset ( R2 = 0 . 91 , F ( 1 , 8 ) = 89 . 75 , p<0 . 001 ) ( Figure 6C , left graph ) . However , the results from scenario 2 did not reveal any systematic pattern between rsc and spike-synchrony ( R2 = 0 . 09 , F ( 1 , 8 ) = 0 . 76 , p>0 . 05 ) ( Figure 6C , right graph ) . In fact , these data show a very narrow window of modulations in spike-synchrony ( −1 to 0 . 7 Hz; note differences in scales on both axes between left and right panel ) , which further supports the use of the jitter correction method for removing spurious spike-synchrony activity due to slow co-variation signals . Taken together , these data indicate that attention effects on the spike-synchrony and rsc observed in feature selective neurons might be mediated by an external neural population that induced coincident spikes across the feature selective neural set . Finally , we examined the relation between FR and spike-synchrony , both of which showed feature selective attention effects in the expected direction , and behavior . Correct and incorrect trials were sorted as a function of FR and jitter-corrected spike-synchrony separately ( see [36] , [37] for a similar analysis ) . The sorted data were divided into five bins of equal sizes to reduce the effects of outliers , and the percentage of correct trials within each bin was calculated . This procedure was performed in feature selective cells with at least 25 trials per attention condition . This resulted in 86 single neurons and 42 neural pairs for the FR and spike-synchrony analyses , respectively . Regression analyses were performed using percent correct as the response-variable and the neurophysiology as predictor ( FR or synchrony rate ) . The regression analyses on the FR data revealed a significant relationship with behavior when attention was directed to the preferred feature of cells ( F ( 5 , 80 ) = 4 . 19 , p<0 . 05 , R2 = 0 . 20 ) . In addition , we observed an inverse relationship between FRs and behavior when attention was directed to vision ( F ( 5 , 80 ) = 4 . 11 , p<0 . 05 , R2 = 0 . 20 ) . However , the range between the lower and higher behavior bins for both attention conditions were ∼3% , indicating that FR attention effects had a very narrow window for modulating behavior . The regression analyses on the spike-synchrony data showed a positive relationship for the attend towards the preferred feature modality ( F ( 5 , 37 ) = 3 . 17 , p<0 . 05; R2 = 0 . 29 ) and an inverse relationship for the attend towards vision conditions ( F ( 5 , 37 ) = 3 . 28 , p<0 . 05; R2 = 0 . 30; see Figure 7B ) . However , the range between the lower and higher behavior bins for both attention conditions was almost four times the range of modulation in the FR . No other significant relationships were observed . Note that negative spike-synchrony values indicate that the average synchrony in those bins was lower than the jitter-corrected spike-synchrony . To study the links between rsc and behavior we implemented a design similar to that in [11] . Briefly , for each trial we computed the FR response of a neural pair and projected that value to an “attention” axis , which was derived by drawing a line that linked the mean FR response of all correct trials of the attend visual and the attend tactile ( i . e . , orientation or frequency , separately ) . The FR of a single trial in the attend orientation or attend frequency was assigned a proximity value , which was the distance from its location on the attention axis to the mean of the attend visual and attend tactile response . If the point was closer to the mean of the attend vision condition then it was assigned a negative value . If it was closer to the attend tactile response then it was given a positive value . The behavior for each trial was then sorted with respect to the distance values , and averaged across five bins of equal size . These data were submitted to a regression analysis using distance values as the predictors and the behavior as the response variable . Only feature selective neural pairs with at least 25 trials per condition were included in the analysis . This resulted in 42 neural pairs . As shown in Figure 7C the proximity analyses failed to reveal a significant relationship with behavior for any attention condition ( p>0 . 05 ) . Note that we used attend to vision as a reference for the proximity values , thus the behavior/distance relationship for this condition was not computed .
Our data suggest that gain-related feature selection mechanisms are analogous across the visual and somatosensory systems . Similar to previous findings in visual cortex [11] , [13] , [15] , we observed that attention modulated a large set of neurons in the tactile modality according to the feature similarity gain model , whereby higher FRs were elicited when attention matched a neuron's preferred feature modality . Further , we found that attention effects in somatosensory cortex are stimulus specific with greater modulations on the preferred versus non-preferred stimulus value ( a measure of attention effects within a feature modality ) . However , we found that these feature-specific attention effects did not scale with the FMSI of neurons ( a measure of attention effects between feature modalities ) . This finding suggests that attention modulation is more pronounced within versus between feature modalities ( e . g . , 45° versus 135° in the orientation modality as compared to orientation versus frequency modalities ) . Taken together , our results indicate that attention biases the activity of the entire set of neurons selective for features in the attended modality , but these effects may be further enhanced within the sub-population encoding the relevant stimulus values of the task . That both tactile and visual sensory systems are governed by similar feature-based attention mechanisms promotes the hypothesis that feature selection is controlled by a common set of feature-specific neural areas , whose neurons encode similar stimulus features across the senses ( e . g . , oriented stimuli in vision and touch ) . The neural areas containing these putative cross-modal feature selective neurons are unknown , but the lateral prefrontal and lateral intra-parietal cortices are likely candidates since they engage in top-down attention and encode inputs from multiple sensory modalities [38] . Indeed , a recent single-unit study in non-human primates found that neurons in the prefrontal cortex encode information about the relevant stimulus in a visual discrimination task [30] . A question that merits further investigation is whether the tactile and visual systems employ similar neural mechanisms mediating other forms of stimulus selection such as spatial , temporal , and object-based attention . Our dataset showed that attention modulated spike-synchrony in a feature-specific manner , whereby higher synchrony was observed when attention matched the preferred feature modality of the neural pairs . The data showed that the magnitude of this attention effect was predictive of animals' behavior , with greater spike-synchrony associated with improved performance . Equally important , the opposite relationship was observed when attention was directed towards vision . These findings highlight the effectiveness of the attention system to enhance the neural circuits engaged in processing relevant stimuli associated with the task goals but also to suppress unrelated or distracting inputs . An important observation is that the behavioral performance of animals was not strictly contingent on the amount of spike-synchrony in the population . As Figure 7B shows , even in the absence of spike-synchronous events ( see e . g . , the first bin that shows spike-synchrony below chance levels ) , the behavioral performance was well-above chance , indicating that additional mechanisms may mediate behavior ( possibly those mechanisms reducing rsc as previous studies show [11] ) . An alternate explanation is that we only record from a subset of all neurons in various areas of the brain that lead to the animal's behavior . It would be interesting to assess , as Cohen and Maunsell found for rsc [11] , whether spike-synchrony attention effects and their relationship to behavior are better accounted for by increasing the pool of neurons exhibiting synchronous spikes . Unfortunately , we are not able to answer this question because our experimental setup very rarely allowed us to record activity from more than two neurons at the same time . Attention has been shown to decrease rsc when it is apportioned to the relevant spatial location of visual stimuli [39] . Our data partially support these findings by showing that rsc between feature selective SII cells was increased when animals performed the visual task . Unexpectedly , our data also revealed increased rsc when attention was directed towards the preferred feature of neural pairs . This finding is inconsistent with results in V4 reported in [11] , which found reductions in rsc in neurons that displayed feature-specific FR effects . Specifically , the authors reported an inverse relationship between attention effects on rsc and FR , with greater FR attention modulations associated with lower rsc . This pattern of effects led the authors to conclude that attention decreases rsc in neural pairs whose tuning matches the attended feature , a conclusion that does not align with our findings . A putative factor underlying differences between the studies may be in the definition of feature selective neurons . Cohen and Maunsell [11] determined feature preference based on the effect that attention had on their FR , whereby greater FR attention effects to a particular feature ( e . g . , orientation ) implied neural selectivity for that feature . We , on the other hand , defined neural feature selectivity , independently of attention , based on the neuron's responses to a collection of orientation and frequency stimuli presented during sessions where animals were not performing a tactile attention task . Another possibility leading to differences is that we only analyzed feature attention effects on fully isolated and well-characterized single-units whereas Cohen and Maunsell [11] analyzed these effects in the pooled activity of both single and multiunit activity . The increase in rsc when attention matched the preferred feature modality of neural pairs motivates the following question: if rsc reflect “noisy” and redundant information , then why would attention increase the noise across neurons that encode relevant features of stimuli ? We surmised that because spike-synchrony is also a correlation mechanism , the enhanced synchrony rates observed in the same neural population might underlie the increases in rsc . Experimental observations and simulation analyses provide evidence for this hypothesis ( see Figures 6A and 6C ) . The data showed that increases in rsc coincided with enhancements in spike-synchrony when attention matched the feature preference of cells . These results are in agreement with those by Bair and colleagues [28] who showed a similar linear relationship in MT cells , in non-human primates engaged in a motion discrimination task . The question emerges then , what neural mechanism ( s ) gave rise to the pattern of attention effects in spike-synchrony and rsc ? A putative hypothesis , which is supported by our numerical simulations , is that these effects were driven by a neural population , likely residing in higher-order cortical areas that caused transient but temporally coincident spikes across feature selective cells in SII cortex . Indeed , the addition of common spikes to a population would result in enhanced spike-synchrony because these induced APs would occur almost at the same time across the entire neural cohort . But , in addition , these common spikes would produce increases in rsc because the amount of added APs would co-vary across the population on a trial-by-trial basis . We note that this hypothesis is speculative , and although our simulations provide support for it , more rigorous physiological studies are needed . It is important to note that we are not claiming that spike-synchrony gives rise to correlated noise activity ( i . e . , rsc ) . Rather , our explanation is that because spike-synchrony and rsc measure correlated spiking activity using similar mathematical operations ( see equations in Materials and Methods section ) , enhancements in spike-synchrony will lead to increases in rsc . However , the opposite is not always the case . That is , enhancements in rsc can occur in the absence of spike-synchrony . This is highlighted in our dataset , which revealed increased rsc without enhancements in spike synchrony during attend visual trials ( Figure 5B ) , and it is also observed in Cohen and Maunsell [11] . This pattern argues in favor of spike-synchrony and rsc being independent temporal correlation mechanisms . However , a key element in this proposal is that the correlated spikes reflecting rsc must occur during time windows wider than those defined for spike-synchrony ( i . e . , >±2 ms ) . That is , the correlated spikes within a trial must occur asynchronously . As suggested by the study of Cohen and Maunsell [9] , alpha-band ( 8–14 Hz ) oscillations related to sensory suppression may have caused the reductions in rsc observed in our and their studies . This neural mechanism is thought to index suppression of activity in neurons encoding distracting inputs over relatively broad timescales [16] , [36] , [37] , [40]–[43] . In summary , our findings show that attention only decreased rsc when it was deployed away from vision . That is , it did not decrease rsc in a feature-specific manner . However , this does not imply that rsc are inconsequential for facilitating feature selection . In fact , studies by Cohen and colleagues elegantly show the opposite [9] , [10] , [11] , [44] . These studies report strong links between reductions in rsc and behavioral performance at the single trial level . One reason for our failure to reveal a link between rsc and behavior may be due to limitations in our experimental setup to simultaneously record large samples of neurons . As reported in [11] , the ability to predict behavior based on rsc depends on the number of neurons used in the analyses . These authors showed that simulations with <∼five neurons yields very poor predictions of behavior ( ∼50% ) , but this ability substantially increases with larger sample populations , with an asymptote at ∼80 neurons . Unfortunately , the vast majority of time our recording paradigm only allowed us to record from two cells at a time . Studies in the visual system , as well as our own dataset , indicate that feature-based attention operates by increasing the FR responses of neurons when attention matches their preferred stimulus feature [13] , [14] . This mechanism is known as the feature similarity gain model of attention . However , while this model is a reliable predictor of gain-related attention effects in single cells , there are considerable disadvantages in employing attention mechanisms solely based on gain modulations . Because mean-rate codes encode the physical attributes of sensory stimuli ( e . g . , intensity or brightness ) and neurons FRs are also modulated by attention , together they produce non-unique solutions to different combinations of stimulus features and attention conditions . This is illustrated in the following example: if a neural population's FR co-varies with both stimulus intensity ( e . g . , contrast , sound amplitude , or skin indentation ) and attention , how does the nervous system dissociate between a strong-unattended and a weak-attended stimulus [45] ? This ambiguity problem suggests that feature selection may rely on additional neural mechanisms that do not interfere with codes representing the sensory characteristics of stimuli , such as temporal correlation codes ( see [46]–[48] for alternative explanations on visual stimuli with different contrasts ) . Indeed , our dataset revealed that attention enhanced spike-synchrony when it matched the preferred feature of neurons and decreased rsc when it was directed away from vision . This pattern of effects suggests that selective attention implements multiple mechanisms to mediate feature selection . We posit that attention operates by suppressing the background and “correlated” population noise while enhancing the synchronous activity across the neural cohort encoding the relevant features of sensory stimuli . We postulate that these mechanisms may operate in parallel and in concert , with suppression of correlated noise underlying enhancements in focused attention ( i . e . , spatial attention in vision or somatotopic attention in touch ) as suggested by Mitchell and colleagues [12] . We devised a model that may account for the attention effects observed in the spike-synchrony and rsc data . Figure 8 shows a diagram of a neural population in sensory cortex ( e . g . , SII ) that is composed of neurons selective for different stimulus features ( e . g . , orientation , frequency , motion ) and have different spatial or somatotopic RFs ( e . g . , upper left visual field or digit 3 on the hand , respectively ) . The feature selectivity of each neuron is indicated by their color while its RF property is depicted by the box that encases it . The model proposes that all neurons selective for feature “X” receive inputs from a source that is selective for the same feature ( Figure 8 , upper green ellipse ) , regardless of whether they share the same spatial or somatotopic RF . Further , the five most inner neurons in sensory cortex receive inputs from a source that has a common RF , but it is not selective for an individual feature ( Figure 8 , lower gray ellipse ) . This neural population adds the same amount of spikes to each sensory neuron inside the center rectangle , which underlie correlated “noise . ” The model contends that when attention is directed to feature “X” and to the location encoded by neurons in the center rectangle , feature-attention activates the neural population inside the green ellipse , indicated by the additive symbol inside the blue circle , which causes coincident APs in all green colored sensory neurons . In parallel , somatotopic- or spatial-attention suppresses the activity of the neural population inside the gray ellipse , indicated by the minus symbol inside the red circle , which effectively decreases the “correlated noise” added to the sensory neurons inside the center rectangle . This sequence of events leads to green colored neurons inside the center rectangle to exhibit increased spike-synchrony , and as a result , increased rsc ( see explanation above ) . This prediction is supported by our dataset , which shows increases in spike-synchrony and rsc when attention is directed towards the preferred feature of cells . Importantly however , the model also predicts that neurons selective for feature “Y” ( i . e . , orange colored neurons ) located inside the center rectangle would exhibit reduced rsc without increases in spike-synchrony . This pattern is consistent with our findings of decreased rsc in the absence of spike-synchrony enhancements when attention is directed away from the preferred feature of cells ( see Figure 5B , middle panel , and example neurons in Figure 4A ) . Finally , when directing attention away from stimuli encoded by neurons inside the center rectangle , the model predicts that these cells would show increased rsc in the absence of spike-synchrony enhancements . This pattern is consistent with our data showing increased rsc without enhanced spike-synchrony when attention is directed towards the visual modality ( Figure 5 ) . This model can also account for feature-attention effects in the FR of single cells , in that higher FRs are expected when attention matches the cell's feature selectivity , regardless of their RF location . In particular , the model would predict that when attention is deployed to feature “X” in the location encoded by neurons in the center rectangle , cells selective for feature “X” with RF in the flanking rectangles would exhibit increased FR as compared to cells selective for “Y” and “Z” with the same RFs . Indeed , a similar pattern of activity has been observed in MT cells in non-human primates engaged in a motion discrimination task [13] , whereby higher FR activity was observed in cells whose preferred motion direction matched the attended direction regardless of whether spatial attention was directed to the cells' RF . Although this model accounts well for the findings observed in our study and others , additional experiments are needed to validate its intricacies by testing whether attention biases the activity of the external populations depicted in the green and gray ellipses in the predicted manner .
All animal surgeries were performed under sterile conditions and during anesthesia . Further , all surgical and experimental procedures were approved by the internal review board ( IRB ) and the animal care and use committee ( ACUC ) of the Johns Hopkins University . Standard operant conditioning procedures were employed , whereby each animal was rewarded with drops of water or juice for every correct response . The animal's health was monitored daily by the experimenters and approximately every 2 weeks by trained veterinarians and other staff . All procedures that might have produced pain or distress were minimized . Single unit ( SU ) responses were obtained from the hand regions of SII from five hemispheres in three male rhesus ( Macaca mulatta ) monkeys ( average weight 6 . 73 , 5 . 14 , and 4 . 5 kg ) . Each monkey was trained to perform a visual and tactile discrimination task . The tactile stimulus consisted of two perpendicular bars ( 90° apart ) . Each bar was independently controlled by a linear motor that controlled the stimulus bar's vertical displacement and vibrating frequency ( animals 1 and 2 only ) . The tactile bar presented to animal 3 did not vibrate , and was controlled by two motors that rotated and indented the bar to the desired angle and amount , respectively . We restricted stimulation to the distal pads of digits 2 , 3 , or 4 depending on the RF of the recorded neuron . The vast majority of neurons had RFs that span multiple digits , and sometimes the entire hand [49] . For these neurons , the stimulator was placed on the distal pad that evoked the largest firing activity ( i . e . , the hotspot ) . For animals 1 and 2 the orientation of the bars was either 45° or 135° relative to the long axis of the finger and the vibration frequency was either 10 or 40 Hz . All combinations of vibration frequencies and orientations were presented with equal likelihood . To compensate for differences in stimulus perceptibility due to differences in intensity [50] , the 10-Hz vibrating stimulus was presented with amplitudes of either 150 or 300 microns , while stimuli vibrating at 40 Hz were presented with amplitudes of either 30 or 60 microns . These frequency/amplitude combinations lie along the same iso-intensity discrimination functions of humans [51] . On the basis of the similarities in the frequency/amplitude neural threshold curves between humans and rhesus macaques [52]–[54] , we argued that the animal's successful performance on the frequency task was achieved by focusing on the vibrating feature of the sensory stimulus as opposed to its indentation amplitude . For animal 3 oriented bars with angles ranging from 0° to 157 . 5° , relative to the long axis of the finger , were presented . All stimuli were presented for a period of 500 ms with an on/off ramp of 25 ms . The animal was seated in a comfortable chair with the head restrained . The animal's hands were supinated and restrained throughout the recording session . The sequence of events in a typical trial for this animal is illustrated in Figure 1B . A trial began with an oriented bar indented on one of the animal's distal fingerpad for 500 ms ( 0° to 157 . 5° , in steps of 22 . 5° ) . After a delay period of 900 ms a second oriented bar was indented on the same fingerpad and with the same duration . The second stimulus had the same or an orthogonal orientation ( i . e . , 90° difference ) to the first stimulus . In the attend orientation trials , the animal pressed a foot switch in the forward or backward direction if the stimuli had the same or different orientation , respectively . In attend visual trials , the animal experienced the same tactile stimulation , but it was trained to press the foot switch when a white square ( 2° visual angle ) , which was continuously presented on the screen , was dimmed . A drop of liquid was given for every correct trial . This animal performed the tactile and visual trials on separate blocks , and this was cued by changing the pattern on the screen from an illuminated square ( visual task ) to a blank screen ( tactile task ) . Separate blocks of trials were run to characterize a neuron's orientation and frequency selectivity to tactile features . During these trials the animal sat quietly while receiving drops of water at random intervals . Trials within each block were randomly presented . Tactile stimuli in both of these blocks were presented for 500 ms with an on/off ramp of 25 ms , and the inter-stimulus-interval set to 500 ms . A total of 297 neurons were recorded from all animals . Neurons with mean rate <5 Hz across all attention blocks were discarded and only neurons that had at least eight valid trials per condition were analyzed . Further , only neurons that contained a full balanced dataset of experimental conditions ( i . e . , all feature characterization and attention protocols within each experiment ) were included in the analyses . This led to the exclusion of 92 neurons , leaving 205 neurons ( animal 1 = 89 , animal 2 = 40 , and animal 3 = 76 ) and 122 neural pairs retained for analyses . An important requirement of our experiment was to record neural activity from multiple well-isolated single neurons with specific tuning characteristics at the same time . This precluded us from recording simultaneous activity from a large sample of neural pairs , as previous studies have done using chronically implanted micro-electrode arrays ( e . g . , [9]–[11] ) . Our primary objective was to investigate how feature-based attention modulates activity of feature selective neurons . To this end , most analyses were performed on neurons that had preference for a particular tactile feature . This resulted in the analysis of 128 single neurons ( 65 orientation-selective only , 30 frequency-selective only , 39 selective for both types of tactile-features ) and 57 neural pairs . Neurons that were selective for both types of tactile-features were discarded from all analyses of attention effects . | Attention can select stimuli in space based on the stimulus features most relevant for a task . Attention effects have been linked to several important phenomena such as modulations in neuronal spiking rate ( i . e . , the average number of spikes per unit time ) and spike-spike synchrony between neurons . Attention has also been associated with spike count correlations , a measure that is thought to reflect correlated noise in the population of neurons . Here , we studied whether feature-based attention biases the correlated activity between neurons when attention is directed towards their common preferred feature . Simultaneous single-unit recordings were obtained from multiple neurons in secondary somatosensory cortex in non-human primates performing feature-attention tasks . Both firing rate and spike-synchrony were enhanced when attention was directed towards the preferred feature of cells . However , attention effects on spike-synchrony had a tighter relationship with behavior . Further , attention decreased spike-count correlations when it was directed towards the receptive field of cells . Our data indicate that temporal correlation codes play a role in mediating feature selection , and are consistent with a feature-based selection model that operates by reducing the overall noise in a population and synchronizing activity across subpopulations that encode the relevant features of sensory stimuli . | [
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"... | 2014 | Temporal Correlation Mechanisms and Their Role in Feature Selection: A Single-Unit Study in Primate Somatosensory Cortex |
Bats harbor many viruses , which are periodically transmitted to humans resulting in outbreaks of disease ( e . g . , Ebola , SARS-CoV ) . Recently , influenza virus-like sequences were identified in bats; however , the viruses could not be cultured . This discovery aroused great interest in understanding the evolutionary history and pandemic potential of bat-influenza . Using synthetic genomics , we were unable to rescue the wild type bat virus , but could rescue a modified bat-influenza virus that had the HA and NA coding regions replaced with those of A/PR/8/1934 ( H1N1 ) . This modified bat-influenza virus replicated efficiently in vitro and in mice , resulting in severe disease . Additional studies using a bat-influenza virus that had the HA and NA of A/swine/Texas/4199-2/1998 ( H3N2 ) showed that the PR8 HA and NA contributed to the pathogenicity in mice . Unlike other influenza viruses , engineering truncations hypothesized to reduce interferon antagonism into the NS1 protein didn't attenuate bat-influenza . In contrast , substitution of a putative virulence mutation from the bat-influenza PB2 significantly attenuated the virus in mice and introduction of a putative virulence mutation increased its pathogenicity . Mini-genome replication studies and virus reassortment experiments demonstrated that bat-influenza has very limited genetic and protein compatibility with Type A or Type B influenza viruses , yet it readily reassorts with another divergent bat-influenza virus , suggesting that the bat-influenza lineage may represent a new Genus/Species within the Orthomyxoviridae family . Collectively , our data indicate that the bat-influenza viruses recently identified are authentic viruses that pose little , if any , pandemic threat to humans; however , they provide new insights into the evolution and basic biology of influenza viruses .
Bats are present throughout most of the world and account for more than a fifth of mammalian species . They are natural reservoirs of some of the most deadly zoonotic viruses , including rabies virus , Ebola virus , Henipaviruses , and SARS coronavirus [1] , [2] . Recently , nucleic acids obtained from bat samples indicated bats may be a reservoir of a new group of influenza viruses ( bat-influenza ) that are phylogenetically very distantly related to other influenza viruses [3] , [4] . Type A , B , and C influenza viruses belong to the Orthomyxoviridae family and their genomes are composed of 7–8 negative sense RNA segments ( vRNAs ) . While influenza B ( IBV ) and C viruses mainly infect human hosts , influenza A virus ( IAV ) has a broad host range; including humans , marine mammals , horses , pigs , waterfowl , and poultry . New subtypes of IAV , which have novel hemagglutinin ( HA ) and/or neuraminidase ( NA ) surface glycoproteins , are introduced into the human population by zoonosis and this periodically leads to devastating pandemics . Past pandemics include the “Spanish flu” ( H1N1 ) in 1918 , “Asian flu” ( H2N2 ) in 1957 , “Hong Kong flu” ( H3N2 ) in 1968 , “Russian flu” ( H1N1 ) in 1977 , and the recent “swine origin” flu ( pH1N1 ) in 2009 . Pandemic viruses are often reassortant viruses composed of vRNAs that are derived from multiple IAV lineages that previously circulated in swine and/or avian reservoirs ( e . g . , 1957 avian-human reassortant , 1968 avian-human reassortant , and 2009 avian-swine-human reassortant ) . The discovery of putative bat-influenza viruses expands the known host species reservoirs that may serve as a source of novel viruses , which is a major concern for public and animal health [4] , [5] . Infectious bat-influenza viruses couldn't be isolated [3] , [4] and although several structural and biochemical characterization studies have been conducted with the putative bat-influenza HA , NA , and part of PA , none of the vRNAs have been shown to be functional in a replicative virus [3] , [4] , [6]–[10] . This has led to questions such as: ( 1 ) are the putative bat-influenza vRNA sequences identified derived from infectious viruses or are they merely nucleic acid relics harbored in bats [5] , ( 2 ) are the vRNA segments sequenced from a single bat-influenza virus or are they from multiple potentially incompatible viruses , and ( 3 ) were the sequences of the complete gene segments , which is a significant technical challenge , determined accurately . The inability to culture infectious viruses is the major hurdle to confirm the existence of these novel influenza viruses , and to answer important questions , such as pathogenicity in animal models , ability to reassort with other influenza viruses , and their potential risk to public health [5] , [11] . The goals of this study were to synthesize the complete viral genome , characterize the bat-influenza virus using non-infectious approaches , then generate a replicative virus , and use it as a model to better understand bat-influenza viruses .
Lack of infectious particles in the original bat specimens is a potential factor in the inability to isolate/culture bat-influenza using multiple host cell substrates [3] . Based on digital sequence information published by Tong et al . [3] , we synthesized the complete genome of A/little yellow-shouldered bat/Guatemala/164/2009 ( H17N10 ) ( Fig . S1 ) and cloned it into reverse genetics plasmids to rescue this putative bat-influenza virus ( Bat09 ) . Thousands of spherical influenza-like particles budded into the supernatants of human cells ( 293T ) transfected with the Bat09 reverse genetics plasmids ( Fig . 1A ) . The supernatants were inoculated into embryonated chicken eggs and cell lines derived from many species ( canine ( MDCK ) , mink ( Mv1-Lu ) , swine ( ST ) , African green monkey ( Vero ) , human ( A549 , Calu-3 ) , and free-tailed bat ( Tadarida brasiliensis , Tb1Lu ) ; however , none of the host cell substrates tested supported productive virus infection ( determined by serial passage and subsequent real-time RT-PCR ) . Previous biochemical and structural studies with purified proteins of Bat09 hemagglutinin ( HA ) and neuraminidase ( NA ) indicate that the HA doesn't bind to canonical sialic acid receptors of influenza viruses and the NA doesn't have neuraminidase activity , which is characteristic of IAV and IBV NAs [6]–[9] . To further examine if the HA and NA proteins are the major blocks to the propagation of the Bat09 virus , we attempted to rescue reassortant viruses that contained the 6 internal protein coding vRNAs ( PB2 , PB1 , PA , NP , M , and NS ) from Bat09 and the surface glycoprotein vRNAs ( HA and/or NA ) from a recombinant A/Puerto Rico/8/1934 ( PR8 ) . PR8 is a lab adapted H1N1 virus that has been used for many years in research and vaccine settings because it replicates efficiently in embryonated chicken eggs , cell lines ( e . g . , MDCK ) and in the mice , but has low risk to humans . However , the three PR8-HA/NA reassortant genotypes containing the Bat09 internal protein vRNAs couldn't be rescued following transfection ( Fig . 1B ) . While the Bat09 internal protein/vRNAs are capable of generating proteins and producing influenza-like particles , they may have critical mutations that were inhibiting infectivity , or they can't cooperate efficiently with the PR8-HA/NA proteins/vRNAs . To further address the inability to rescue Bat09 or the Bat09:PR8-HA/NA reassortants , we created a modified HA vRNA ( mH1 ) that contained the protein coding region from PR8-H1 flanked by putative cis-acting terminal packaging signals from Bat09 that we hypothesized would be similar to the regions known to be central to packaging of A/WSN/1933 and PR8 [12] , [13] ( Fig . 1C and Fig . S2 ) . The Bat09 NA gene segment was modified using a similar strategy to replace the NA coding region with PR8-N1 , while the putative bat NA packaging signals were retained ( mN1 ) ( Fig . 1C and Fig . S2 ) . Co-expression of the mH1 and mN1 vRNAs with the six Bat09 internal protein vRNAs efficiently rescued a reassortant Bat09:mH1mN1 virus ( Fig . 1B ) . The reassortant Bat09:mH1mN1 formed particles similar to that of Bat09 ( Fig . 1A ) and replicated robustly in vitro and in ovo ( Fig . 1D , 1E ) . Next generation sequencing demonstrated that the consensus sequence of the virus stocks from 1 passage in MDCK cells or embryonated chicken eggs was identical to that of the reverse genetics plasmids . Furthermore , even after 3 passages in MDCK cells , we still didn't identify any nucleotide polymorphisms accounting for >10% of the genomic population that would suggest strong selective pressure on Bat09 genes or the modified HA/NA genes of PR8 . To investigate whether Bat09:mH1mN1 is able to infect and replicate in mice , a mouse study was performed using the mouse adapted PR8 IAV as a positive control . Bat09:mH1mN1 replicated efficiently in mouse lungs ( Fig . 2A ) , and caused significant weight loss as early as at 4 days post inoculation ( 4 dpi ) ( Fig . 2B ) . The virulence of Bat09:mH1mN1 ( 75% mortality ) was close to that of the PR8 virus ( 100% mortality ) ( Fig . 2C ) . Histopathological analysis showed that the Bat09:mH1mN1 virus caused typical influenza-like lesions characterized by a varying degree of broncho-alveolar epithelial degeneration and necrosis , and interstitial pneumonia . The peribronchiolar and perivascular areas were infiltrated by moderate numbers of lymphocytes and plasma cells ( Fig . 2D ) . The histopathology identified correlates with presence of virus antigen in the mouse lungs ( Fig . 2E ) . Next generation sequencing was used to determine if the Bat09 vRNAs were genetically stable in mice . Although nucleotide polymorphisms ( at the level of 12%–36% ) were detected at sporadic loci throughout the Bat09 vRNAs , each lung sample only had one such polymorphism on average , and none of the mutations were found in more than one mouse . Nonetheless , serial passage of this virus in mice may identify mutations in the Bat09 backbone critical to replication/pathogenesis in mice . We did identify a low level nucleotide polymorphism in the modified PR8 HA at residue at 187 that emerged in multiple Bat09:mH1mN1 inoculated mouse lung samples collected at 3 and 5 dpi ( HA-K187E , 10%–20% of the genomic population ) . This unanticipated result may have also occurred in PR8 inoculated mice; however the lung specimens from these mice were not sequenced . The virulence of the Bat09:mH1mN1 in mice could partly result from the H1 and N1 of the mouse adapted PR8 virus . To further investigate pathogenicity of Bat09-like viruses we rescued another modified Bat09 virus that expresses H3N2 surface glycoproteins from A/swine/Texas/4199-2/1998 ( H3N2 ) ( TX98 ) , which we have used in pigs previously [14] . The HA/NA vRNAs of Bat09:mH3mN2 were modified using a similar strategy used to generate the mH1/mN1 , whereby the coding regions of Bat09 glycoproteins were replaced with TX98 H3N2 , while the putative Bat09 packaging signals were retained ( mH3/mN2 ) ( Fig . 3A ) . The rescued Bat09:mH3mN2 virus replicated to peak titers close to that of TX98 ( Fig . 3B ) and both viruses were inoculated into mice to compare the morbidity ( weight loss ) , mortality and virus replication at various times post inoculation . All mice survived infection and both viruses ( Bat09:mH3mN2 and TX98 ) caused little effect on weight gain as compared to the mock inoculated animals ( Fig . 3C ) , indicating little overall disease . Titration of virus in the lung tissues showed that the Bat09:mH3mN2 virus replicated as efficiently as the TX98 control in the mice at early time points , yet it appeared to be cleared more rapidly ( Fig . 3D ) . This data suggests that some of the pathogenicity observed in the Bat09:mH1mN1 infected mice likely results from the mouse adapted HA/NA of PR8 . However , it is clear that the bat influenza internal protein vRNAs do support replication of the modified viruses ( Bat09:mH1mN1 and Bat09:mH3mN2 ) in vitro , in ovo , and in the mouse lungs . The slightly lower replication efficiency and pathogenicity of those two viruses compared to the corresponding PR8 and TX98 viruses could be ascribable to either the nature of the Bat09 internal protein vRNAs or the engineering of the modified HAs and NAs . Bat-influenza viruses appear to have diverged from IAV a very long time ago and their internal protein vRNAs have many unique features that are not seen in IAVs [3] , [4] . Therefore , the biological roles of the various vRNA segments and their protein products are likely to have both similarities and intriguing differences . Many deadly bat viruses ( e . g . , filoviruses ) have evolved powerful molecular mechanisms that inhibit host ( e . g . , human ) immune responses [15]–[18] . Therefore , to gain an understanding of how bat-influenza viruses may evade the host innate immune response we analyzed the Bat09 NS1 protein using interferon induction experiments and carboxy-terminal truncation mutations known to attenuate IAVs . The NS1 protein of IAVs is critical for pathogenicity of many strains because of its ability to antagonize the host interferon response [19] . To compare the direct effect of Bat09-NS1 and PR8-NS1 on interferon-β production , we expressed the proteins ectopically in human HEK-293T and then infected them with Sendai virus to stimulate the innate immune response . Activation of interferon-β promoter was determined by a luciferase mediated reporter assay [16] . Bat09-NS1 inhibited host interferon-β induction comparable to that of the PR8-NS1 , and carboxy-terminal truncation of Bat-NS1 protein ( NS1-128 and NS1-73 , see Fig . S2C for diagram ) decreased its ability to inhibit interferon-β production ( Fig . 4A ) . These results are consistent with the attenuating effect that these NS1 truncations have on PR8 ( Fig . 4A ) and other IAV NS1 proteins; thereby , providing a strategy to generate live attenuated influenza vaccines [14] , [20]–[22] . A VSV-luciferase virus mediated bioassay was also performed to compare the effect the NS1 truncations have on the Bat09 viruses' ability to inhibit host innate immune response [22] . The replication of the VSV-luciferase virus , which is sensitive to innate immune activation , is inversely correlated with type I interferon induced by influenza virus . Truncation of the Bat09-NS1 modestly reduced VSV replication , whereas truncation of the PR8-NS1 severely inhibited VSV replication ( i . e . , luciferase expression ) ( Fig . 4B ) . These results were confirmed by analysis of influenza virus replication kinetics in a human lung epithelial cell line ( Fig . 4C ) . The Bat09-NS1 truncated viruses ( Bat09:mH1mN1ss-NS1-128 and Bat09:mH1mN1ss-NS1-73 ) replicated to titers of 106–107 TCID50/ml ( near wild type NS1; Bat09:mH1mN1ss ) , whereas the PR8-NS1 truncation mutants had 100–1000 fold lower titers than PR8 ( Fig . 4C , Fig . S2 for gene and virus diagrams ) . To analyze the impact of these Bat NS1 truncation mutations in vivo we inoculated mice with the same panel of modified Bat09 viruses , or the PR8-NS1-126 as a control . In contrast to the significant attenuation conferred by the truncated NS1 in PR8 ( PR8-NS1-126 ) , recombinant bat-influenza viruses with truncated NS1 genes ( Bat09:mH1mN1ss-NS1-128 and Bat09:mH1mN1ss-NS1-73 ) replicated efficiently in the lungs ( Fig . 5A ) , caused significant morbidity ( Fig . 5B ) , and remained 100% lethal in mice ( Fig . 5C ) . Altogether the NS1 studies show that the Bat09 NS1 protein inhibits host interferon-β production and carboxy-terminal truncation mutations reduce its ability to antagonize this response , likely through mechanisms similar to IAV ( Fig . 4A ) . However , in contrast to IAV , truncation ( NS1-128 , NS1-73 ) of the Bat09 NS1 didn't dramatically impact the viruses' ability to antagonize the host innate response , or replicate and cause disease in mice ( Fig . 4B , C and Fig . 5 ) . We analyzed the Bat09 PB2 gene because of its central role in the species specificity of IAVs , and some of the critical residues involved are known to be virulence determinants in mice and ferrets [23]–[28] . Asparagine ( N ) 701 in the PB2 protein is a mammalian-signature in IAVs and when this residue was mutated to aspartic acid ( D , an avian-signature ) in the modified Bat09 ( Bat-701D ) , it decreased virus titers in lungs , morbidity ( minor weight loss ) , and resulted in 100% survival ( Fig . 6 ) . The bat-influenza PB2 also has a serine ( S ) residue at position 627 , which is unlike either mammalian or avian IAVs . Replacing the serine 627 with the mammalian-signature residue lysine ( K ) [24] , [27] in the context of 701D ( Bat-627K/701D ) increased virus replication in the lungs but only caused slightly more weight loss ( compared to the Bat-701D virus ) and it remained attenuated in mice ( Fig . 6 ) . In contrast , introducing another virulence marker PB2-E158G [23] into the PB2-N701D virus ( Bat-158G/701D ) dramatically increased the pathogenicity of the Bat09 virus ( 100% mortality ) , which was higher than the Bat09 virus with wild type PB2 ( Bat09:mH1mN1 , Fig . 6 ) . In addition , introducing the PB2-E158G ( Bat-158G ) into the wild type PB2 resulted the most significant increase of virus replication , morbidity , and mortality ( Fig . 6 ) , indicating there is an additive effect between the two virulence determinants ( PB2-158G and PB2-701N ) in the Bat09 PB2 . All viruses collected from mouse lungs were deep sequenced to confirm the stability of the engineered mutations and although sporadic nucleotide polymorphisms ( 10% - 44% ) were detected in the viral genomes ( 1 to 2 such polymorphisms per mouse sample on average ) , none of them occurred at the engineered loci . The high genetic stability of the modified Bat09 viruses in mice is consistent with the notion that the bat influenza viruses are mammalian viruses that have been evolving and adapting in the bats for a long period of time . To determine the molecular basis for the altered pathogenicity imparted by the various mutations in the PB2 we examined their effects on the viral polymerase activity in human 293T cells using a luciferase-mediated mini-genome replication assay ( Fig . 7 ) . At all temperatures tested , the PB2-N701D mutation decreased the polymerase activity and the PB2-E158G mutation enhanced the polymerase activity , consistent with the decreased and increased pathogenicity in mice , respectively ( Fig . 6 ) . Interestingly , the PB2-627S showed intermediate polymerase activity compared to the PB2-627K and PB2-627E ( Fig . 7 ) . In addition , the polymerase activity of the PB2-158G and PB2-627E/K mutants decreased proportionally when they were combined with the PB2-701D mutation ( Fig . 7 ) . This result is consistent with the observation that Bat-158G/701D appeared to be less pathogenic than the Bat-158G virus ( Fig . 6 ) . Collectively , the data collected on the Bat09 PB2 show that amino acid residues known to be important in replication , species specificity , transmission , and/or pathogenesis of IAV are important in the replication and pathogenesis of Bat09 . Reassortment of IAVs is important in the evolution of IAVs and generation of panzootic and pandemic strains . Furthermore , efficient replication of bat-influenza internal protein vRNAs in human cells and mice , as well as their pathogenicity , necessitated an assessment of reassortment potential between Bat09 and other influenza viruses . Replication of vRNAs from different parental viruses is a factor critical in the generation of reassortant progeny . Transcription/replication of mini-genome reporter constructs showed that the viral RNA dependent RNA polymerase ( RdRp ) , which is a heterotrimer of PB1 , PB2 , and PA , from bat-influenza , IAVs , and IBVs generally recognize and transcribe their cognate vRNAs more efficiently than non-cognate vRNAs ( Fig . S3 ) . Intriguingly , the Bat09 polymerase replicated the IBV reporter very efficiently ( Fig . S3 ) . Additionally , most RdRp combinations ( PB2 , PB1 , PA ) between bat-influenza and IAVs nearly abolished the polymerase activity in this very sensitive mini-genome reporter assay ( Fig . 8A–I ) . Interestingly , the NP protein , which is a single-strand RNA-binding nucleoprotein , is completely compatible between Bat09 and IAVs ( Fig . 8A–I ) , but it is incompatible between the bat-influenza and IBV ( Fig . 8J ) . Although some gene segment combinations showed limited polymerase activity in the mini-genome assays , we couldn't generate any reassortant viruses using reverse genetics between Bat09:mH1mN1 and PR8 that contain partly compatible RdRp components ( e . g . , Bat-PB2/PR8-PB1/PR8-PA ) , including the highly compatible NP vRNA/protein ( Table 1 and Table 2 ) . Instead , the PR8-M segment could unidirectionally substitute for the Bat09-M segment ( Table 2 ) . This likely results from the highly conserved nature of the M vRNA and proteins ( M1 , M2 ) . Swapping the putative cis-acting packaging signals of the Bat-NP and known packaging signals of the PR8-NP , or between the Bat-NS and PR8-NS didn't enable rescue of viruses containing either the NP or NS vRNAs in a heterologous virus background ( Table 3 and see Fig . S2 for diagrams ) . Low efficiency of packaging at least some vRNA segments from the heterologous virus is also a major restrictive factor for reassortment . For instance , a reassortant virus containing six internal protein vRNAs from Bat09 and the HA and NA from PR8 couldn't be rescued , whereas the PR8 HA and NA coding regions flanked by Bat09 packaging regions ( mH1 and mN1 ) can efficiently reassort with the Bat09 internal genes ( Fig . 1B and Table 4 ) . Nevertheless , PR8 HA and NA can individually reassort ( 7∶1 ) with the Bat09 six internal protein vRNAs when mN1 or mH1 were provided , respectively ( Table 4 ) . The inability to rescue the 6∶2 reassortant Bat09:PR8-H1N1 virus may result from compounding the low efficiency of packaging for each of the wild type PR8-HA and PR8-NA vRNAs into the bat-influenza backbone . The mN1 can also reassort with the other seven segments from PR8 , even when many silent substitutions ( ss ) were introduced into the N1 coding regions to disrupt the remaining PR8 packaging signals ( Table 4 ) . Actually , another modified NA that contains the coding region from IBV NA flanked by the putative packaging region of the Bat09-NA ( Bat-N10ps-FluB-NA ) can also be rescued in the PR8 background , strongly suggesting that the bat-influenza NA segment could be efficiently packaged into the PR8 virus , whereas the Bat09 HA packaging signal didn't mediate efficient packaging of the mH1 into the PR8 backbone ( Table 4 ) . While the generation of reassortants through plasmid-based reverse genetics is a powerful and sensitive way to rescue influenza viruses , it's difficult to generate every possible gene constellation and accompanying minor nucleotide variations that could give rise to progeny reassortants during co-infection . Therefore , we attempted to generate reassortants between a modified Bat09 virus and PR8 using a classical co-infection approach . However , when MDCK cells were inoculated at a high multiplicity of infection ( MOI ) with both PR8 and Bat09:mH1mN1 viruses , reassortment between the two parental viruses was not detected . We plaque purified 118 progeny viruses from the co-infection and 53 of them were the parental PR8 virus and 65 of them were the parental Bat09:mH1mN1 virus . Although more exhaustive classical reassortant studies are needed to completely evaluate the generation of natural reassortants between these viruses , the data indicate that PR8 and Bat09:mH1mN1 don't efficiently reassort . Recently , another bat-influenza virus A/flat-faced bat/Peru/033/2010 ( H18N11 ) ( Bat10 ) was identified in Peru and phylogenetic analysis indicated this virus diverged from the bat-influenza viruses identified in Guatemala ( e . g . , Bat09 ) so long ago that genetic diversity between these two bat-influenza viruses is higher than that of IAVs [4] . Reassortment of the PB2 , PB1 , PA , and NP segments in mini-genome polymerase activity assay demonstrated that the Bat09 and Bat10 viruses were fully compatible ( Fig . 8K ) . Most importantly , successful reassortment between the two modified bat viruses ( Bat09:mH1mN1ss and Bat10:mH1mN1ss ) ( Table 5 and Fig . S2 for diagrams of constructs ) proved that these genetically divergent bat-influenza virus vRNAs were highly interchangeable , in contrast to their very low compatibility with IAV and IBV . Interestingly , classical co-infection of the Bat09:mH1mN1 and Bat10:mH1mN1 viruses in MDCK cells readily generated reassortant progeny viruses with various genotypes , and some were apparently preferentially selected ( e . g . , Bat10:Bat09-NS reassortant , Table S2 ) , demonstrating the merit of classic co-infection strategy in identification of gene constellations that may have certain advantages . Collectively the mini-genome replication , reverse genetics reassortment , and co-infection reassortment experiments strongly suggest that two divergent bat-influenza viruses readily reassort with each other , whereas they won't reassort with canonical IAVs in the natural setting .
The generation of synthetic modified bat-influenza viruses ( e . g . , Bat09:mH1mN1 ) that grow to high titers in commonly used influenza virus culture substrates and mice is an important step toward understanding these novel bat-influenza viruses . The rescue of Bat09:mH1mN1 and Bat09:mH3mN2 viruses demonstrates that the putative vRNAs of Bat09 function efficiently together and are probably derived from either one virus , or a group of compatible viruses , whose PB2 , PB1 , PA , NP , M , and NS proteins efficiently replicate and package vRNAs in host cells commonly used to culture influenza viruses ( Fig . 1 ) . Importantly , the data also shows that the bat-influenza HA and NA were the sole determinants inhibiting Bat09 virus rescue , and that the terminal regions of HA and NA of bat-influenza viruses selected for our constructs contain cis-acting vRNA packaging signals . Although wild type bat-influenza virus ( Bat09 ) couldn't be propagated in the human , canine , mink , avian , porcine or bat cell lines we tested , consistent with Tong et al . [3] , it is likely that the bat-influenza virus can infect some other cell cultures from other species and/or tissues , especially cells derived from appropriate bat species . Our Bat09:mH1mN1 studies provide other unique insights , which can't be gleaned from non-infectious assays . For instance , non-infectious assays ( interferon-β reporter assay , Fig . 4A ) showed the Bat09 NS1 carboxy-terminal truncationss ( NS1-128 and NS1-73 ) were similar to the truncated PR8 NS1 ( NS1-126 and NS1-73 ) , which largely lost the ability to inhibit the host interferon response . However , mouse experiments with the replicative bat-influenza viruses revealed that the truncation of Bat09 NS1 had minimal effects on the viral pathogenesis compared to the truncation of PR8 NS1 ( Fig . 5 ) . Differences in the attenuating impact observed in the PR8-NS1 and the Bat09-NS1 truncated viruses suggests that Bat09 has novel molecular mechanisms that have evolved in the amino terminal portion of NS1 and/or other internal protein vRNAs to antagonize/evade the host innate immune response . The PB2 of IAV plays important roles in replication , species specificity , transmission , and pathogenesis [23]–[29] . Our analysis of bat-influenza PB2 demonstrated that it is also a virulence determinant and as anticipated conversion of mammalian-signature residues at position 701 to avian-signature ( N701D ) attenuated the virus , and the E158G substitution [23] enhanced virulence . PB2-627 is one of the most studied positions differentiating avian viruses ( glutamic acid ) and mammalian viruses ( lysine ) [24] , [27] . Intriguingly , the bat-influenza PB2 has a serine at position 627 , which is unlike mammalian or avian IAVs . Our data show that PB2-627S has intermediate polymerase activity compared to PB2-627E and PB2-627K in mammalian cells , suggesting an alternative evolutionary pathway that avian influenza viruses may be able to take for mammalian adaptation . Reassortment of the segmented genomes of Orthomyxoviruses is a powerful evolutionary mechanism that is central to the success of these pathogens . Viruses within a Genus readily reassort upon co-infection of a single host cell ( e . g . , avian and swine IAV ) ; whereas , viruses from a different Genus ( e . g . , IAV and IBV ) don't reassort . The factors important for generation of reassortant progeny from two parental influenza viruses include: recognition and replication of vRNAs by parental virus RdRp , protein-protein interaction/compatibility ( e . g , heterotrimeric RdRp ) , and vRNA-protein interactions needed for virion morphogenesis . The RNA transcription/replication promoter of each influenza vRNA segment is formed by base pairing of highly conserved nucleotides at the 5′ and 3′ termini , which form a partially double-stranded structure . The IAV Genus has specific nucleotide variations within the termini that distinguish it from IBV . The termini of bat-influenza vRNAs also show conserved 5′ and 3′ complementarity; however , they also have distinct nucleotide variation . Therefore , we used mini-genome replication studies to analyze promoter recognition and RdRp activity of various combinations of the PB1 , PB2 , PA subunits in combination with various NPs from IAV , IBV , or bat-influenza . The data show that the wild type RdRp most efficiently replicate their cognate vRNAs , and that both IAV and IBV RdRp have 50–60% reduction in activity with the bat-influenza mini-genome . Many PB1 , PB2 , PA combinations between bat-influenza and IAV/IBV dramatically reduce activity , which demonstrates protein-protein incompatibility between the RdRp subunits . Interestingly , the bat-influenza NP and IAV NP were completely compatible in the mini-genome assay , however NP reassortant viruses could not be generated ( Table 2 and Table 3 ) suggesting that the incompatibility of NPs may also involve complicated protein-vRNA interactions . IAVs of various subtypes can infect and reassort in bat cell lines [30] , [31] , providing a permissive environment for them to reassort with bat-influenza viruses . However , our reassortant analysis indicates that while two divergent bat-influenzas readily reassort , bat-influenza and IAVs don't easily reassort in co-infection experiments . Reverse genetics reassortment studies showed the PB2 , PB1 , PA , NP , and NS vRNAs of bat-influenza don't efficiently reassort with the IAV or IBV , and provide many additional tantalizing results . For example , reassortants were not rescued from relatively compatible RdRp combinations in the mini-genome assay ( e . g . Bat-PB2/PR8-PB1/PR8-PA , Fig . 8A ) and demonstrate that divergent Bat09 and Bat10 can efficiently reassort with each other ( Table 5 ) . The M segment is the most highly conserved gene among influenza A and B viruses . We found that the PR8-M segment could substitute for the Bat09-M segment ( Table 2 ) , indicating that the M vRNAs/protein ( s ) of PR8 and Bat09 have enough conservation in both cis-acting packaging signals and functional domains of the proteins ( M1/M2 ) to enable the replication of the modified Bat09 virus . In contrast , putative packaging signal swapping of the NP and NS segments didn't overcome reassortment defects suggesting that incompatibility at the protein-protein or protein-vRNA level is likely to be a critical factor inhibiting reassortment between the bat-influenza and other influenza viruses . Alternatively , one could argue that that since the vRNA packaging signals of bat-influenza NP and NS segments have not been delineated , the putative packaging regions incorporated in the Batps-PR8 constructs may not be sufficient for packaging the modified vRNAs . However , the well-defined PR8 packaging signals incorporated in our modified gene segments should be sufficient to package the corresponding bat-influenza NP and NS vRNAs ( PR8ps-Bat-NP and PR8ps-Bat-NS , Fig . S2D ) in the PR8 backbone . The failure to rescue the PR8ps-Bat NP or NS viruses , as well as the PR8:Bat09-M reassortant virus , strongly suggests protein-protein or protein-vRNA level incompatibility and provides a unique opportunity to better understand the functional domains of these proteins through characterizing chimeric/mosaic proteins containing motifs/domains from both viruses . Another caveat with our bat-influenza reassortment experiments is the focus on interactions with the laboratory adapted PR8 virus , which was chosen primarily due to biosafety concerns . Reassortment between the Bat09:mH1mN1 virus and other IAVs , particularly avian viruses ( e . g . , H5N1 , H7N9 ) that appear to be more compatible in the mini-genome assay ( Fig . 8 ) , are needed to fully assess reassortment potential of bat-influenza . However , based on our results from the NP reassortment and the Bat-PB2/PR8-PB1/PR8-PA reassortment experiments ( Table 1 and Table 2 ) , the likelihood of rescuing a reassortant with RdRp components from both Bat and IAVs is very low . Finally , since the HAs and NAs of the bat influenza viruses can't be used to rescue viruses using contemporary influenza virus host substrates , we were not able to fully assess the ability of the HA or NA to reassort with other influenza viruses ( limited assessment provided in Table 4 ) . However , the known bat influenza viruses ( Bat09 , Bat10 ) could pose a pandemic threat if their HA and NA acquire mutations that impart binding to canonical influenza virus receptors and rescuing the NA for neuraminidase activity , or acquisition of binding and entry through alternative human cell surface receptors . Collectively , our experiments suggest that the bat-influenza virus is unlikely to reassort with an IAV or IBV and spread to other species even if they were to infect the same host cell . The restriction on reassortment appears to result from multiple levels of incompatibility ( RNA-RNA , RNA-protein , and/or protein-protein ) that are either additive or synergistic . Consequently , our data suggest that due to the extremely limited ability of genetic information exchange between bat-influenza and IAV or IBV , the International Committee on Taxonomy of Viruses could consider classifying these two bat-influenza virus lineages as a new Genus or Species within the Orthomyxoviridae . This study also demonstrated the power of synthetic genomics in rapid characterization and risk assessment of an emerging virus , even when the virus itself is not readily cultured . The synthetic genomics/reverse genetics strategy employed provides an infinite supply of wild type bat-influenza particles that can be used to identify permissive cells or animals . The availability of our modified bat-influenza virus , opens many other avenues of investigation and discovery , including , for instance , to gain a better understanding of cis-acting signals in the vRNAs that are important in bat-influenza transcription , replication , packaging/particle morphogenesis , and to use forward genetics to elucidate viral protein-protein and/or viral protein-host protein interactions . Finally , continued study of bat-influenza viruses and integration of data from other contemporary influenza viruses is important in the elucidation of the evolutionary history of influenza viruses .
The study was reviewed and approved by the Institutional Biosafety Committee at Kansas State University ( protocol #903 ) , and by the institutional biosafety committee at the J . Craig Venter Institute ( protocol # 3414 ) . We conducted the initial studies using PR8 gene fragments to generate the modified bat-influenza viruses and to test the reassortment potential because PR8 is a widely used lab/mouse adapted BSL2 virus that poses very low risk to humans or livestock . Subsequently , TX98 H3N2 genes were used in a few experiments because this is a BSL2 swine virus , which we have used previously and the viruses generated were considered low risk . The animal studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal protocol ( protocol #3339 ) was reviewed and approved by the Institutional Animal Care and Use Committee at Kansas State University . All animal studies were performed in a Biosafety Level 3 facility located at the Biosecurity Research Institute at Kansas State University under the approved protocol #3339 following the American Veterinary Medicine Association guidelines on euthanasia . For virus inoculation , each mouse was anesthetized by inhaling 4% isoflurane . Mice were euthanized if more than 25% of weight was lost after virus inoculation . Euthanasia of mice was conducted by inhaling 4% isoflurane followed by cardiac puncture and cervical dislocation . No survival surgery was performed , and all efforts were made to minimize suffering . Human embryonic kidney 293T ( HEK-293T ) cells , mouse rectum epithelial carcinoma ( CMT-93 ) cells , and African green monkey kidney ( Vero ) cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . Madin-Darby canine kidney ( MDCK ) cells were maintained in minimum essential medium ( MEM ) supplemented with 5% FBS . Human lung epithelial ( A549 ) cells , bat lung epithelial ( Tb1Lu ) cells , mink lung epithelial ( Mv1Lu ) cells and swine testis ( ST ) cells were maintained in MEM supplemented with 10% FBS . Human lung epithelial ( Calu-3 ) cells were maintained in MEM supplemented with 10% FBS , 1% nonessential amino acids , and 1 mM sodium pyruvate . Nucleotide sequences of the eight gene segments of A/little yellow-shouldered bat/Guatemala/164/2009 ( H10N17 ) ( Bat09 ) were retrieved from the GenBank database . A total of 472 oligonucleotides of 56–60 bases in length were designed for enzymatic assembly of the eight segments . The assembly and error correction processes were performed as recently described [32] , [33] , modified with increased time at all extension steps ( from 72°C for 1 min to 72°C for 2 min ) for efficient assembly of the polymerase segments . The synthesized segments ( Fig . S1 ) were cloned into the modified bidirectional influenza reverse genetics vectors pBZ66A12 [34] using the recombination-based method [35] and transformed into Stella competent E . coli cells ( Clontech ) . Colonies were selected and sequenced . The appropriate clones for each segment were propagated for plasmid preparation and verified by sequencing . The resulting plasmids are pBZ146A1 ( PB2 ) , pBZ147A11 ( PB1 ) , pBZ148A20 ( PA ) , pBZ149A30 ( HA ) , pBZ150A31 ( NP ) , pBZ151A36 ( NA ) , pBZ152A42 ( M ) and pBZ153A45 ( NS ) . The whole process only took seven days to complete . The plasmids containing Bat09 PB2 mutations were constructed by site-directed mutagenesis using the pBZ146A1 as template . The NS1 truncation constructs were generated by Gibson assembly and details of the truncations are diagramed in Fig . S2C . The modified ( m ) Bat09 HA and NA ( mH1 , mN1 , mH1ss , and mN1ss , see Fig . S2A , S2B for diagrams , and Fig . S4 for sequence alignment ) were synthetized by Gibson assembly from oligonucleotides . Silent substitutions ( ss ) were introduced to disrupt the putative packaging signals in the PR8 HA and NA terminal coding regions . The mH1ss and mN1ss are thus more appropriate than the mH1 and mN1 to assess the HA and NA packaging signal compatibility between Bat09 and PR8 . The Batps-PR8-NP , PR8ps-Bat-NP , Batps-PR8-NS , and PR8ps-Bat-NP constructs were constructed similarly and diagramed in Fig . S2D . As a comparison of the speed of different synthesis strategies , the eight gene segments of A/flat-faced bat/Peru/033/2010 ( H18N11 ) ( Bat10 ) were synthesized by Genewiz ( NJ , USA ) in the vector plasmid of pUC57 based on the GenBank database and subcloned into pHW2000 vector . The resulting plasmids ( pHW-H18-PB2 , pHW-H18-PB1 , pHW-H18-PA , pHW-H18-HA , pHW-H18-NP , pHW-H18-NA , pHW-H18-M and pHW-H18-NS ) were confirmed by sequencing . The whole process took more than one month . The PB2 , PB1 , PA and NP genes were also subcloned into the pDZ vector for use in the mini-genome assay . Diagrams of the mutant or modified genes of Bat09 and Bat10 are described in Fig . S2 . The pPol1-NS-Luc reporters used in the mini-genome polymerase activity assay were described in Fig . S2E . Sequences of all constructs used in this study were confirmed to ensure absence of unwanted mutations and the GenBank accession numbers are KM203345-KM203356 . Briefly , 0 . 6 µg of plasmid for each gene segment was mixed and incubated with 15 µl of Mirus TranIT-LT1 ( Mirus Bio , Madison , WI ) at 20°C for 20 min . The transfection mixture was transferred to 90% confluent 293T/MDCK cell monolayers in a 35-mm tissue culture dish and incubated at 37°C with 5% CO2 for 8 h . The transfection supernatant was replaced with 3 ml of Opti-Mem I medium ( Life Technologies ) supplemented with 0 . 3% bovine serum albumin ( BSA ) fraction V ( Life Technologies ) , 3 µg/ml tosylsulfonyl phenylalanyl chloromethyl ketone ( TPCK ) -trypsin ( Worthington , Lakewood , NJ ) , and 1% antibiotic-antimycotic ( Life Technologies ) . Three days post-transfection , culture supernatant ( passage 0 , P0 ) was collected and 0 . 5 ml of that was inoculated into MDCK cells in 6-well plates at 37°C . Supernatant ( P1 ) was collected at 4 days post-inoculation ( dpi ) , or when severe cytopathic effect ( CPE ) was observed . The P1 supernatant was further passaged blindly for two passage before determined to be negative for rescue . Titers of the viruses used in this study were determined by TCID50 assay in MDCK cells . Rescue efficiency definition . Very easy ( ++++ ) : P0 viral titer 106–108 TCID50/ml , or severe CPE observed in P1 within 1 dpi; Moderate ( +++ ) : P0 titer 104–106 TCID50/ml , or obvious CPE observed in P1 within 2 dpi; Difficult ( ++ ) : P0 titer 102–104 TCID50/ml , or weak CPE observed in P1 within 4 dpi; Very difficult ( + ) : P0 titer lower than 102 TCID50/ml , or CPE not observed until P2/P3; Negative ( Neg ) : rescue failed , no CPE observed through passage 3 . Various transfection conditions including different transfection reagents , temperatures , and incubation time before supernatant collection were attempted to rescue the wild type Bat09 virus and the reassortants between Bat09 and PR8 . However , none of them generated any positive rescue results if they were negative under standard rescue condition described above . Bat09 transfection supernatants were also transferred to various cells ( MDCK , mink lung Mv1-Lu , swine testis , Vero , A549 cells , Calu-3 , bat lung epithelial Tb1Lu ) and embryonated chicken eggs and passaged at least three times . The real-time RT-PCR assays targeting Bat09 and PR8 M genes were used to confirm negative results ( primers and probes are possible upon request ) . To determine whether virus particles of Bat09 and other viruses can be produced by reverse genetics system , a total of thirty-five ml of transfected 293T cell supernatants for each virus were collected at 48 hours post transfection and centrifuged at 8000 rpm for 20 minutes to remove the cell debris . Then the clear supernatant was loaded on 30% ( w/v ) sucrose in centrifuge tubes and was concentrated at 27 , 000 rpm ( Optima LE-80K ultracentrifuge , Beckman Coulter ) for 2 hours . The virus pellets was dissolved in 100 µl of water and the viral particles were fixed by incubating with 0 . 2% paraformaldehyde at 37°C for 48 hours . The fixed particles were dipped on a 200 mesh copper grid and the grid was dried and stained with negative staining before observation under an electron microscope . MDCK monolayers in 12-well plates were washed twice with PBS , and then 2 ml of virus growth medium ( VGM ) was added to each well . The cells were inoculated at a multiplicity of infection ( MOI ) of 0 . 01 TCID50/cell with the Bat09:mH1mN1 virus or PR8 virus ( Bat09:mH3mN2 virus or TX98 virus ) and incubated at 37°C . Supernatants were collected at 1 , 2 , and 3 days post inoculation ( dpi ) . Inoculations of Calu-3 cells were performed similarly , except that an MOI of 0 . 02 TCID50/cell was used for the following viruses: Bat09:mH1mN1ss , Bat09:mH1mN1ss-NS1-73 , Bat09:mH1mN1ss-NS1-128 , PR8 , PR8-NS1-73 , and PR8-NS1-126 . The VGM used for MDCK cells was EMEM supplemented with 0 . 15% BSA fraction V , 2 µg/ml TPCK-trypsin , and 1% antibiotic-antimycotic , and the VGM used for Calu-3 cells was EMEM supplemented with 0 . 3% BSA fraction V , 1 µg/ml TPCK-trypsin , and 1% antibiotic-antimycotic . All virus titers were determined by TCID50 assay using MDCK cells . Six of 10-day-old embryonated chicken eggs were inoculated with Bat09:mH1mN1 or PR8 at 103 TCID50/egg . After 2 days incubation at 35°C , allantoic fluid was collected from each egg and titrated individually . The 4 eggs with the highest titers in each virus group was used to calculate the average titer and generate the graph in Fig . 1E . A modified Multi-segment RT-PCR [35] , [36] was used to amplify influenza-specific segments . The only modification to the procedure was the primers used for amplification were changed to match bat influenza termini . The oligonucleotide primers used were Uni12/Inf-5G ( 5′-GGGGGGAGCAGAAGCAGG-3′ ) and Uni13/Inf-1 ( 5′-CGGGTTATTAGTAGAAACAAGG-3′ ) . The M-RTPCR amplicons were used for Illumina Miseq library construction via Nextera DNA sample prep kit ( Illumina , Inc . ) and sequenced using the Illumina MiSeq ( Illumina , Inc . ) according to manufacturer's instructions . SNP variations were identified using custom software that applies statistical tests to minimize false positive SNP calls that could be caused by the types of sequence-specific errors that may occur in Illumina reads identified and described in Nakamura , et al . [37] . To overcome this problem , the protocol requires observing the same SNP , at a statistically significant level , in both sequencing directions . Once a minimum minor allele frequency threshold and significance level are established by the user , the number of minor allele observations and major allele observations in each direction and the minimum minor allele frequency threshold are used to calculate a p-value based on the binomial distribution cumulative probability , and if the p-values calculated in each of the two sequencing directions are both less than the Bonferroni-corrected significance level , then the SNP call is accepted . For our analyses , we used a significance level of 0 . 05 ( Bonferroni-corrected for tests in each direction to 0 . 025 ) , and a minimum minor allele frequency threshold of 10% of the read population . To measure the IFN-antagonist function of NS1 , a luciferase-based , Sendai virus-mediated IFN-β promoter activation assay was conducted as previously described [16] . Briefly , 293T cells in 24-well plates were transfected with empty vector ( 200 ng ) or increasing amounts of wild type ( WT ) or carboxyl terminal truncated NS1 from Bat09 and PR8 ( 2 ng , 10 ng , and 50 ng of NS1 expression plasmids supplemented with 198 ng , 190 ng , and 150 ng of empty vector , respectively ) . Also co-transfected were 200 ng of an IFN-β-promoter-luciferase reporter plasmid ( pIFNβ-Luc ) and 20 ng of a plasmid constitutively expressing Renilla luciferase ( pRL-TK from Promega ) . At 18 hours post transfection , cells were infected with Sendai virus to induce the IFN-β promoter . A dual-luciferase assay was performed at 18 hour post virus inoculation , and firefly luciferase was normalized to Renilla luciferase activity . The relative luciferase activity of the group with empty vector was set as 100% , and the other groups were presented relative to that . As previously described for the VSV-GFP virus mediated interferon bioassay [22] , in the VSV-Luciferase virus mediated bioassay , A549 cells were inoculated with one of the wild type or NS1 truncated viruses at an MOI of 4 TCID50/cell , or were mock-inoculated; supernatants were then collected at 24 hpi . Supernatants were treated with UV irradiation to inactivate viruses and were then transferred to naïve A549 cells . Following 24 h of incubation at 37°C , supernatants were removed , and the cells were inoculated with VSV-Luciferase virus [38] , at an MOI of 2 TCID50/cell . The firefly luciferase expression in the cells was measured using the Luciferase Assay System ( Promega ) at 4 hpi with VSV-Luciferase . The luciferase-mediated mini-genome polymerase activity assay was performed as previously described , using a PolI-driven reporter plasmid and pDZ-based PB2 , PB1 , PA , and NP bidirectional expression plasmids [21] , [35] . To determine the effects of PB2 mutations on polymerase activity ( Fig . 7 ) 293T cells were co-transfected with 0 . 2 µg each of the PB2 ( WT or mutant ) , PB1 , PA , NP , and a pPol1-FluA-NS-Luc ( firefly luciferase flanked by A/New York/1682/2009 [23] ) . As a control for transfection efficiency , 0 . 02 µg of the Renilla luciferase plasmid pRL-TK ( Promega ) was also co-transfected . After 18 hours of incubation at 33°C , 37°C , and 39°C , luciferase production was assayed using the dual-luciferase reporter assay system ( Promega ) according to the manufacturer's instructions . Firefly luciferase expression was normalized to Renilla luciferase expression ( relative activity ) . The relative activity of the PB2-WT was set as 1 fold , and the relative activities of the PB2 mutants were presented relative to that ( Fig . 7 ) . To test the compatibility between RNPs ( PB2 , PB1 , PA , and NP ) and viral RNA promoters from bat-influenza virus ( Bat09 ) ( Fig . S3 ) , IAV ( A/PR/8/1934 ) , and IBV ( B/Russia/1969 ) , 293T cells were co-transfected with 0 . 2 µg each of the PB2 , PB1 , PA , NP , and a pPol1-NS-Luc reporter plasmid , followed by incubation at 37°C for 18 hours . Three reporters were used in this study , including pPolI-Bat-NS-Luc ( firefly luciferase flanked by Bat09 NS non-coding regions ) , pPol1-FluA-NS-Luc , and pPolI-FluB-NS-Luc ( firefly luciferase flanked by B/Russia/1969 NS non-coding regions ) ( Fig . S2D ) . For each combination of RNP and pPolI-NS-Luc reporter ( from Bat09 , A , or B Type ) , three independent replicates were conducted . For each RNP , the luciferase activity with the reporter from the same virus ( e . g . , Bat-RNP and pPol1-Bat-NS-Luc ) was set at 100% , and the activities with the other two reporters ( e . g . , pPol1-FluA-NS-Luc and pPol1-FluB-NS-Luc ) were presented relative to that ( Fig . S3 ) . The PB2 , PB1 , PA , and NP compatibility between Bat09 and the following influenza viruses was examined in the study ( Fig . 8 ) : A/PR/8/1934 ( lab adapted human H1N1 ) , A/Ann Arbor/6/1960 ( human H2N2 ) , A/New York/238/2005 ( human H3N2 ) ; A/New York/1692/2009 ( human H1N1 seasonal ) , A/New York/1682/2009 ( human H1N1 pandemic ) , A/canine/New York/6977983/2010 ( canine H3N8 ) , A/turkey/Ontario/7732/1966 ( avian H5N9 ) , A/Hong Kong/213/2003 ( avian H5N1 ) , A/Anhui/1/2013 ( human H7N9 ) , B/Russia/1969 ( lab adapted human IBV ) , and A/flat-faced bat/Peru/033/2010 ( bat H18N11 ) . For the compatibility test between Bat09 and IAVs ( Fig . 8A–I ) , 293T cells were co-transfected with 0 . 2 µg each of the PB2 , PB1 , PA , NP ( from Bat09 or IAV ) , 0 . 1 µg of pPolI-Bat-NS-Luc plasmid and 0 . 1 µg of pPolI-FluA-NS-Luc . For compatibility test between Bat09 and IBV ( Fig . 8J ) , 293T cells were co-transfected with 0 . 2 µg each of the PB2 , PB1 , PA , NP ( from Bat09 or B/Russia/1969 ) , 0 . 1 µg of pPolI-Bat-NS-Luc plasmid and 0 . 1 µg of pPolI-FluB-NS-Luc . For compatibility test between Bat09 and Bat10 ( Fig . 8K ) , 0 . 2 µg each of the PB2 , PB1 , PA , NP ( from Bat09 or Bat10 ) , and pPolI-Bat-NS-Luc plasmids were used ( The NS non-coding regions of Bat09 and Bat10 have the same sequence ) . Renilla luciferase was also co-transfected and dual-luciferase reporter assay system was used . For each combination of PB2 , PB1 , PA , and NP ( from Bat09 or another influenza virus ) , three independent replicates were conducted at 37°C , the luciferase activity of the all-Bat09-combination ( Bat09-PB2/Bat09-PB1/Bat09-PA/Bat09-NP ) was set at 100% , and the activities of other 15 combinations were presented relative to that . A total of 98 female BALB/c mice aged 6 to 7 weeks were randomly allocated to 7 groups ( 14 mice/group ) . Six mice were intranasally inoculated with 103 TCID50 of each virus ( Bat 09:mH1mN1 , Bat09:mH1mN1-PB2-701D , Bat09:mH1mN1-PB2-627K701D , Bat09:mH1mN1-PB2-158G701D , Bat09:mH1mN1-PB2-158G , PR8 , or MEM Mock ) in 50 µL fresh MEM medium while under light anesthesia by inhalation of 4% isoflurane . To determine the virus replication in mouse lungs , three mice from each group were euthanized on both 3 and 5 day post-inoculation ( dpi ) . Another 8 mice from each group were intranasally inoculated with 104 TCID50 of viruses in 50 µL MEM medium; all eight mice were kept to monitor body weights and clinical signs . Weights were recorded daily and general health status was observed twice daily . After the onset of disease , the general health status was observed three times daily . Severely affected mice ( i . e . , more than 25% body weight loss ) were euthanized immediately , and the remaining mice were euthanized on 14 dpi . All control mice were intranasally inoculated with 50 µL fresh MEM ( mock group ) , three control mice were necropsied at 3 and 5 dpi , the remaining mice were kept until the end of the animal study . During necropsy , the right part of the lung was frozen at −80°C for virus titration , and the left part of the lung was fixed in 10% formalin for histopathologic examination . For virus titration , the 10% lung homogenate was prepared in cold fresh MEM medium by using a Mini Bead Beater-8 ( Biospec Products; 16 Bartlesville , OK ) . The homogenate was centrifuged at 6000 rpm for 5 minutes , and the supernatant was titrated by infecting MDCK cells in 96-well plates . For the histopathologic examination , lung tissues fixed in 10% phosphate-buffered formalin were processed routinely and stained with hematoxylin and eosin . The lungs were examined microscopically both for the percentage of the lung involved and for the histopathologic changes seen , including bronchiolar and alveolar epithelial necrosis , intraalveolar neutrophilic inflammation , peribronchiolar inflammation , and bronchiolar epithelial hyperplasia and atypia . For detection of virus NP antigens in lung sections on day 5 post infection , a rabbit anti-H1N1 ( 2009 flu pandemic ) NP polyclonal antibody was used ( Genscript , USA ) . A pathologist examined each slide in a blinded fashion . A total of 70 female BALB/c mice aged 6 to 7 weeks were randomly allocated to 5 groups ( 14 mice/group ) . To determine virus replication , six mice were intranasally inoculated with 104 TCID50 of each virus ( Bat09:mH1mN1ss-NS1-WT , Bat09:mH1N1ss-NS1-73 , Bat09:mH1mN1ss-NS1-128 , and PR8-NS1-126 ) in 50 µL MEM medium while under light anesthesia by inhalation of 4% isoflurane . Three mice from each group were killed on both 3 and 5 day post-inoculation ( dpi ) . Another 8 mice from each group were intranasally inoculated with 105 TCID50 of each virus in 50 µL MEM medium for morbidity and mortality comparison . All the other procedures are same with described previously . A total of 42 female BALB/c mice aged 6 to 7 weeks were randomly allocated to 3 groups ( 14 mice/group ) . To investigate virus replication in mice , six mice from each group were intranasally inoculated with 3×104 TCID50 of virus or mock-inoculated with 50 µL fresh MEM medium while under light anesthesia by inhalation of 4% isoflurane . Three of six inoculated mice from each group were euthanized at 3 and 5 day post-inoculation ( dpi ) . To evaluate viral pathogenicity in mice , the remaining eight mice from each group were intranasally inoculated with 3×105 TCID50 of virus ( Bat09:mH3mN2 , and TX98 ) in 50 µL fresh MEM medium or mock-inoculated with 50 µL fresh MEM medium . The mice were monitored body weights and general health status daily . After the onset of disease , the general health status was observed twice per day . Severely affected mice ( i . e . , more than 25% body weight loss ) were humanly euthanized , and the remaining mice were euthanized and bloods were collected from each mouse to isolate serum for the HI assay at 14 dpi . Sample collection and analysis , and virus titration were performed as described above . To study the reassortment between Bat09:mH1mN1 and PR8 or Bat10:mH1mN1 , confluent monolayer of MDCK cells in 6-well-plates were co-infected with both viruses ( Bat09:mH1mN1 and PR8 , or Bat09:mH1mN1 and Bat10:mH1mN1 ) . Both modified Bat09:mH1mN1 and Bat10:mH1mN1 viruses showed similar replication kinetics in MDCK cells , whereas the PR8 replicated more efficiently than both modified viruses in MDCK cells . Therefore , for the co-infection study with PR8 and Bat09:mH1mN1 viruses , the cells were infected with the PR8 at MOI of 1 and with the Bat09:mH1mN1 at MOI of 4 ( a ratio of both viruses is 1∶4 ) . For the co-infection study with Bat09:mH1mN1 and Bat10:mH1mN1 viruses , the cells were infected with each virus at MOI of 1 ( a ratio of both viruses is 1∶1 ) . The co-infected MDCK cells were incubated at 37°C with 5% CO2 for 1 hour . After 1 hour of incubation , the supernatant was removed and the infected cells were washed with fresh MEM for 10 times . One mL of infection medium supplemented with 1 µg/mL TPCK-trypsin ( Worthington , Lakewood , NJ ) was added on cells . The supernatant containing progeny viruses was collected at 24 hours after inoculation . Plaque assays were performed in MDCK cells to select single virus from co-infected supernatants . The purified single virus ( plaque ) was amplified for further analysis . To identify the origin of each gene of the purified single virus , specific RT-PCR was used to differentiate internal genes from Bat09:mH1mN1 , Bat10:mH1mN1 and PR8 viruses ( primers for specific RT-PCR are available upon request ) . The surface HA and NA genes were differentiated by sequencing HA and NA non-coding regions ( packaging signals ) since three parental viruses contain identical HA and NA ORF sequences and different sequences in non-coding region ( it is difficult to differentiate them by RT-PCR ) . For the RT-PCR , RNAs were extracted from each amplified single virus using a QIAamp Viral RNA Mini Kit ( Qiagen ) . cDNA was synthesized by using the bat universal 12 primer ( 5′-AGCAGAAGCAGG-3′ ) for the samples from the co-infection study with Bat09:mH1mN1 and Bat10:mH1mN1 viruses , and by using a mixture of an IAV universal 12 primer ( 5′-AGCRAAAGCAGG-3′ ) and the bat universal 12 primer ( 5′-AGCAGAAGCAGG-3′ ) for the samples from the co-infection study with Bat09:mH1mN1 and PR8 viruses . If the origin of internal genes determined by the specific RT-PCR was inconclusive , sequencing was performed to confirm the results from specific RT-PCR ( All sequence primers are available upon request ) . Luciferase activity , virus titers , and mouse weights were analyzed by using analysis of variance ( ANOVA ) in GraphPad Prism version 5 . 0 ( GraphPad software Inc , CA ) . One-way ANOVA with Dunnett's multiple comparison test was used to determine the significance of the differences ( P<0 . 05 ) among different groups . For simple comparisons , Student's t test was used to examine the significance of differences observed . Error bars represent standard deviation ( ±SD ) . | The identification of influenza virus-like sequences in two different bat species has generated great interest in understanding their biology , ability to mix with other influenza viruses , and their public health threat . Unfortunately , bat-influenza viruses couldn't be cultured from the samples containing the influenza-like nucleic acids . We used synthetic genomics strategies to create wild type bat-influenza , or bat-influenza modified by substituting the surface glycoproteins with those of model influenza A viruses . Although influenza virus-like particles were produced from both synthetic genomes , only the modified bat-influenza viruses could be cultured . The modified bat-influenza viruses replicated efficiently in vitro and an H1N1 modified version caused severe disease in mice . Collectively our data show: ( 1 ) the two bat-flu genomes identified in other studies are replication competent , suggesting that host cell specificity is the major limitation for propagation of bat-influenza , ( 2 ) bat-influenza NS1 antagonizes host interferon response more efficiently than that of a model influenza A virus , ( 3 ) bat-influenza has both genetic and protein incompatibility with influenza A or B viruses , and ( 4 ) that these bat-influenza lineages pose little pandemic threat . | [
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] | 2014 | Characterization of Uncultivable Bat Influenza Virus Using a Replicative Synthetic Virus |
There are 91 known capsular serotypes of Streptococcus pneumoniae . The nasopharyngeal carriage prevalence of particular serotypes is relatively stable worldwide , but the host and bacterial factors that maintain these patterns are poorly understood . Given the possibility of serotype replacement following vaccination against seven clinically important serotypes , it is increasingly important to understand these factors . We hypothesized that the biochemical structure of the capsular polysaccharides could influence the degree of encapsulation of different serotypes , their susceptibility to killing by neutrophils , and ultimately their success during nasopharyngeal carriage . We sought to measure biological differences among capsular serotypes that may account for epidemiological patterns . Using an in vitro assay with both isogenic capsule-switch variants and clinical carriage isolates , we found an association between increased carriage prevalence and resistance to non-opsonic neutrophil-mediated killing , and serotypes that were resistant to neutrophil-mediated killing tended to be more heavily encapsulated , as determined by FITC-dextran exclusion . Next , we identified a link between polysaccharide structure and carriage prevalence . Significantly , non-vaccine serotypes that have become common in vaccinated populations tend to be those with fewer carbons per repeat unit and low energy expended per repeat unit , suggesting a novel biological principle to explain patterns of serotype replacement . More prevalent serotypes are more heavily encapsulated and more resistant to neutrophil-mediated killing , and these phenotypes are associated with the structure of the capsular polysaccharide , suggesting a direct relationship between polysaccharide biochemistry and the success of a serotype during nasopharyngeal carriage and potentially providing a method for predicting serotype replacement .
Streptococcus pneumoniae , or pneumococcus , is an important pathogen worldwide and is a causative agent of pneumonia , meningitis and otitis media . There are 91 known pneumococcal serotypes , and each produces a biochemically distinct polysaccharide capsule that is in most cases covalently attached to the cell wall . Serotype affects nearly every aspect of pneumococcal pathogenesis and of nasopharyngeal carriage , which precedes disease and serves as the reservoir for transmission of the organism [1] . The serotypes most common in both invasive disease [2] , [3] and carriage [4] show remarkable consistency across geography and time despite some differences in the details . Serotypes differ not only in their prevalence , but also in their tendency to cause invasive or mucosal disease ( ratio of disease cases to carriers ) [5]–[8] , their age distribution [4] , their tendency to cause outbreaks [4] , [9] , and their degree of antimicrobial resistance [10] . The polysaccharide-protein pneumococcal conjugate vaccine ( Prevnar , or PCV7 ) targets seven clinically relevant serotypes in young children . Currently , the vaccine is in widespread use , and while PCV7 has been largely successful at reducing the burden of invasive pneumococcal disease in the United States . [11] , evidence is emerging that serotypes not targeted by the vaccine , such as types 19A and 15 , are increasing in importance in both carriage and invasive disease [12]–[15] . In the long-term , disease caused by replacement serotypes could partially undermine the impact of the vaccine . As more countries introduce PCV7 or explore alternative polysaccharide formulations , it will be critical to understand the factors that determine serotype patterns of carriage . We sought to measure the biological differences among capsular serotypes that may account for these effects on pneumococcal epidemiology and then use these biological measures as predictors of prevalence before and after vaccination . Duration of carriage , a determinant of prevalence , varies between serotypes [16] , [17] and could , in part , be influenced by the interactions between bacteria and host immune effectors . The capsule protects against phagocytic clearance by blocking the deposition and function of opsonins directed against cell surface antigens [18]–[20] . In addition , capsule can affect susceptibility to trapping by neutrophil extracellular traps ( NETs ) [21] , killing by defensins [22] , and clearance by mucus [23] . Strains that produce more capsule in vitro are more virulent in vivo [24] , but degree of encapsulation has not previously been shown to strongly impact nasopharyngeal colonization [25] . Immune-mediated clearance from the nasopharynx involves both antibody-dependent and antibody-independent mechanisms of immunity [26]–[29] . Antibody-independent clearance is thought to involve an IL-17A-mediated T-cell response [30] , [31] , which results in the recruitment of neutrophils to the site of infection and subsequent clearance of colonization [31] , [32] . Neutrophils can kill pneumococci in the presence or absence of opsonins , and heavily encapsulated strains can avoid phagocytic uptake [33]–[36] . Capsular polysaccharide quantity and degree of encapsulation could be influenced by a number of factors , and recent work has demonstrated that sugar metabolism could play a regulatory role [37] , [38] . We hypothesized that serotypes that require more energy or carbon to synthesize a polysaccharide repeat unit would ultimately have smaller , less inhibitory capsules . In this study , we demonstrate an association between polysaccharide structure , degree of encapsulation , susceptibility to neutrophil-mediated killing , and carriage prevalence . We propose a model in which serotypes that produce metabolically inexpensive polysaccharides will be more heavily encapsulated , which in turn allows them to persist in the nasopharynx for a longer duration and results in higher prevalence . These results will be particularly useful in predicting the impact of serotype-replacement in various settings .
First , we evaluated whether the production of a capsule affected susceptibility to opsonin-independent killing by human neutrophils . We tested an invasive type 6B clinical isolate , its unencapsulated isogenic derivative and the reconstituted strain with the type 6B capsule locus reinserted . The wild type and the reconstituted encapsulated strain were significantly more resistant to killing than the unencapsulated mutant ( Figure 1A ) . Additionally , by flow cytometry we found that the unencapsulated strain was more efficiently associated with neutrophils than the wild type or the reconstituted strain ( Figure 1B ) . To test whether highly prevalent serotypes are more resistant to neutrophil-mediated killing , we used a panel of TIGR4 capsule-switch variants that are isogenic except for the capsule locus . The more prevalent serotypes , such as 19F and 23F , were indeed most resistant to killing , while types that are rarely isolated from carriage , such as types 4 and 5 , were more efficiently killed ( r = 0 . 77 , p<0 . 001; Figure 1C ) . To confirm these results , we tested another set of five isogenic capsule-switch variants that were constructed in strain 603 , a type 6B clinical isolate . Again , we found that resistance to neutrophil-mediated killing was associated with higher carriage prevalence ( Figure S1 ) . The serotype rank-order of susceptibility to killing was the same in both sets of isogenic capsule-switch variants with the exception of type 6B , which was more resistant to killing in the 603 genetic background . To determine whether the effect of serotype on avoidance of neutrophil-mediated killing could be generalized to clinical carriage isolates , we tested a set of strains from diverse bacterial genetic backgrounds . There was a significant association between susceptibility to killing of the TIGR4 isogenic capsule variants and the corresponding clinical strain of the same serogroup ( Figure 1D ) . These results indicate that serotype is a major determinant of resistance to neutrophil-mediated killing in diverse genetic backgrounds , though it is not the only determining factor . We next wanted to evaluate whether differences in degree of encapsulation between serotypes could affect interactions with neutrophils . To determine the degree of encapsulation , we measured the zone of exclusion of fluorescent dextran molecules by the capsule [39] using our isogenic TIGR4 capsule-switch variants . Serotype significantly influenced the size of the zone of dextran exclusion , and types with larger zones of exclusion were more resistant to neutrophil-mediated killing and more prevalent in carriage ( Figure 2 ) . Serotypes that had large zones of dextran exclusion also had clearly visible capsules when suspended in India ink ( Figure S2 ) . We also found that pre-incubating the neutrophils with purified capsular polysaccharides had no effect on neutrophil-mediated killing ( data not shown ) , further suggesting that the capsule is blocking the bacterial surface from the neutrophils rather than being itself a target . These correlations suggest that degree of encapsulation affects resistance to neutrophil-mediated killing . However , given the differences in structure between polysaccharides , it is possible that some other structural feature , which happens to be correlated with degree of encapsulation , is actually the mechanism of differential resistance to killing . To test the hypothesis that degree of encapsulation is the relevant measure , we made comparisons within a serotype , where structure should be conserved but degree of encapsulation could be manipulated by changing carbon sources , as previously reported [40] , [41] . A derivative of TIGR4 producing a type 19F capsule was grown in a semi-defined medium with either fructose , which has been reported to reduce capsule production [41] , or glucose . We found that bacteria grown in fructose were less encapsulated than those grown in glucose , both by India Ink microscopy ( Figure 3C , D ) and by capsular polysaccharide inhibition ELISA ( Figure 3A ) . Significantly , fructose-grown bacteria were more susceptible to surface killing , and heavily encapsulated serotypes were more strongly affected by growth in fructose ( Figures 3B , S3 ) . These results , which are consistent with findings in group A Streptococcus [42] , indicate that degree of encapsulation directly affects susceptibility to non-opsonic neutrophil-mediated killing . To determine whether our in vitro findings were applicable to an in vivo situation , we co-colonized mice with a mix of isogenic TIGR4 capsule-switch variants producing type 14 and type 19F polysaccharides . These serotypes were chosen because of their different phenotypes in the in vitro assays and because they are easily distinguishable by colony morphology on blood agar plates . In past experiments , we have shown that both strains can colonize mice at similar densities when inoculated alone . Based on our in vitro results , we hypothesized that type 19F would out-compete type 14 . We intranasally inoculated mice with a ratio of 100 CFU of type 14 to 1 CFU of type 19F ( proportion 19F = 0 . 01 ) . Consistent with our hypothesis , while only 1% of the inoculum was type 19F , 90% of the colonies recovered from the nasal washes after 7 days were type 19F , indicating that type 19F colonizes mice significantly better than type 14 ( 4 mice , average proportion 19F: 0 . 90 , SEM: 0 . 04 , binomial probability: p<0 . 001 compared to inoculum ) . Having found a relationship between prevalence and degree of encapsulation and resistance to neutrophil-mediated killing , we next wanted to evaluate bacterial factors that could influence these phenotypes . We hypothesized that the extent of encapsulation , and subsequently the epidemiologic properties of the serotype , could be constrained by the metabolic requirements for biosynthesis of different capsular polysaccharides . By examining published polysaccharide structures and biochemical pathways , we determined the number of carbons and the number of high-energy bonds ( ATP-equivalents ) that are required to generate one polysaccharide repeat unit . Consistent with the hypothesis , we found a significant association between these measures of metabolic cost and degree of encapsulation and a trend between metabolic cost and resistance to non-opsonic killing ( Table 1 ) . Next , we evaluated the relationship between these correlates of polysaccharide structure and carriage prevalence . We began by assessing prevalence in populations not exposed to PCV7 . Using carriage data pooled from three studies in the United Kingdom , the United States , and The Netherlands , we found a statistically significant inverse correlation between metabolic cost and serotype frequency among circulating types ( Table 1 , Figure 4 ) . These analyses were performed among serotypes with an average frequency of at least 1% . As Figure 4 shows , serotypes that were extremely rare ( <1% ) had a broad range of carbons per repeat unit , suggesting that polysaccharide structure is not the sole determinant of the success of a serotype , and in particular suggesting that a low metabolic cost is necessary , but not sufficient for high prevalence in unvaccinated populations . We noted that serotype 3 – which has the least costly repeat unit structure and is known for its mucoid colony phenotype that is attributed to extensive capsule production – was an outlier in this relationship , with relatively low carriage prevalence . While the reason for its outlier status is unclear , it is notable that type 3 polysaccharide can generate a relatively strong antibody response early in infancy [43] , and this serotype is highly clonal . Without serotype 3 , the relationships become much stronger ( Table 1 ) . Finally , we evaluated the relationship between polysaccharide structure and prevalence in a population exposed to PCV7 , which has opened an ecological niche for serotypes not included in the vaccine . To date , no principle has been identified to predict which non-vaccine serotypes would become most common in vaccinated populations . We hypothesized that metabolic cost might provide such a principle to predict which non-vaccine serotypes would become most common in vaccinated populations . We compared the prevalence of serotypes in a post-vaccine carriage sample in Massachusetts in 2004 [12] , [44] to measures of metabolic cost . Similar to what was seen before vaccination , there was no distinctive pattern of metabolic cost among the serotypes with frequencies <1% , but among those at appreciable frequencies , there was a negative relationship between metabolic cost and prevalence ( Figure 4B ) , albeit here not statistically significant ( Table 1 ) . Once again , serotype 3 was an extreme outlier , and exclusion of type 3 resulted in a strong association between metabolic cost and prevalence ( Table 1 ) . We also found that increased degree of encapsulation was itself associated with higher post-vaccine carriage prevalence among the serotypes that we tested ( Figure S4 ) .
Our data suggest a novel biological explanation for serotype patterns of pneumococcal carriage . If clearance of pneumococcus from the nasopharynx depends on T-cell-mediated immunity [28] , [31] , we would expect that serotypes that successfully avoid neutrophil-mediated killing will persist longer in the nasopharynx ( thereby also obtaining more opportunities for transmission ) and , as a result , be more prevalent in the population . Our results demonstrate a clear link between capsular polysaccharide structure and prevalence . We propose a model in which serotypes producing polysaccharides that are less metabolically costly will be more heavily encapsulated and thus avoid phagocytic clearance and persist in carriage . It is possible , though , that innate immune effectors in addition to or instead of neutrophils could determine the serotype prevalence hierarchy . In particular , phagocytes other than neutrophils could play a role , and antimicrobial peptides [22] , mucus-mediated clearance [23] , complement , C-reactive protein [45] , and antibodies directed against the capsule or other surface antigens could all affect clearance rates of different serotypes from the nasopharynx . Likewise , some of these factors could affect the rate of acquisition of new carriage episodes , which would also influence the prevalence of a serotype . While interactions with the host likely play a large role in shaping serotype patterns , polysaccharide production itself could affect the fitness of the bacterium and thus its ability to compete with other serotypes during colonization . We did not observe any meaningful differences in growth rate between serotypes in vitro , but it is possible that under in vivo conditions , polysaccharide production causes a competitive disadvantage for some serotypes . It is also possible that our measures of “metabolic cost” do not reflect the true cost to the bacteria since the organisms could respond to environmental pressures by changing their carbon and energy utilization . Our findings provide a simple method for ranking serotype prevalence . The currently available vaccine for infants targets seven clinically relevant serotypes , but alternative formulations , including 10- and 13-valent vaccines , are being explored for use in developing countries . Since the relationship between polysaccharide structure and serotype prevalence is observed in both vaccinated and unvaccinated populations , this could potentially be used as a tool for predicting patterns of serotype replacement in different settings . It has long been known that many characteristics of pneumococcal epidemiology were associated with serotype . Here , using two sets of pneumococci that are isogenic except for their capsular serotypes , we have shown that an in vitro predictor of these epidemiologic traits –susceptibility to non-opsonic killing – is determined in large part by capsular type rather than bacterial genetic background; moreover , using multiple clinical isolates , we have shown that these in vitro properties of different serotypes are consistent across diverse genetic backgrounds . However , it is also clear from our data that these in vitro properties can vary within capsular serotypes , consistent with the possibility that noncapsular factors , such as bacterial adhesins , could affect the success of a strain during colonization and could subsequently influence patterns of serotype replacement . Certain clones have been particularly successful and have acquired multiple serotypes [46] , [47] , further supporting the notion that other genetic factors influence the fitness of a pneumococcal strain . Likewise , while the types that are present at appreciable frequencies show a negative relationship between prevalence and metabolic cost , there are many serotypes with low metabolic costs that remain rare , most notably serotype 3 . This finding suggests that a metabolically inexpensive capsule is necessary , but not sufficient , for high prevalence ( Figure 4 ) . The mechanistic explanation for the association between polysaccharide structure and degree of encapsulation remains to be elucidated . We evaluated a number of structural features including the number of particular monosaccharide units , number of N-acetylated sugars and number and proportion of hydrophobic residues . One possibility is that limited supplies of energy or carbon impose more stringent limits on the production of metabolically costly capsule subunits than on the production of less costly ones . Another possibility , not mutually exclusive , results from the fact that serotypes with less costly polysaccharide repeat units also tend to have a higher ratio of charged molecules per carbon . While capsular charge does not have a large direct effect on interactions with neutrophils , it can affect the 3-dimensional stability of the capsule and the degree of encapsulation of the bacteria [39] , [48] . Hence , capsular types with higher ratios of charge to carbon may have physically larger and/or more inhibitory capsules than those with lower ratios . Clearly , this latter mechanism is not the entire explanation , because the neutral type 14 capsule is relatively inhibitory ( near the median in the neutrophil-killing assay ) , but it may play a contributory role . Whatever the mechanism ( s ) involved , our experimental manipulations of polysaccharide production for a single serotype suggest that capsular size itself affects interactions with the host even when structure is held constant . It has previously been noted that there is an inverse correlation between carriage prevalence and invasiveness [6] , and our findings may also explain this phenomenon . Bacteria attached to the epithelium tend to be less heavily encapsulated than unattached pneumococci [49] . It has been suggested that direct interaction with epithelial cells can promote invasion into underlying tissue and blood , and capsule could disrupt this process [50] , [51] . As a result , serotypes that avoid association with neutrophils might also interact less with the epithelium and be less likely to invade . In contrast , highly invasive serotypes , such as types 4 and 5 , are efficiently killed by neutrophils , but they might interact more closely with the mucosal surface and be more likely to invade . We have evaluated degree of encapsulation by measuring the zone of inhibition of FITC-Dextran by the capsule . While this method does not directly determine polysaccharide quantity , it does provide a physiologically relevant measure of capsule thickness , which could in turn affect accessibility of a number of surface structures that could be recognized by neutrophils and other immune effectors . These findings raise several additional questions that should be addressed in future studies . From the population-biological perspective , a key question is: if metabolically costly capsules reduce the ability of pneumococci to persist in the nasopharynx , how do these capsular types persist in the population in the face of competition from the metabolically less costly , more common types ? One possibility is that these types occupy a distinct ecological niche . Such a niche could be a direct consequence of these strains' less pronounced capsule ( perhaps allowing more intimate association with the epithelium ) or could be created by the existence of serotype-specific immunity in some hosts , which may be most prevalent against the most common types , providing an advantage to the less common types to which fewer hosts are naïve [29] . In summary , these results suggest that the epidemiologic phenomena of serotype-specific prevalence in carriage and possibly serotype replacement can be explained , in substantial part , by bacterial characteristics measurable in vitro and furthermore by biochemical properties of the capsules themselves . Previously , it had been observed that capsular types differ in their epidemiological properties [4] , [6] , [16] , [17] , but it was not clear to what extent these properties resulted from the nature of the capsule itself or from the genetic backgrounds with which particular types – especially the rare types , which tend to be highly clonal – are associated . The hypothesis that capsule itself causes these epidemiologic differences is strengthened by our findings that the biochemical properties of the capsule predict epidemiologic properties , and that mechanistic intermediates such as degree of encapsulation and resistance to neutrophil killing , could be altered by changing only the capsule .
Blood was obtained from healthy volunteers according to a protocol approved by the Office of Human Research Administration at Harvard School of Public Health . Neutrophils were isolated using a Histopaque 10771 , 11191 gradient ( Sigma-Aldrich , St . Louis , MO ) according to the manufacturer's instructions and used immediately . Capsule-switch variants were constructed on the TIGR4 genetic background [52] and were backcrossed at least three times , except for serotypes 1 and 5 , in which we could only accomplish one backcross . The TIGR4 parent strain was itself backcrossed as a control for the transformation procedure . The 603 capsular variants were constructed with the same method and were all backcrossed 3 times , except for type 6B , which was backcrossed once as a transformation control . Nasopharyngeal clinical carriage isolates ( Table S2 ) were colony-purified prior to use . Strain 603WT is a type 6B invasive disease isolate [53] , 603cap- was constructed by replacement of the capsule biosynthesis locus with the Janus cassette , and 603cap-:6B was constructed by transforming 603cap- with genomic DNA from the parent strain , as described previously [52] , [54] . All strains were grown in Todd Hewitt Broth with 0 . 5% yeast extract ( THY ) ( BD , Franklin Lakes , NJ ) at 37° with 5% CO2 unless otherwise noted . In some cases , strains were grown in a semi-defined minimal media [55] with 1000 U/mL catalase ( MP Biomedicals , Solon , OH ) and 10 mM sugar . Neutrophil surface killing assays were performed as described previously [31] . Briefly , bacteria were grown to mid-log phase and frozen in THY/10% glycerol at −80° . On the day of the experiment , bacteria were thawed and diluted to 5×103 CFU/mL in saline , and 10 µL of this suspension was spotted and allowed to dry at room temperature on trypticase soy agar with 5% defibrinated sheep blood ( TSA II ) ( BD ) with 10 replicates per plate . Twenty microliters of neutrophils ( 2×106 cells/mL ) were then overlaid , allowed to dry , and incubated overnight at 37° with 5% CO2 . Percent survival was calculated by comparing killing of each strain to a duplicate control plate with no neutrophils . All experiments were repeated on several days with different frozen lots of bacteria . The overall killing efficiency varied between experiments , so we normalized the data by dividing percent survival for each serotype by percent survival of type 9N to obtain relative survival . The mean relative survival from at least two independent experiments is presented unless otherwise stated . For experiments comparing susceptibility to killing of isogenic capsule switch variants and clinical isolates , the serogroup mean survival was used . Bacteria were grown to mid-log phase , washed in Hanks balanced salt solution ( HBSS ) ( Cellgro , Manassas , VA ) with 1% BSA , resuspended in FITC ( Calbiochem , La Jolla , CA ) ( 0 . 5 mg/mL in HBSS/1%BSA ) and incubated for 1 hour at 4° . The bacteria were then washed twice and frozen in HBSS/1%BSA with 10% glycerol . Staining was evaluated by flow cytometry to ensure equivalent staining between strains . To evaluate bacterial association with human neutrophils , we developed a protocol based in part on Lee et al . [56] . In a 24-well dish , we added 1 mL of blood agar base ( BD ) per well and allowed it to solidify . FITC-labeled bacteria were then thawed , washed in HBSS/1%BSA , and resuspended to 1×108 CFU/mL . 50 µL of bacteria were added to each well , allowed to dry , and overlaid with 2 . 5×105 neutrophils in 50 µL . After 20 minutes , 1 mL ice cold HBSS/BSA was added to each well to harvest the neutrophils . This mixture was then washed twice and fixed with 1% formalin for 2 hours . Bacterial association with the neutrophils was assessed by flow cytometry using a MoFlo flow cytometer ( Dako Cytomation , Denmark ) , and analysis was performed in Summit v4 . 3 ( Dako ) . The average relative fluorescence from two independent experiments is presented . The degree of encapsulation was determined by measuring the zone of exclusion of FITC-dextran ( 2000 kDa , Sigma ) , based on the method of Gates et al . [39] . Bacteria were grown overnight on TSA II plates , swabbed into PBS , and 20 uL of bacteria were mixed with 2 uL of a 10 mg/mL stock solution of FITC-dextran , and used to create wet mounts with cover slips . The slides were viewed on a Nikon Eclipse 80i with a 100× objective , and fluorescent images were captured with a Spot RT SE camera . The images were analyzed with UTHSCSA ImageTool for Windows v3 . 0 ( University of Texas Health Science Center in San Antonio ) . The area of FITC exclusion was determined , excluding chains and clumps of cells . For each serotype , the mean area of 100–250 cells was determined , and at least two images were collected from each of at least two independently prepared slides . The capsular zone was also visualized with India ink ( Higgins , Oak Brook , IL ) wet mounts , as described previously [57] . When visualized with this method , the bacterial body is surrounded by a bright halo , which results from light diffraction , and this halo is surrounded by a zone of clearance representing the capsule . Strains were grown in 10 mL semi-defined media with 10 mM of glucose or fructose to mid-log phase and centrifuged . The concentration was adjusted to OD620 = 0 . 6 in 1 mL , and the suspension was lysed with 0 . 1% deoxycholate for 30 minutes and incubated with 100 U mutanolysin overnight at 37° [58] . Cell-associated type 19F capsular polysaccharide and phosphocholine were quantified using an inhibition ELISA , based on the method of Wessels et al [59] . Briefly , immulon-4 plates ( Thermo Scientific , Waltham , MA ) were coated by incubating overnight with 5 µg/mL cell wall polysaccharide ( Statens Serum Institute , Copenhagen , Denmark ) or 2 µg/mL type 19F capsular polysaccharide ( ATCC , Manassas , VA ) . TEPC15 ( Sigma , mouse monoclonal IgA directed against phosphocholine , 1:5000 ) or typing serum 19b ( Statens , 1:10 , 000 ) was mixed 1:1 with bacterial lysates or standard dilutions of type 19F polysaccharide or cell wall , as appropriate . TEPC15 was detected with goat anti-mouse IgA-HRP ( Southern Biotech , Birmingham , AL ) , and typing sera was detected with goat anti-rabbit IgG-HRP ( Southern Biotech ) ( 1:8000 ) . After developing with TMB substrate ( KPL , Gaithersburg , MD ) and addition of 1N HCl , the absorbance at 450nm was determined . 5 week old C57/BL6J ( Jackson Laboratories , Bar Harbor , ME ) were inoculated intranasally as described previously [31] . Briefly , mice were challenged with 3 . 5×106 CFU of strain TIGR4:14 and 3 . 5×104 CFU of strain TIGR4:19F . At one week post-inoculation , mice were euthanized by CO2 inhalation and tracheal washes were collected from the nostrils . Colonization density of each strain was determined by colony morphology and serotype was confirmed by latex agglutination ( Miravista Diagnostics , Indianapolis , IN ) . All animals were handled in strict accordance with good animal practice , and all animal work was approved by the Harvard Institutional Animal Care and Use Committee . Chemical structures of the capsular polysaccharide units were obtained from Kamerling [60] . Structural data was not available for serotypes 23A , 23B , 35F or 38 , so they were excluded from the analysis . Pathways for central metabolism were obtained from www . biocyc . org [61] . Capsule-specific sugar precursor biosynthesis pathways were obtained from Bentley et al [62] and Aanensen et al [63] . For each serotype , we calculated the number of high-energy bonds ( ATP , UTP , CTP , TTP ) and the number of carbons required to generate each of the sugar precursors and subsequently the number of carbons and high-energy bonds required to generate one polysaccharide repeat unit ( Table S1 ) . We included high energy bonds required for importing carbon and glutamine and for converting monosaccharides to NDP-sugars . For energy calculations , we ignored acetate and pyruvate , since they are byproducts of normal metabolism . Carriage prevalence data were obtained from [12] , [17] , [44] , [64] . We calculated serotype frequency among carriage isolates and averaged among the three studies to minimize the effect of microepidemics . Only serotypes with frequency greater than 1% among carriage isolates were included in correlations between prevalence and biochemical properties . Percent survival between strains was compared using either t-tests or ANOVA , as appropriate . Non-parametric Spearman correlation was used to evaluate the relationship between epidemiologic measures and resistance to neutrophil mediated killing or chemical composition of the capsule . For post-PCV7 correlations , serotypes targeted by the vaccine and those lacking structural information were ignored . Linear regression was used to evaluate the relationship between neutrophil-mediated killing of isogenic capsule switch variants and clinical carriage isolates . Analyses and graphing were performed in Graphpad Prism v5 . 0 . | Streptococcus pneumoniae , or pneumococcus , is an important pathogen worldwide and causes a wide range of diseases , mostly in young children and the elderly . There are 91 serotypes of pneumococcus , each of which produces a unique polysaccharide , called the capsule , that attaches to the bacterial surface and prevents it from being cleared by the host . The serotypes differ greatly in their prevalence in the human population . There is currently a vaccine , effective in infancy , which targets seven clinically important serotypes , but several types not covered by the vaccine are beginning to increase in carriage frequency . As a result , it is critical to understand why some serotypes are frequently carried in the human population while others are not . In this study , we find that the high-prevalence serotypes tend to be more heavily encapsulated and more resistant to killing by neutrophils . Significantly , we find that the biochemical properties of the different polysaccharides can be used to predict their carriage frequency both before and after introduction of the vaccine . These results provide a biologically plausible explanation for differences in prevalence between serotypes . | [
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"... | 2009 | Pneumococcal Capsular Polysaccharide Structure Predicts Serotype Prevalence |
Following domestication , livestock breeds have experienced intense selection pressures for the development of desirable traits . This has resulted in a large diversity of breeds that display variation in many phenotypic traits , such as coat colour , muscle composition , early maturity , growth rate , body size , reproduction , and behaviour . To better understand the relationship between genomic composition and phenotypic diversity arising from breed development , the genomes of 13 traditional and commercial European pig breeds were scanned for signatures of diversifying selection using the Porcine60K SNP chip , applying a between-population ( differentiation ) approach . Signatures of diversifying selection between breeds were found in genomic regions associated with traits related to breed standard criteria , such as coat colour and ear morphology . Amino acid differences in the EDNRB gene appear to be associated with one of these signatures , and variation in the KITLG gene may be associated with another . Other selection signals were found in genomic regions including QTLs and genes associated with production traits such as reproduction , growth , and fat deposition . Some selection signatures were associated with regions showing evidence of introgression from Asian breeds . When the European breeds were compared with wild boar , genomic regions with high levels of differentiation harboured genes related to bone formation , growth , and fat deposition .
The domestic pig is an important livestock species and an important protein source worldwide . The pig originated from the wild boar , Sus scrofa , by multiple independent domestications , mainly in Asia Minor , Europe and East Asia [1] , [2] . Domestication and subsequent selective pressures altered the behaviour and phenotypic characteristics of these animals [3] . Local pig types were developed in Europe and Asia after domestication , but the development of phenotypically distinct breeds chiefly occurred with the commencement of organised breeding in the 18th century [4] . Strict organised breeding was adopted to improve and develop livestock breeds and Britain in particular was a main centre of the early improvement of pig breeds [5] , [6] , as a reaction to increasing demand for meat in the wake of the industrial revolution . From the 18th century pig breeds were selectively bred for specific production traits such as early maturation , rapid growth and increased prolificacy . In addition , the coat colour phenotype ( which includes both skin and hair pigmentation ) was another morphological trait often used during the selective breeding process . Substantial morphological changes occurred in breeds over a short period of time , resulting in the development of numerous distinct pig breed phenotypes in Britain . Charles Darwin commented on the rapid morphological changes in pig breeds at that time: “Chiefly , in consequence of so much crossing , some well-known breeds have undergone rapid changes; thus , according to Nathusius […] the Berkshire breed of 1780 is quite different from that of 1810; and , since this latter period , at least two distinct forms have been borne the same name . ” [4] . Although breeds tended to be formed by complex crossing with numerous other breeds , including a number from Asia , to introduce desirable traits [4]–[6] , after improvement the breeds were kept distinct , resulting in highly specialised phenotypically distinct and genetically differentiated pig breeds [7] . From the 20th century , with the recognition of the benefits of genetic improvement and changing consumer preferences , certain pig breeds experienced further strong selection for lean meat content , muscularity and enhanced reproduction [5] , [6] . To better understand the genetic basis for phenotypic variation in the pig , studies have focused on important traits relevant to the breed development process with the aim of identifying , characterising and mapping candidate genes , and subsequently identifying the underlying causal mutations and allelic differences between breeds [8] , [9] . Studies mapping quantitative trait loci ( QTL ) have particularly focused on muscle growth . Fine mapping of one of these regions ( SSC2 ) identified a causal mutation in the IGF2 gene , where a single nucleotide change is associated with high muscle content in some commercial pig populations [10] . The level of fat on the carcass is also a production trait of economic impact and QTL studies have mapped loci associated with fat deposition to various chromosomes , in particular SSC4 and SSC7 [11] , [12] . Reproductive traits have received attention in pigs with several genes investigated in relation to litter size and the number of teats ( ESR , PTHLH and PTHR1 ) [13] . Coat colour is considerably varied amongst breeds within domesticated animal species and investigations into the genetics of pigmentation have identified numerous loci influencing these traits [8] , [9] . Variation at two genes , KIT and MC1R , is associated with a variety of pig breed colour types including red , black and white colouring and belted and spotted phenotypes [14]–[16] . With growing genomic resources , selection mapping approaches are increasingly being implemented to identify genetic variants that underlie the phenotypic diversity in domesticated animals . These approaches involve scanning the genome for levels of population differentiation and diversity [17] . Genome-wide scans for signatures of diversifying selection in livestock species have detected signals revealing candidate genes related to morphological variation such as body size , skeletal formation , cranial structure and coat patterns , and production traits such as muscle conformation and milk yield [18]–[25] . To further explore the genetic variation underlying the phenotypic diversity of pig breeds , a genome-wide scan of a diverse set of commercial and traditional British/European pig breeds was performed to identify genomic regions showing signatures of between-breed ( diversifying ) selection using levels of breed genetic differentiation ( FST ) . Based on these results , sequence data from three candidate regions was analysed to investigate potential causative variants .
A genome-wide scan for signatures of selection in 13 European pig breeds ( Table 1 ) was carried out by estimating Wright's FST , a measure of population genetic differentiation , at each genetic marker . After adopting a sliding window approach , candidate regions that may have experienced diversifying selection were identified by taking the 99th percentile of the empirical distribution of FST–windows ( Figure S1 ) . A total of 491 FST–windows per breed were deemed as outlier regions and as many were adjacent SNPs that clustered together , a total of 446 genomic regions displayed strong breed differentiation . The genome-wide scan revealed five genomic regions of extremely high levels of differentiation that overlapped in five or more breeds; all of these regions contain biologically interesting candidate genes ( Table 2 ) . One such region was observed in eight breeds on SSC5 ( 32 . 32–34 . 06 Mb ) . In all but two of the breeds , the peak FST–window ( ∼32 . 6–32 . 8 Mb ) overlapped with the genes WIF1 ( 32 . 66–32 . 72 Mb ) and LEMD3 ( 32 . 77–32 . 89 Mb ) . This region is orthologous to a region in dogs associated with ear morphology [19] , [24] . Another region was detected in five breeds on SSC7 ( 54 . 00–57 . 00 Mb ) , where at the 97 . 5th percentile a further four breeds also exhibited a signal . On SSC8 , a region of high differentiation spanning 71 . 84–75 Mb was observed in nine breeds . More striking was the extended region of differentiation on SSC8 spanning 40–75 Mb observed in most breeds , with numerous overlapping and non-overlapping peaks of FST across a large genomic region on that chromosome ( Figure S1 ) , although fewer than five breeds overlapped directly in their peak FST–windows , except in the narrow interval mentioned above . Duroc was the only breed that did not show high levels of differentiation in this region , or even on that chromosome , at either the 99th or 97 . 5th percentile . Outlier regions were also found on SSC15 ( 139 . 60–142 . 10 Mb ) , observed in six breeds , and on SSC16 ( 18 . 72–20 . 63 Mb ) , observed in five breeds . Most extreme genomic regions were observed in fewer breeds ( 1–4 ) ( Figure S1 ) and we highlight examples of those found in the within-breed 99 . 9th percentile that overlapped QTLs and contained biologically interesting genes ( Table S1 ) . The Duroc breed exhibited several signatures of diversifying selection on two chromosomes . On SSC14 a highly differentiated region ( 123 . 08–123 . 41 Mb ) overlapped with QTLs for fatty acid composition in Duroc [26] , [27] and includes a gene involved in fatty acid biosynthesis , ELOVL3 ( 123 . 08–123 . 083 Mb ) [28] . On SSC15 a highly differentiated genomic region ( 85 . 73–86 . 62 Mb ) contained the MYO3B ( Class III myosin B ) gene ( 85 . 63–85 . 93 Mb ) , which directly overlapped the peak FST-window ( 85 . 83 Mb ) . An extended differentiated genomic region was observed in the Landrace breed on SSC13 , with the highest FST–window occurring at 73 . 06 Mb , close to the GHRL gene ( 73 . 47–73 . 48 Mb ) . In addition , QTLs related to various reproductive traits in pigs have been mapped to SSC13 [29] and overlap with the extended differentiated genomic region . Large , breed-specific signatures of diversifying selection were not limited to the commercial breeds , but also were observed in the traditional breeds ( Table S1 ) . Gloucestershire Old Spots displayed a signal of diversifying selection on SSC11 , close to EDNRB ( 54 . 69–54 . 72 Mb ) , a gene implicated in coat colour pattern in mammals [30] . Near the peak FST–window ( 55 . 20 Mb ) many SNPs in this region were fixed in this breed whereas alleles were segregating in all other pig breeds ( Figure 1 ) . A weaker signal in the region of this gene ( seen in the 99th but not 99 . 9th percentiles ) appeared in Mangalica and British Saddleback breeds ( Figure S1 ) . Another breed-specific signature of selection was observed on SSC5 at a different coat colour locus in the Berkshire . KITLG ( KIT ligand , 98 . 74–98 . 78 Mb ) was just upstream from a 99 . 9th percentile FST–window ( 98 . 84 Mb ) on SSC5 and KITLG fell within the 99th percentile differentiation region . Many SNPs in the region of this gene were almost fixed for the same allele in Berkshire and the Asian breed , Meishan , whilst alleles were segregating in the other European pig breeds ( Figure 1 ) . Levels of genetic differentiation were examined between the European pig breeds and wild boar ( Table 1 ) . None of the SNPs were found to be fixed for alternative alleles in the pig breeds and wild boar . The genome-wide distribution of FST for domestic pig breeds compared with wild boar is shown in Figure 3A . FST–windows falling into the 99th percentile were viewed as candidates of signatures of selection ( Table S11 ) and contained some biologically interesting genes , as described below . A genomic region on SSC1 showed high levels of differentiation ( 1 . 07–3 . 19 Mb , Table S11 ) , homologous to a region of the canine genome associated with brachycephaly ( broad and short skull shape ) in dog breeds [31] , [32] . This region contains , amongst seventeen characterised and uncharacterised genes , THBS2 ( 1 . 59–1 . 62 Mb ) and SMOC2 ( 2 . 23–2 . 24 Mb ) , which were suggested as candidates for brachycephaly in the above-mentioned papers ( Figure 3B ) . Pairwise FST–SNPs between wild boar and each breed in this region ( 48 SNPs ) revealed maximum breed average FST values for Tamworth ( 0 . 42 ) , Welsh ( 0 . 43 ) and Landrace ( 0 . 45 ) , none of which have extremely brachycephalic skulls . A highly differentiated genomic region was also observed on SSC7 ( 31 . 30–38 . 89 Mb , Table S11 ) . This region is close to the pig major histocompatibility complex: class I ( ∼24–26 Mb ) , class II ( ∼29 Mb ) and class III ( ∼27 Mb ) . Within the differentiated region there are several genes of biological interest , including PPARD ( 36 . 14–36 . 22 Mb ) ( Figure 3C ) . Pairwise FST–SNPs ( 207 ) between wild boar and each breed in this region revealed highest breed average FST–SNPs in two commercial breeds , Duroc ( 0 . 50 ) and Landrace ( 0 . 37 ) , and one traditional breed , Large Black ( 0 . 38 ) ; the minimum value of breed average FST was in Tamworth ( 0 . 09 ) . Another interesting differentiation region observed between the domestic pigs and wild boar was on SSCX ( Table S11 ) . Amongst other genes , this region contained AR ( 60 . 31–60 . 50 Mb ) , the androgen receptor , previously suggested as a candidate gene for backfat thickness in pigs due to its proximity to mapped QTLs [33] . Other regions showing substantial differentiation between wild boar and pig breeds were found on SSC12 , SSC13 and SSC14 but no clear candidate genes could be identified . Consistent with previous studies [34] , [35] , genome-wide clustering results indicated substantial Asian ancestry for the European breeds . The clustering results indicated that the inferred ancestry of all Meishan individuals ( a breed of Chinese origin , Table 1 ) to the first ( “Asian” ) cluster was high ( 92 . 3–93 . 9% ) . In contrast , the inferred ancestry of the European individuals to the second ( “European” ) cluster was lower ( breed averages ranged from 69 . 6% for Large White up to 87 . 3% for Mangalica ) . With levels of ancestry varying across the genome , regions with particularly strong signals of Asian introgression into European breeds were identified according to two criteria: ( 1 ) high introgression probabilities ( 99th percentile ) calculated by STRUCTURE software and ( 2 ) low differentiation based on FST ( below the 1st percentile of individual European breeds versus Meishan ) ( Table S12 ) . Two candidates of introgression overlapped with signals of selection associated with ear morphology . A genomic region on SSC5 ( 32–35 Mb ) , overlapping the region of differentiation detected when prick-eared breeds were contrasted with flat-eared breeds , was found in Gloucestershire Old Spots , Large Black and Mangalica ( a signal of introgression in British Saddleback , the other flat-eared breed , was observed in this region only in the FST analysis ) . A genomic region on SSC7 ( 33–38 Mb ) , overlapping with one of the regions of differentiation detected when prick-eared breeds were contrasted with intermediate-eared breeds , was found in British Saddleback , Duroc , Landrace and Welsh . Another signal of introgression was detected on SSC11 ( 54–55 Mb ) in Gloucestershire Old Spots , which overlapped with the differentiated region found in this breed and may be associated with coat pattern . The chromosome with the greatest number of regions showing evidence of Asian introgression was SSC14 , where several regions overlapped across multiple breeds ( 81–85 Mb , eight breeds; 93–94 Mb , four breeds; 96–98 Mb , three breeds; 103–107 Mb , three breeds ) . Based on the differentiation results , three genomic regions were further investigated using genome sequence data for 76 individuals from European and Asian breeds ( Table S13 ) .
Signatures of diversifying selection were found for traits related to morphological variation described by breeding criteria . Ear morphology is one trait that plays a major role in pig breed standards with strict conditions over ear form . By grouping breeds based on this phenotypic trait , the genome-wide scan suggested that the genetic basis of ear variation in pigs involves at least three genomic regions , located on SSC5 and SSC7 . The region on SSC5 was associated with the difference between prick or intermediate ears and large , flat ears and the signals on SSC7 were associated primarily with the differences between prick- and intermediate-eared breeds . Our results from an introgression analysis also suggest that the SSC5 region of flat-eared breeds derives from Asian pigs . The signatures of selection associated with ear morphology concurred with an earlier QTL study of the trait in pigs [38] . The SSC7 QTL of Wei et al [38] overlaps directly with the first differentiated region ( 31 . 82–34 . 19 Mb ) on that chromosome . The suggestion that PPARD located on SSC7 plays a role in ear variation in pig breeds could not be supported as it was not positioned near either of the two signals of selection identified on this chromosome . However , as PPARD is involved in many biological processes and is located next to major QTLs for fat deposition and growth , its role in ear morphology warrants further investigation [39] . The QTL peak on SSC5 reported by Wei et al [38] is located approximately 10 Mb upstream of the peak FST signal but the confidence interval for the QTL location could overlap this position . Genome-wide association studies ( GWAS ) on ear morphology in dog breeds identified a region underlying this trait that was syntenic to the region on SSC5 in this study [19] , [24] . Both these studies suggest MSRB3 and HGMA2 as candidate genes due to the proximity of the associated SNP . However , in the pig breeds the peak signal was located closer to LEMD3 , which is involved in bone morphogenetic protein ( BMP ) signalling . Recently , a fine mapping study in pigs has suggested HMGA2 as a candidate locus for this QTL [40] . Mutations in the human version of this gene are associated with disorders involving increased bone density , suggesting a possible role in bone development [41] . However , analysis of coding sequences of these genes in this region of SSC5 for prick- and flat-eared pig breeds did not reveal any shared non-synonymous differences between the two groups , suggesting that changes in regulatory elements or miRNA genes may be responsible . Expression studies are required to test this hypothesis . Like ear morphology , variation in coat colour patterns occurred post-domestication and signals of selection related to the traits indicate strong historic selection for the different phenotypes . Molecular studies have already identified the major coat colour loci in pigs , KIT and MC1R , for which allelic variation is associated with many of the coat colour variants ( see references in [9] , [17] ) . However , in this study signals of selection were not observed at or near MC1R ( SSC6 ) for individual breeds that have an allele associated with a particular coat colour or when breeds were grouped by coat colour traits . The other locus , KIT ( SSC8 ) , is found ∼1 Mb downstream from a differentiated region shared by three breeds ( British Saddleback , Hampshire , Pietrain ) . Several possible explanations could account for weak and absent signals of diversifying selection at KIT and MC1R , respectively . The differentiated region on SSC8 was quite extensive in genomic size and KIT may have been one of several targets of selection in that region , thus dampening any KIT-specific signals . Furthermore , allelic variation at both KIT and MC1R is associated with a large variety of coat colours and patterns for many breeds . With the breed set analysed in this study , there is no simple dichotomous division of the breeds based on coat type for these two genes , which could have weakened the power of this approach . Lastly , the inter-SNP distances in the MC1R region of SSC6 were particularly high ( the distance between the flanking markers was in the 99th percentile of the genome-wide distribution of inter-SNP distances ) . Thus it appears that the MC1R region was not well covered by the PorcineSNP60 chip , which may explain why no signals of diversifying selection were detected there . In contrast to the weak or absent signals of selection at the two major coat colour loci , KIT and MC1R , strong breed-specific signals of diversifying selection were observed near other coat colour loci . Two non-synonymous mutations were found in the endothelin receptor B ( EDNRB ) gene , in a region exhibiting substantial differentiation unique to Gloucestershire Old Spots . EDNRB encodes a G protein-coupled receptor that binds to the different isoforms of endothelins . The EDNRB-endothelin interaction plays a role in a range of critical physiological processes including the formation of enteric nerves and melanocytes ( pigment-producing cells ) , both of which are neural crest derivatives [42] , [43] . Mutations in EDNRB , leading to a reduced expression of the gene and partial or complete loss-of-function , have been shown to be associated with changes in pigmentation due to its role in melanocyte development [43] , [44] . The piebald phenotype in mouse , characterised by white coat spotting [43] , results from the insertion of a large retrotransposon in the first intron of EDNRB [45] . Several different mutations in humans are associated with a loss of pigmentation in the hair , skin and iris ( Hirschsprung's disease/Waardenburg syndrome ) [43] while a missense mutation gives rise to the Lethal White Foal Syndrome [46] , where homozygous foals are completely white ( and die early due to intestinal blockage ) while heterozygous animals usually have distinctive white patches . The mechanism ( s ) by which point mutations in EDNRB could be associated with ( partial ) loss of function is not yet known . The amino acid changes at residues 64 ( Jinhua ) and 68 ( Gloucestershire Old Spots and Xiang ) are both located in the N-terminal extracellular domain of the protein . One of the non-synonymous EDNRB mutations associated with Hirschsprung's disease is located in the same domain , at residue 57 . This domain has been suggested to be important for stable ligand binding [47]–[49] . Furthermore , human EDNRB is believed to be cleaved by a metalloprotease at R64|S65 ( R65|S66 in pig ) and a truncated EDNRB ( missing the first 64 residues ) was found to be functional but had significantly reduced cell surface expression [50] . Using a program that predicts cleavage sites by membrane-type metalloproteases ( SitePrediction , [51] ) , the reference pig EDNRB with S68 was , like its human homologue , found more likely to be cleaved at the R65|S66 site than the Gloucestershire Old Spots protein with F68 ( unpublished results ) . The SNPs that alter residues 64 and/or 68 may result in an incomplete or uncleaved EDNRB and hence altered expression on the cell surface . Black spotting in the Gloucestershire Old Spots has been previously associated with the EP allele at the melanocortin receptor 1 ( MC1R locus ) : a 2-bp insertion in MC1R causes a frameshift mutation which results in a premature stop codon further downstream [15] . That study also demonstrated irregular somatic reversion to the black form of MC1R in two spotted breeds , Pietrain and Linderod , such that some regions of the body ( black spots ) expressed the form of the protein that enables black pigment production , whereas other ( white ) regions mainly expressed the mutated ( non-functional ) form of the protein . However , as breeds with various spotted and non-spotted patterns carry the 2-bp insertion , it is likely that additional loci also influence coat pattern and colour in these breeds . A recent paper demonstrated the complex interactions between melanocortin and endothelin signalling in determining coat patterns in cats [52] and similar interactions may also influence coat pattern diversity in pigs . We propose that the variant MC1R , resulting from the 2-bp insertion ( and somatic reversion ) , may interact with partial loss of function in EDNRB such that only part of the body is populated by melanocytes which have the potential to revert and become pigmented . This in turn could give the Gloucestershire Old Spots its characteristic spotting pattern of relatively few and small spots compared to those observed , for example , in Pietrain . Functional analyses are required to characterize the effects of the Gloucestershire Old Spots variants on EDNRB function and on pigmentation patterns . Although the variants at EDNRB were unique to the Gloucestershire Old Spots in the analysis of European breeds , they were shared by the Asian breed Xiang . We do not have phenotypic information for the Xiang individual who shares the Gloucestershire Old Spots variants but one of the most common Xiang subtypes is two-end black with a white middle body , akin to the familiar piebald mouse ( http://www . viarural . com . pe/ganaderia/a-porcinos/exteriorcerdos/paises/china . htm ) . The Jinhua breed , which carries a proline to serine change at nearby residue 64 ( Figure 4; [53] ) , has a similar phenotype . The difference in the phenotypes between the Asian breeds and Gloucestershire Old Spots is likely to be related to their different MC1R genotypes . The Asian breeds with EDNRB mutations do not carry the MC1R insertion ( unpublished results ) , consistent with previous studies that show a low frequency or absence of this allele in Asian pigs [54] , [55] . The two Gloucestershire Old Spots individuals are substantially more similar to the Asian breeds than the European ones in the EDNRB region . This finding , the shared EDNRB genotypes of Gloucestershire Old Spots and Xiang , and the introgression results described above together suggest an Asian origin for the Gloucestershire Old Spots mutations . A putatively selected region identified in the Berkshire breed includes the KITLG locus and further sequence analysis revealed several non-synonymous variants in this breed . KITLG binds to the KIT receptor and plays a role in the melanocyte production pathway . Variation at the locus has been implicated in different skin pigmentation phenotypes in mice ( i . e . steel mutant ) [44] , [56] and humans [57] , [58] , including hypo- and hyper-pigmentation , and has been investigated previously for its role in pig colouration [59] . The breed standard for Berkshire is a black animal with six white points ( on the snout , tip of the tail and tips of each of the legs ) . The Berkshire was allegedly highly variable in coat colour until introgression of Asian genetic material and selection for breed homogeneity led to its contemporary coat pattern [5] , [6] . Our tests using PorcineSNP60 data did not detect evidence of Asian introgression for Berkshire in the KITLG region ( as assessed using comparisons with Meishan ) , although Berkshire shared the C1089T variant with Jiangquhai , another Asian breed , but not with any other European or Asian individuals . Furthermore , the two non-synonymous variants found in Berkshire were more common in the Asian than the European breeds . Similarly , Okumura and colleagues [37] , [60] found evidence for an Asian origin of KITLG in Berkshire , as the breed shared haplotypes similar to Asian breeds at the locus whilst differing from other European breeds . We identified the same two non-synonymous variants ( A919G , G458A ) in Berkshire and several Asian breeds as Okumura and colleagues [37] , [60] . However , these variants cannot on their own explain the Berkshire phenotype because they were also found in three European individuals , including a Pietrain and a Tamworth ( both homozygous ) , the latter breed which is red . Alternatively , the Berkshire phenotype might be attributed to differential regulation of KITLG , in conjunction with variation at other pigmentation genes ( e . g . MC1R—Berkshire also carries the black spotting allele discussed above—and KIT ) . This could be related to the C1089T 3′-UTR variant that was only seen in Berkshire and Jiangquhai ( also a black breed ) or another regulatory element . Cis-regulatory differences in KITLG expression have been associated with pigmentation differences in stickleback fish [61] and a SNP located 350 Kb upstream of the KITLG gene was found to be associated with human hair colour , suggesting a possible regulatory role [62] . However , we were unable to search for variants in either proximal or distant enhancer/repressor elements due to errors in this region of the current pig genome assembly . Signatures of diversifying selection were found that may be associated with important pig production traits . Teat number is an important reproductive trait because with increased litter size , which is often selected for in pig breeds , a sufficient number of teats are required to support the litter [13] . Although the FST teat-trait analysis results had some ambiguity , the signal on SSC12 seen in the 14 vs 12 teats comparison but not the ‘control’ comparison ( breeds with 14 teats compared with one another and breeds with 12 teats compared with one another ) overlapped with documented QTL . Both Hirooka et al [63] and Rodriguez et al [64] reported a significant QTL for teat number on this chromosome , with the latter study suggesting that the most likely position of the QTL was between markers SW874 ( 23 . 67 Mb ) and SW1956 ( 40 . 77 Mb ) , which overlapped with the region of high differentiation observed in the current study . The NME1 gene , which is found in this region ( 27 . 46–27 . 50 Mb ) , plays a role in mammary gland development . NME1-deficient mice , although they reproduce normally , have delayed mammary gland development [65] and incomplete maturation of the lactiferous duct in the nipple [66] . Amongst the production characteristics that commercial pig breeds share , they also possess breed-specific characteristics . Duroc pigs are known for their high intramuscular fat content ( IMF ) in comparison to other commercial pig breeds [67] and for their higher concentrations of saturated and mono-unsaturated fatty acids ( and lower concentrations of poly-unsaturated fatty acids ) [68] , characteristics that play key roles in meat quality . Uemoto et al [27] found a significant QTL for fatty acid composition in Duroc on SSC14 that has not been reported for other breeds . This QTL region overlaps with an extreme differentiation region observed only in the Duroc breed and contains ELOVL3 , a gene involved in the synthesis of fatty acids; in mice a lack of ELOVL3 resulted in decreased levels of certain fatty acids due to an inability to convert saturated fatty acyl-CoAs into very long chain fatty acids [28] . In addition , SCD ( stearoyl-CoA desaturase ) , a gene located close to the peak differentiation region , encodes a key enzyme in the synthesis of fatty acids and has thus been proposed as a candidate gene for the fatty acid composition QTL [27] . Landrace also exhibited high levels of differentiation , in this case in an extended region of SSC13 . The peak differentiation values were found close to the grehlin ( GHRL ) gene , which is a candidate for associations with appetite and feeding behaviour . The regulation of voluntary food intake is controlled by a biological cascade of chemical signals that controls appetite and satiation , where various hormones are involved in the starting and/or termination of an eating episode . Grehlin has been specifically proposed in prompting hunger feelings and therefore initiating eating [69] . Its involvement in regulating feeding behaviour in pigs has only recently been considered [70] . By comparing pig breeds with their ancestral species , the wild boar , we sought to identify genomic regions and genes that could be involved in the domestication process . The largest differentiated genomic region between the domestic pig breeds and wild boar was observed on SSC7 . Numerous QTLs have previously been mapped to this chromosome for traits such as growth , carcass length , skeletal morphology and backfat depth using several types of crosses [11] , [12] . Several genes located in the region of differentiation have been investigated for possible physiological roles: PPARD and CDKN1A have been considered candidates for fat deposition [71] and , as mentioned above , PPARD has also been considered a candidate gene for ear structure variation [39] . In addition , the genomic signal of selection is close to the MHC region , a complex that is crucial in vertebrate immunity , making it a potential source of evolutionary change on the chromosome . The large differentiated region on SSC7 may reflect strong diversifying selection in domestic pig breeds as this chromosome appears to influence many pig production traits . Domestic pig breeds are also different from wild boar in skeletal morphology . Substantial changes have occurred in the body and cranial dimensions following domestication [72] . In the comparison of pig breeds with wild boar , a region of genetic differentiation identified on SSC1 is syntenic to a region associated with cranial dimensions in dog breeds [32] . The cranial trait under investigation in the dog studies , brachycephaly , is characterised by a strong alteration of the facial bone structure through shortening of the muzzle and shortening and widening of the skull [31] . Pig breeds possess variable skull morphology ranging from a long snout ( Tamworth ) to shorter wider faces ( Berkshire , Gloucestershire Old Spots , Large Black ) to very short faces with upturned snouts , similar to brachycephaly in dogs ( Middle White ) ( see Figure S1 ) . However , Middle White , the most brachycephalic-like breed , did not show significant differentiation from wild boar in the SSC1 region . Incidentally , it has been suggested that Middle White acquired its ‘dished’ face from Asian pigs [6] . However , there was no evidence of Asian introgression into the Middle White in the regions orthologous to the dog brachycephaly regions , suggesting that if it did indeed acquire its squashed face from Asian pigs , there has been independent evolution for this trait in dogs and pigs . As various skeletal and cranial changes occurred after domestication of the wild boar [72] , the region of high differentiation overlapping the brachycephaly region in dogs could be associated with other bone alterations . The putative genomic signatures of selection for breed-defining phenotypic traits and levels of breed genetic differentiation reflect the historical development of the pig breeds . The Duroc had the strongest signals of diversifying selection , evidenced by the levels of genomic differentiation , which were observed to be unique to this breed and unlike the other breeds , no signals of diversifying selection were observed on SSC8 for the Duroc , indicating that this breed may have a distinct genetic origin , as previously noted from microsatellite and sequence data [35] , [73] . Some of the clearest signals of both diversifying selection and introgression from Asian pigs were associated with highly visible phenotypes such as coat pattern and ear morphology , suggesting that these traits have been under particularly strong selection during the development of European pig breeds . In particular , selection associated with flat ears was detected in breeds that do not appear to share recent ancestry [7] , [73] , which may reflect convergent evolution through independent selection for that trait . In contrast , although microsatellite markers indicate a common ancestry for Berkshire and Gloucestershire Old Spots [7] , [73] , shared differentiation signals were not seen , illustrating differing breed development trajectories . Signatures of selection were also observed in regions associated with certain quantitative traits in pig production , but there was a paucity of signals at loci associated with those related to reproduction . The lack of differentiation signals associated with such traits may reflect their control by many genes of small effect , as suggested by Boyko and colleagues [19] . The genomic regions identified in this study using the genetic differentiation approach generally did not overlap with those identified in a scan for extreme homozygosity in European pig breeds: none of the regions identified in five or more breeds overlapped with the regions reported by Rubin and colleagues [25] and only two out of 109 regions identified in individual breeds overlapped ( SSC1:172 . 13 Mb and SSC15:115 . 17–115 . 77 Mb ) . The Rubin study used more dense genomic data so it is possible that the Porcine SNP60 chip did not contain variants close to the regions they identified . However , in our study we have detected what appear to be genuine signals of selection in pig breed development . Another explanation for the lack of overlap between the studies is that , by pooling genomic data across several breeds , Rubin and colleagues [25] identified regions of homozygosity that were shared amongst the breeds , arguably picking out candidates more likely to be involved in the domestication process and early , post-domestication pig development . In contrast , our methodological approach searched for between-breed differences , thus revealing candidates arising from diversifying selection that occurred during breed development .
DNA samples were obtained from blood samples collected by veterinarians according to national legislation , from tissue samples from animals obtained from the slaughterhouse or , in the case of wild boar , from animals culled within wildlife management programs . DNA samples were obtained from blood samples collected by veterinarians according to national legislation , from tissue samples from animals obtained from the slaughterhouse or in the case of wild boar , from animals culled within wildlife management programs . Samples for SNP genotyping were obtained from between 24 and 34 individuals for 14 pig breeds , described in Table 1 , and were genotyped using the PorcineSNP60 chip assay [74] . Most breed samples ( including the Asian breed , Meishan ) were from the PigBioDiv study whereby a maximum of two individuals were sampled from a litter from as many herds as possible , so as to have as few related individuals as possible in the sample set [75] . For the four commercial breeds ( Duroc , Landrace , Large White and Pietrain ) , the data was from individual commercial populations , which were found to be good representatives of the breeds based on clustering analysis of multiple populations ( unpublished results ) . Welsh samples were provided by the Pedigree Welsh Pig Society . Wild boar samples were those used in the original SNP discovery procedure [74] . Genotype data are deposited in the Dryad repository ( http://dx . doi . org/10 . 5061/dryad . c2124 ) . All analyses were carried out in R ( [76] , http://www . r-project . org/ ) . A series of quality control measures were applied to the dataset to filter out any possible genotyping anomalies . First , SNP markers that had greater than 10% missing genotypes were discarded . Second , markers that were monomorphic across all the breeds ( i . e . MAF<0 . 01 ) were also discarded from further analysis . Third , SNP markers were tested for deviations from Hardy-Weinberg equilibrium within each breed using an exact test [77] . At a critical rejection region of 8 . 33×10−7 ( 0 . 05/60 , 000 ) a total of 66 SNPs did not conform to HWE expectations in one or more breeds and were removed from the analysis . Of these , 46 deviated from HWE due to excess of heterozygote genotypes in one or more breeds . The other 20 SNPs deviated from HWE due to heterozygote deficit in one or more breeds . Fourth , markers that were not mapped to the porcine genome were removed , based on the current pig genome assembly , Sus scrofa ( SSC ) Build 10 . 2 . For the remaining markers , SNPs that were not yet mapped to a specific location on a specific chromosome of the pig genome were also filtered out . Following quality control , 49 260 markers were considered for the majority of analyses ( see below for one exception ) . After QC , average individual genotype coverage was 99 . 20% across all breeds and average individual genotype coverage in individual breeds ranged from 96 . 09% in the Mangalica breed to 99 . 96% in the Hampshire breed . Pairwise Wright's FST [78] , the classical measure of population genetic differentiation , was used to detect signatures of diversifying selection . We previously showed [79] that pairwise measures of differentiation were better at identifying markers that distinguished breeds than global measures and that Wright's estimate of FST was highly correlated to that of Weir & Cockerham's [80] . The use of population ( breed ) differentiation to identify candidate selected regions , as implemented in the current study , was originally suggested by Akey and colleagues [81] . This approach was justified by use of simulations in a follow-up study on dogs [18] and has subsequently been implemented in various empirical studies [22] , [24] , [82] . The PorcineSNP60 chip assay was designed to include SNPs evenly distributed across the genome , with per-chromosome average inter-SNP distances ranging from 30 to 40 kb ( except for SSCX ) ( based on builds 7 and 8 ) [74] , with a median of 30 kb for the genome-wide distribution . Across the genome , the majority ( 80% ) of inter-SNP distances were less than 70 kb in this study . Recent studies ( e . g . Ref . [83] ) show high linkage disequilibrium across commercial pig genomes ( r2∼0 . 4 between adjacent SNPs on the PorcineSNP60 chip ) , suggesting that our study is likely to detect most signals . To account for stochasticity in locus-by-locus variation , for all of the FST analyses a 13-SNP sliding window was implemented on the estimated values , with the mid-SNP determining the genomic location of the window ( hereafter designated as FST-window ) . To allow 13-SNP sliding windows across a whole chromosome , the first window on a chromosome was centred at the 7th SNP position and the last window on a chromosome was centred at the 7th from last SNP position . Candidate selected regions were defined as the 99th percentile of the empirical distributions of FST-windows , except where indicated otherwise . A breed average FST was first calculated . FST was estimated between pairs of European breeds at each SNP marker using the breed allele frequencies . For each breed this produced 12 breed-pairwise FST comparisons at each SNP marker . The FST at each SNP marker for all of these pairwise comparisons were averaged to produce an overall FST for each SNP marker in each breed ( here after designated as FST-SNP ) . The FST analysis was extended to compare groups with different phenotypic traits . For each trait classes were formed , based on the observed phenotypic variation between breeds ( see below ) , and breeds were placed into one of the classes . For each trait , FST was estimated between each breed in one class compared against each breed in the next class and averaged across the pairwise comparisons to obtain a FST-SNP estimate . A summary table of the different traits , the phenotypic classes and the class designation of each breed is shown in Table S2 . Ear morphology in European pigs is variable , ranging from upright or prick ears that may be slightly inclined forwards ( the ancestral state as seen in wild boar ) , to a medium sized ear that points forwards and downwards but is not too heavy , to a completely dropped ear that is long , thin and lies relatively flat against the face slightly curbing vision of the animal ( see Figure S2 ) . Ear morphology was grouped into the following classes: prick-eared breeds , intermediate-eared breeds and flat-eared breeds . Coat colour in European pigs is a highly variable phenotypic trait including from black , red , brown and white , with and without spots and belts . The coat colour was grouped into the following classes: red coat breeds compared with non-red coat breeds; saddleback breeds compared with non-saddleback breeds; white coat breeds compared with non-white coat breeds; red coat breeds compared with black coat breeds . Amongst the breed standard requirements set by the British Pig Association ( BPA ) , the number of teats is one listed criterion . Using this trait , breeds were grouped in the following classes: breeds where the BPA standards required a minimum of 14 displayed teats compared with breeds where the BPA standards required a minimum of 12 displayed teats , Berkshire and Middle White were removed from this trait comparison because there was not a definitive breed standard requirement ( breed standards suggested a “minimum of 12 but preferably 14 teats” ) and Mangalica was also removed because the breed standard number of teats was unknown . Levels of genetic differentiation between the domestic pig breeds and wild boar were estimated . The SNPs that were monomorphic in the pig breeds were compared with wild boar genotypes to determine if some were segregating in the wild boar . The ( mapped ) breed-monomorphic SNPs that were segregating in the wild boar were added to the set of polymorphic SNPs described above , giving a total of 49 556 markers . FST was estimated between wild boar and each pig breed , which produced 13 pairwise comparisons at each SNP marker . The FST at each SNP marker for each of these pairwise comparisons were averaged to produce an overall FST for each SNP marker ( here after designated as FST-SNP ) . Two methods were employed to infer signals of Asian introgression in European breeds . First , an FST analysis , as described above , was used to quantify differentiation between the Asian Meishan breed and each of the 13 European breeds . Regions of particularly low differentiation ( below 1st percentile ) were interpreted as showing evidence of Asian introgression . Second , a Bayesian analysis was performed using the site-by-site linkage model in STRUCTURE software [84] . This model was designed to infer the ‘population-of-origin’ assignment of genomic regions and has been used to determine levels of introgression between populations ( e . g . Ref . [85] ) . Each of the 13 European breeds was compared with the Meishan breed , using no a priori population information: at a pre-defined number of clusters , K = 2 , the linkage model was run five times for 20 , 000 iterations after a burn-in of 40 , 000 iterations ( which included 20 , 000 iterations with the admixture model ) . Due to computer memory limitations , for the analysis 15 individuals per breed ( approximately half of the total dataset ) were chosen at random and every second marker across each chromosome was removed from the input data set leaving a total of 24 630 markers . Ancestry proportions across the two clusters ( “Asian” and “European” ) were estimated for each of the European individuals . Estimates of Asian ancestry for each European animal for each SNP were obtained from the probability of assignment to the Asian cluster and then averaged across the individuals within each breed . As described above , a sliding window average of Asian ancestry values across each chromosome was calculated , with windows composed of 7 SNPs ( half the number used for the analyses of the full set of SNPs ) . The average value for the window was assigned to the position of the central SNP . These values were interpreted as probabilities of introgression from Asian to European breeds . In order to identify genomic regions with clear signals of Asian introgression , we identified SNP positions ( to the closest Mb ) that met two criteria: ( 1 ) values below the 1st percentile of the Meishan-European breed FST-windows distribution and ( 2 ) found in the 99th percentile of STRUCTURE-calculated introgression probabilities for that breed . DNA samples for sequencing were obtained as described above for SNP genotyping . Individual samples ( 52 ) from 12 of the European breeds analysed above ( no Welsh pigs were included ) as well as 24 samples from eight Asian breeds ( Table S13 ) were sequenced using the Illumina HiSeq2000 platform , with library preparation and sequence generation per manufacturers protocols . Sequence mapping and variant calling were carried out as described previously [25] , [34] . Briefly , Illumina ( v . 1 . 3–1 . 8 ) formatted fastq files , with sequence reads of 100 bp were subject to quality trimming prior to sequence alignment . The trimming strategy involved a 3 bp sliding window , running from 5′ to 3′ , with sequence data upstream being discarded if the 3 bp window average quality dropped below 13 ( i . e . average error probability equal to 0 . 05 ) . Only sequences of 45 bp or more in length were retained . In addition , sequences with mates <45 bp after trimming were also discarded . During trimming , quality scores were re-coded to follow the Sanger fastq format to standardize downstream processing . Sequences were aligned against the Sscrofa10 . 2 reference genome using Mosaik 1 . 1 . 0017 . Alignment was performed using a hash-size of 15 , with a maximum of 10 matches retained , and 7% maximum mismatch score , for all pig populations and outgroup species . Alignment files were then sorted using the MosaikSort function , which entails removing ambiguously mapped reads that are either orphaned or fall outside a computed insert-size distribution . Alignment archives were converted to BAM format using the Mosaiktext function . Manipulations of BAM files , such as merging of alignments archives pertaining the same individual , were conducted using SAMtools v . 1 . 12a [86] . Variant allele calling was performed per individual using the pileup function in SAMtools , and variations were initially filtered to have minimum quality of 50 for indels , and 20 for SNPs . In addition , all variants showing higher than 3x the average read density , estimated from the number of raw sequence reads , were also discarded to remove false positive variant calling originating from off-site mapping as much as possible . Heterozygous variants and those with minimal SNP/indel qualities were further inspected manually to ensure that they were true variants . We examined the sequence variation in three genomic regions that showed extreme differentiation in one or more breeds ( Table S1 ) for the individuals from the 12 European breeds: ( 1 ) SSC5:31 . 0–34 . 0 , ( 2 ) SSC5:98 . 0–99 . 0 and ( 3 ) SSC11:53 . 5–55 . 5 Mb . Information for the relevant regions was excised from the BAM files using SAMtools v . 1 . 12a [86] . Alignment files and variants called in these regions for all animals considered in this manuscript are deposited in the Dryad repository ( http://dx . doi . org/10 . 5061/dryad . c2124 ) . For the first region , we identified all variants that were shared by the individuals from flat-eared breeds but differed from all individuals from the prick-eared breeds ( Table S2 ) ; for the second region , we identified all variants that were shared by the two Berkshire individuals but differed from the other individuals; and for the third region , we identified all variants that were shared by the two Gloucestershire Old Spots individuals but differed from the other individuals . Data for the individuals from Asian breeds was then used to examine specific sequence variants , as described in the Results . | The domestic pig , an important source of protein worldwide , was domesticated from the ancestral wild boar in multiple locations throughout the world . In Europe , local types were developed following domestication , but phenotypically distinct breeds only arose in the eighteenth century with the advent of systematic breeding . Recently developed molecular tools for pigs ( as well as other livestock species ) now allow a genetic characterisation of breed histories , including identification of regions of the genome that have been under selection in the establishment of breeds . We have applied these tools to identify genomic regions associated with breed development in a set of commercial and traditional pig breeds . We found strong evidence of genetic differentiation between breeds near genes associated with traits that are used to define breed standards , such as ear morphology and coat colour , as well as in regions of the genome that are associated with pork production traits . It is well documented that crosses with Asian pigs have been used to modify European breeds . We have found evidence of genetic influence from Asian pigs in European breeds , again in regions of the genome associated with breed standard characteristics , including ear shape and coat colour , as well as production traits . | [
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] | 2013 | Signatures of Diversifying Selection in European Pig Breeds |
Drug resistance is a major problem in leishmaniasis chemotherapy . RNA expression profiling using DNA microarrays is a suitable approach to study simultaneous events leading to a drug-resistance phenotype . Genomic analysis has been performed primarily with Old World Leishmania species and here we investigate molecular alterations in antimony resistance in the New World species L . amazonensis . We selected populations of L . amazonensis promastigotes for resistance to antimony by step-wise drug pressure . Gene expression of highly resistant mutants was studied using DNA microarrays . RNA expression profiling of antimony-resistant L . amazonensis revealed the overexpression of genes involved in drug resistance including the ABC transporter MRPA and several genes related to thiol metabolism . The MRPA overexpression was validated by quantitative real-time RT-PCR and further analysis revealed that this increased expression was correlated to gene amplification as part of extrachromosomal linear amplicons in some mutants and as part of supernumerary chromosomes in other mutants . The expression of several other genes encoding hypothetical proteins but also nucleobase and glucose transporter encoding genes were found to be modulated . Mechanisms classically found in Old World antimony resistant Leishmania were also highlighted in New World antimony-resistant L . amazonensis . These studies were useful to the identification of resistance molecular markers .
Leishmaniasis refers to a spectrum of parasitic diseases caused by protozoan parasites belonging to the genus Leishmania . The diseases are classified as neglected tropical diseases according to the World Health Organization ( WHO ) and constitute a public health problem in many developing countries of East Africa , the Indian subcontinent and Latin America . Human leishmaniasis has a prevalence of 12 million cases , with an estimated population of 350 million at risk and an incidence of 2 million new cases annually . Depending on Leishmania species , the host immune response , and environmental factors , leishmaniasis exhibits a broad spectrum of clinical manisfestations [1] . For example , in the New World , Leishmania ( Leishmania ) amazonensis , Leishmania ( Viannia ) guyanensis and Leishmania ( Viannia ) braziliensis are the causative agents of cutaneous and mucocutaneous leishmaniasis while Leishmania ( L . ) infantum chagasi is the aetiological agent of American visceral leishmaniasis [1] , [2] . Pentavalent antimonials ( SbV ) , such as sodium stibogluconate ( Pentostam® ) and meglumine antimoniate ( Glucantime® ) have been the first-line drugs in the treatment of all forms of leishmaniasis in South America , North Africa , Turkey , Bangladesh and Nepal . One major drawback of the SbV treatment is the emergence of resistance . For example , more than 60% of patients with visceral leishmaniasis in Bihar State in India are unresponsive to treatment with SbV antimonials [3] . The emergence of antimony resistance is related to inappropriate drug exposure resulting in a build-up of subtherapeutic blood levels and increasing tolerance of parasites to SbV [4] . Other drugs have been introduced as alternative chemotherapeutic agents including pentamidine , paromomycin , liposomal amphotericin B and miltefosine . However , either side effects , lower effectiveness or high cost have limited their use [5] . The mechanisms involved in antimony resistance in Leishmania are partially understood . Antimonial drugs are administered as SbV , a prodrug that is reduced to SbIII , the trivalent and biologically active Sb form [6] , [7] . However , the site of this reduction ( macrophages and/or parasites ) remains unclear . Two genes that encode proteins involved in Sb reduction have been described recently , the arsenate reductase LmACR2 and TDR1 thiol-dependent reductase [8] , [9] . Nevertheless , the role of these reductases in antimony resistance is not clear . Non enzymatic Sb reduction is also possible and probably mediated by the reducing agents glutathione ( GSH ) and trypanothione ( T ( SH ) 2 ) [5] , [10] , [11] . Once reduced in the macrophages , SbIII uptake is mediated by the aquaglyceroporin1 ( AQP1 ) [12] and downregulation of AQP1 gene expression is correlated to resistance [13] . Increases of T ( SH ) 2 levels have been observed in parasites selected for resistance to SbIII or arsenite [14] . This enhancement is usually related to the increased levels of rate-limiting enzymes involved in the synthesis of GSH ( gamma glutamylcysteine synthetase- γ-GCS ) and polyamines ( ornithine decarboxylase – ODC ) [15] , [16] . The use of specific inhibitors of γ-GCS or ODC can revert the resistance phenotype in mutants [16] . The ATP-binding cassette ( ABC ) protein MRPA has been classically related with drug resistance in Leishmania and plays a major role in metal resistance in these parasites [17] . MRPA is a member of the multidrug-resistance protein ( MRP ) family and its localization in intracellular vesicle membranes strongly suggests that it sequesters Sb-thiol complexes into these vesicles [18] . The MRPA gene has been found frequently amplified in laboratory-selected antimony- or arsenite-resistant Leishmania mutants as well as in field isolates [19] , [20] , [21] . Improved knowledge of the mechanisms involved in drug resistance using laboratory-selected mutants or field isolates are mostly derived from Old World Leishmania species such as L . tarentolae [22] , L . major [23] , [24] , L . tropica [25] , L . donovani [26] , and L . infantum [27] . On the other hand , the mechanism of drug resistance in New World Leishmania species remains poorly explored . Nevertheless , phenotypic and molecular characterizations of drug resistance have been recently published for human pathogenic neotropical Leishmania species [28] , [29] , [30] . Resistance to antimony in L . amazonensis has not been well studied as yet . Understanding the mechanisms responsible for drug resistance in Leishmania could support the design of new strategies for the successful treatment of leishmaniasis as well as the identification of molecular markers for resistance . Considering the multiplicity of mechanisms leading to antimony resistance , the simultaneous analysis of gene expression could provide useful information about the antimony-resistance mechanisms in Leishmania and help the identification of new pathways involved in resistance . Recent studies have demonstrated the usefulness of whole-genome DNA microarrays for studying drug resistance in Leishmania [31] , [32] . In this study , populations of L . amazonensis resistant to SbIII were selected in vitro in order to study global gene expression modulation associated with antimony resistance .
Leishmania amazonensis ( MHOM/BR/1989/Ba199 ) promastigotes were maintained in minimum essential culture medium ( α-MEM ) ( Gibco , Invitrogen , NY , USA ) , supplemented with 10% ( v/v ) heat-inactivated fetal calf serum ( Multicell , Wisent Inc . Québec , CA ) , 100 µg/ml kanamycin , 50 µg/ml ampicillin , 2 mM L-glutamine , 5 µg/ml hemin , 5 µM biopterin , ( Sigma-Aldrich , St Louis , USA ) , pH 7 . 0 and incubated at 25°C in B . O . D incubators ( Johns Scientific-VWR , Toronto , CA ) . The parasites were kindly provided by Dr . Aldina Barral , Gonçalo Muniz Research Center , Oswaldo Cruz Foundation , Brazil [33] . Populations of Leishmania amazonensis promastigotes were selected for SbIII resistance as previously described [19] . The four independent mutants of L . amazonensis Ba199SbIII2700 . 1 to Ba199SbIII2700 . 4 were individually selected in 25 cm2 flasks containing 5 ml of α-MEM medium in the presence of SbIII concentrations up to 2700 µM . L . amazonensis Ba199Sb mutants selected for SbIII resistance were grown in the absence of antimony pressure for 20 passages to test for the resistance stability phenotype [34] . The full genome arrays were described previously [31] , [32] , [35] . GeneDB version 3 . 0 of L . infantum genome and L . major genome version 5 . 2 were used for the probe selection . The microarray chip includes a total of 9173 Leishmania specific probes and control probes and made by Agilent Technologies ( Mississauga , ON , CA ) . These arrays have been used successfully with several species [31] , [35] . Total RNA was extracted from 108 promastigotes during the mid-log growth phase using RNeasy Plus mini kit ( Qiagen Sciences , Maryland , USA ) as described by the manufacturer . The quality ( based on the appearance of the spectra ) and quantity of RNA were assessed using RNA 6000 Nano Assay chips on Bioanalyzer 2100 ( Agilent Technologies Santa Clara , CA , USA ) . For each probe , 7 µg of RNA were converted to aminoallyl-dUTP incorporated cDNA using random hexamers ( Roche , Basel , Switzerland ) in presence of Superscript III RNase H reverse transcriptase ( Invitrogen , Carlsbad , CA , USA ) . Aminoallyl-dUTP incorporated cDNA were thereafter coupled to Alexa Fluor 555 or Alexa Fluor 647 ( Invitrogen , Carlsbad , CA , USA ) according to manufacturer recommendations . Fluorescent cDNA were then purified using the probe purification kit ArrayIt ( TeleChem International , Sunnyvale , CA , USA ) and quantified spectrophotometrically . The labeled and purified cDNA from L . amazonensis was mixed with 200 µg/ml sonicated salmon sperm DNA ( Agilent Technologies , Santa Clara , CA , USA ) ; 200 µg/ml yeast tRNA ( Sigma-Aldrich Ltd , ON , CA ) ; 1 x blocking agent buffer ( Agilent Technologies , Santa Clara , CA , USA ) and 1 x hybridization buffer ( Agilent Technologies , Santa Clara , CA , USA ) , then mixed , denaturated 3 min at 95°C and incubated 30 min at 37°C . Mixed labeled cDNAs were applied in the hybridization chamber ( Agilent Technologies , Santa Clara , CA USA ) and the hybridization was performed for 24 h at 65°C into a hybridization oven ( GeneChip® , Stovall Life Sciences , Greensboro , NC , USA ) . Slides were washed 5 min at room temperature in 0 , 5X SSC , 5% Triton-X102 with gentle agitation and subsequently washed 5 min in pre-warmed 0 , 1X SSC , 0 , 005% Triton-X102 at room temperature with occasional stirring . Detection of Alexa Fluor 555 and Alexa Fluor 647 signals were performed on a G2565CA microarray scanner ( Agilent Technologies , Santa Clara , CA , USA ) at 5 µm resolution as previously described [32] . The signal intensity data were extracted from the primary scanned images using GenePix Pro 6 . 0 software ( Axon Instruments , Union City , CA , USA ) . Five different cDNA preparations of each Ba199Sb mutant and their respective Ba199 wild-type were analyzed including dye-swaps . Normalization and statistical analyses were performed in R 2 . 2 . 1 software using the LIMMA ( Linear Models for Microarray Data ) 2 . 7 . 3 package [36] , [37] , [38] . Background correction was performed using the “edwards” method; within-array normalization was done by loess and between array normalization by the Aquantile method . Multiple testing corrections were done using the false discovery rate method with a threshold p value of 0 . 05 . Only genes statistically significant with an absolute ratio greater than 1 . 5 were considered . Custom R programs were used for the generation of the chromosome expression maps . Data are available with the GEO accession number GSE26159 . Three independent RNA preparations were used for each real-time RTPCR experiment . First-strand cDNA was synthesized from 2 . 5 µg of RNA using Oligo dT12–18 and SuperScript II RNase H-Reverse Transcriptase ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer protocol . Equal amounts of cDNA were run in triplicate and amplified in 20 µl reactions containing 1 x SYBR® Green Supermix ( Bio-Rad , Hercules , CA , USA ) , 100 nM forward and reverse primers and 1 µl cDNA target . Reactions were carried out using a rotator thermocycler Rotor Gene ( RG 3000 , Corbett Research , San Francisco , USA ) . Initially , mixtures were incubated at 95°C for 5 min and then cycled 30 times at 95 , 60 and 72°C for 15 sec . No-template controls were used as recommended . Three technical and biological replicates were established for each reaction . The relative amount of PCR products generated from each primer set was determined based on the threshold cycle ( Ct ) value and the amplification efficiencies . Gene expression levels were normalized to constitutively expressed mRNA encoding glyceraldehyde-3-phosphate dehydrogenase ( GAPDH , LmjF30 . 2970 ) . Primers for targeted genes MRPA ( LmjF23 . 0250 ) , NT3 ( LmjF13 . 1210 ) and LmjF26 . 2680 were designed using Primer QuestSM ( www . idtdna . com/Scitools/Applications/Primerquest ) . The sequences of the primers for MRPA are forward 5′-TGAGACACGCCGCATCAAGAGTAT-3′ and reverse 5′-TCAATGCTTCCTGCAGTACGAGGT-3′; for NT3 are forward 5′-AAGTTCATCTGGCCTCTCATGGCT-3′ and reverse 5′-GATGGTTGCAAACCACTTGTCCGT-3′; for LmjF26 . 2680 are forward 5′-ACCCAGTCATTCGTCATGCACTCT-3′ and reverse 5′- ATCTGGTTGACAGCGTCGCAAATG-3′ ; and for the GAPDH control forward 5′-GAAGTACACGGTGGAGGCTG-3′ and reverse 5′-CGCTGATCACGACCTTCTTC-3′ . Genomic DNA was isolated from L . amazonensis Ba199 WT and Ba199 antimony-resistant mutants using DNazol ( Invitrogen , Carlsbad , CA , USA ) following the manufacturer's instructions . Southern-blots and pulse field gel electrophoresis ( PFGE ) conditions were done following standard protocols [20] . Genomic DNAs were digested with PvuI and electrophoresed in 1% agarose gel . The fragments were transferred to HybondTM-N+ membrane ( Amersham Pharmacia Biotech , Sunnyvale , CA , USA ) and submitted to Southern-blot analysis . Chromosomes of L . amazonensis Ba199 WT and antimony-resistant mutants were separated by PFGE in which low melting agarose blocks , containing embedded cells ( 108 log phase cells/ml ) were electrophoresed in a contour clamped homogenous electric field apparatus ( CHEF Mapper , Bio-Rad , Hercules , CA , USA ) in 0 , 5 x Tris-Borate-EDTA , with buffer circulation at a constant temperature of 14°C and run time of 30 h . Saccharomyces cerevisiae chromosomes were used as size markers . DNA was transferred to nylon membranes , cross-linked to the membrane with UV light . The blots were hybridized with [α-32P]dCTP labeled DNA probes . The probes used in the present study included a 450 bp MRPA fragment and a α-tubulin probe used to control the DNA loading . Intracellular thiols were analyzed by derivatizing with mono-bromobimane and separating by high-performance liquid chromatography as described previously [14] , [39] using a chromatograph Shimadzu SCL 10A . Thiols were identified from bimane fluorescence with excitation and emission at 360 and 450 nm , respectively using a coupled fluorescence detector ( Shimadzu RF-10Axl ) . The IC50 values were calculated by linear regression using the software GraphPad Prism 5 . 0 and Sigma Plot 10 . 0 for windows . Differences in the level of intracellular thiols were analyzed by one-way ANOVA followed by Dunnett's multiple comparison test post-test using GraphPad Prism 5 . 0 . The level of significance acceptable was 95% ( p<0 . 05 ) .
Four independent mutants of L . amazonensis were selected step by step for antimony ( SbIII ) resistance . The IC50 value of the sensitive Ba199 strain was 83 µM , whereas the antimony resistant-mutants Ba199SbIII2700 . 1 , 2700 . 2 , 2700 . 3 and 2700 . 4 had IC50 values greater than 2700 µM ( Table 1 ) , the highest achievable soluble SbIII concentration in α-MEM medium at pH 7 . The stability of the resistance phenotype was tested by growing the cells in the absence of SbIII . After 20 passages without drug pressure , only the resistance in mutant Ba199SbIII2700 . 2 was found to be stable , while the other three mutants showed decreased resistance levels ( Table 1 ) . However , reversion was only partial since the Ba199SbIII2700 . 1 , 2700 . 3 and 2700 . 4 mutants were not as sensitive as wild-type cells to SbIII ( Table 1 ) . The susceptibility to miltefosine in the SbIII-resistant L . amazonensis mutants was also tested . None were cross-resistant but 3 out of the 4 lines were surprisingly hypersensitive to it ( Table 1 ) . Intriguingly , we have also observed hypersensitivity to miltefosine in L . infantum SbIII-resistant mutants ( W . Moreira and M . Ouellette , unpublished observations ) . The Ba199SbIII2700 . 2 and Ba199SbIII2700 . 3 lines were selected for gene expression studies using full genome DNA microarrays . We plotted the log2-transformed gene expression ratios of Ba199SbIII2700 . 2 ( red line ) and Ba199SbIII2700 . 3 ( blue line ) compared to Ba199WT parental strain , as a function of the microarray probes ( Fig . 1 ) . Most genes were equally expressed but about 10% of genes showed a statistical significant variation ( summarized in Table S1 and detailed in Tables S2 and S3 ) with approximately 2-fold differential expression but some reached log2-transformed ratio values up to 4 and −4 ( Fig . 1 ) . The differential hybridization data were also represented on a chromosome by chromosome basis ( Figs . 2 and 3 ) . Upregulated and downregulated genes are indicated by red and green lines , respectively , while equally expressed genes were shown as gray regions . Some obvious changes in gene expression were noticed . A specific region at one telomeric end of chromosome 23 was upregulated in Ba199SbIII2700 . 2 ( Fig . 2 ) , while most genes of chromosome 23 seemed upregulated in Ba199SbIII2700 . 3 ( Fig . 3 ) . Chromosome aneuploidy has been described previously in Old World drug resistant Leishmania [31] , [32] and the chromosome maps of Figs . 2 and 3 suggest that this phenomenon also takes place in New World Leishmania species with chromosomes 1 , 10 , 16 , 27 and 31 becoming polyploids in Ba199SbIII2700 . 2 ( Fig . 2 ) while in addition to chromosome 23 , chromosomes 5 , 27 and 32 are polyploids and chromosome 4 is haploid in Ba199SbIII2700 . 3 ( Fig . 3 ) . A region of chromosome 35 , 250 kb from one telomeric end , corresponds to loci where the expression of genes was down regulated in both Ba199SbIII2700 . 2 and Ba199SbIII2700 . 3 mutants ( Figs . 2 and 3 ) . The expression of genes part of a region on chromosome 33 , 1 . 5 Mb from one telomere end was also down regulated in both mutants ( Figs . 2 and 3 ) . The array results led to several candidate genes putatively correlated to resistance . Candidate genes could either be highly differentially regulated or part of large regions differentially regulated , as highlighted in Figs . 2 and 3 . The genes common to both mutants most differentially down regulated included the hypothetical protein gene LmjF26 . 2680 and a putative lmgt2 glucose transporter gene LmjF36 . 6290 ( Fig . 1 and Supplementary Tables S2 and S3 ) . On the other hand , the gene common to both mutants most upregulated was corresponding to the nucleobase transporter NT3 LmjF13 . 1210 . The overexpression of NT3 was confirmed by qRT-RTPCR which yielded similar results as found with microarrays with higher expression of NT3 in Ba199SbIII2700 . 2 compared to 2700 . 3 ( Fig . 1 , Fig . 4 ) . We also tested the two other L . amazonensis mutants available and found that NT3 was also overexpressed in Ba199SbIII2700 . 1 and 2700 . 4 ( Fig . 4 ) . Similarly , we confirmed the down regulation of LmjF26 . 2680 by qRT-RTPCR not only in Ba199SbIII2700 . 2 and 2700 . 3 but also in two other L . amazonensis resistant mutants ( Fig . 4 ) . None of the genes described above were previously linked to antimony resistance in Leishmania . For specific larger regions that were presumed to be up or down regulated as determined from the chromosome maps of Figs . 2 and 3 , we found that the region of chromosome 23 upregulated in Ba199SbIII2700 . 2 ( Fig . 2 ) contained several genes ( Table S2 ) including the ABC protein gene MRPA LmjF23 . 0250 , a well established marker of antimony resistance [40] , [41] . The MRPA gene was also upregulated in Ba199SbIII2700 . 3 ( Fig . 3 ) as determined by microarrays ( Table S3 ) . As discussed above , two regions of chromosome 35 and 33 appeared to be down regulated in both mutants . The region of chromosome 35 encodes for several hypothetical proteins , but also three proteophosphoglycan ( PPG ) genes PPG1 , PPG3 and PPG5 ( Tables S2 and S3 ) . Similarly , the region of chromosome 33 corresponds mostly to hypothetical proteins ( Tables S2 and S3 ) . With the exception of MRPA , none of the genes highlighted in this study were previously linked to antimony resistance . We searched for genes that were previously linked to resistance with significant changes in gene expression and found several genes that were upregulated in the Ba199SbIII mutants and that were involved in redox and thiol metabolism such as peroxidoxin ( LmjF23 . 0040 ) , glutaredoxin ( LmjF05 . 0310 ) , trypanothione synthetase ( LmjF23 . 0460; LmjF27 . 1870 ) , trypanothione reductase ( LmjF05 . 0350 ) , and spermidine synthase ( LmjF04 . 0580 ) ( Tables S2 and S3 ) . The overexpression of several trypanothione biosynthetic genes ( e . g . spermidine synthase , trypanothione synthetase ) prompted us to quantify the level of intracellular reduced thiols , since resistance to SbIII is often correlated to increased glutathione and trypanothione levels in Old World Leishmania [42] . The antimony-resistant L . amazonensis mutants , with the exception of Ba199SbIII2700 . 1 ( for glutathione ) , had significant higher levels of cysteine , glutathione and trypanothione ( Fig . 5 ) . We also tested the role of genes previously not associated with resistance , concentrating on some of the genes most differentially expressed . These genes correspond to the hypothetical gene LmjF26 . 2680 which was down regulated by more than 20-fold in all mutants ( Fig . 4 ) and NT3 that was overexpressed in all mutants as determined by real-time RT-PCR ( Fig . 4 ) . Transfection of LmjF26 . 2680 in wild-type L amazonensis or in its resistant mutants did not change their susceptibilities to SbIII ( results not shown ) . Similarly , transformation and overexpression of NT3 , did not lead to higher resistance to SbIII in wild type cells ( result not shown ) . The MRPA gene was overexpressed in both mutants ( Table S2 and S3 ) and this upregulation was indeed confirmed by qRT-RTPCR in Ba199SbIII2700 . 2 and 2700 . 3 but MRPA was also found overexpressed in Ba199SbIII2700 . 1 and 2700 . 4 ( Fig . 4 ) . The fold increased expression by qRT-RTPCR was higher than what microarray would have suggested . Often , but not always , gene overexpression is correlated to gene amplification in Leishmania [13] , [40] , [41] . Southern blot analysis and careful densitometric quantification has indeed indicated that MRPA gene copy number is increased in the mutants compared to wild-type cells ( Fig . 6A ) . Increased gene copy number is usually due to the formation of extrachromosomal circular or linear elements [43] , [44] although changes in copy number of whole chromosomes have also been reported [31] , [32] . Search for extrachromosomal circles failed by standard alkaline lysis extractions and we thus relied on CHEF gels to separate the Leishmania chromosomes and investigated for the presence of short linear amplicons . Hybridization to a MRPA probe showed the presence of linear amplicons in Ba199SbIII2700 . 1 and 2700 . 2 while the whole chromosome 23 was increased in copy number in Ba199SbIII2700 . 3 and 2700 . 4 ( Fig . 6B ) . These results are consistent with the microarray data ( Figs . 2 and 3 ) .
Resistance to antimony in Leishmania has been studied mostly in Old World species and mostly in strains in which resistance was induced under laboratory conditions ( reviewed in [5] , [42] ) . However , with a better understanding of in vitro resistance mechanisms , more work has recently been done with clinical isolates and some of the markers highlighted in in vitro studies were shown to correlate with drug resistance in clinical isolates [20] , [45] . In general , there is a reasonable agreement between in vitro susceptibility testing and clinical response with Old World Leishmania when assays are carried out with intracellular parasites [45] , [46] , [47] . However , there are conflicting results in linking in vitro susceptibility testing and clinical responses with New World leishmaniasis [48] , [49] . There have been few studies on mechanisms of resistance to antimony in New World parasites and we have thus used here the proven approach of in vitro selected resistant cells . Four independent L . amazonensis clones were selected for resistance to SbIII . Resistance was in general unstable when cells were grown in absence of the drug ( Table 1 ) , a result also recently observed with New World Leishmania selected for antimony resistance [28] . To find possible markers of resistance in these L . amazonensis strains , we carried out RNA expression profiling on full genomic DNA microarrays , a technique proven useful to study resistance mechanisms in Leishmania [31] , [32] , [50] . We found several gene candidates ( Table S2 and S3 ) , some for which the expression was highly modulated in comparison to sensitive isolates . Two of these genes ( the hypothetical LmjF26 . 2680 and NT3 ) were new and were experimentally tested by gene transfection . However , we could not directly link them to resistance . NT3 and LmjF26 . 2680 were respectively overexpressed and down-regulated in four independent mutants ( Fig . 4 ) , and this recurrence would argue for some role in resistance . If it is not directly involved in resistance as the transfection work would suggest , it could either require another product to confer resistance or it may have another more indirect role such as in increased fitness or compensating for other mutations . We noticed that one glucose transporter in Ba199SbIII2700 . 3 was down regulated ( Fig . 1 ) . Decrease glucose uptake , for example by minimizing reactive oxygen species , was suggested as a general mechanism associated with drug resistance in L . amazonensis [51] . Future work will be required to test this . It is also worthnoting that while the expression of NT3 is increased , this is not due to gene amplification . Indeed , the NT3 copy number remains similar to wild-type ( result not shown ) . While changes in expression in resistant isolates are often due to changes in gene copy number , there has been several other reports of increased expression by other means which will likely involve post-transcriptional regulation mechanisms . Indeed , the expression of genes in Leishmania is not controlled at the level of transcription initiation [52] , [53] . The microarray work allowed detecting alterations of expression of large regions of genomic DNA and even of whole chromosomes ( Figs . 2 and 3 ) . In Leishmania these alterations are usually linked to changes in copy number [31] , [32] . One region that attracted our attention was part of chromosome 23 . Mutant Ba199SbIII2700 . 2 had a specific region that was overexpressed while the whole chromosome 23 seemed overexpressed in Ba199SbIII2700 . 3 . The gene MRPA , one marker highly correlated to SbIII resistance in Old World Leishmania , is encoded by chromosome 23 . We tested whether this increased expression was due to changes in copy number and Southern blot analysis indeed confirmed that MRPA is amplified ( Fig . 6A ) . New World Leishmania is divided in two subgenus: Leishmania and Viannia . Gene amplification is rare in the Viannia subgenus [30] and this may be due to an active RNA interference ( RNAi ) mechanism in this subgenus but absent in the Leishmania subgenus [54] . It is thus surprising that there is one report of a circular extrachromosomal amplification of MRPA in L . V . guyanensis selected for antimony resistance [55] . There is , however , ample report of gene amplification in the New World Leishmania subgenus whether it is L . amazonensis [56] , [57] or L . mexicana [58] . No MRPA amplification has been observed in one L . amazonensis strain selected for SbIII resistance [59] but a circular amplification was observed in L . mexicana selected for resistance to the related metal arsenite [58] . The MRPA containing amplicon in L . mexicana or L . V . guyanensis corresponded to an extrachromosomal circle . In Ba199SbIII2700 . 2 the amplification was a linear amplicon and extended from the telomeric region to gene LmjF23 . 0540 ( a region of ∼230 kb ) . All linear amplifications so far described , indeed extended to the telomeric region and are usually forming large inverted duplications [31] . This duplication of the region amplified fits with the size of this linear amplicon ( Fig . 6B ) . Interestingly , we also found an MRPA containing linear amplicon in Ba199SbIII2700 . 1 ( Fig . 6B ) . The amplicon is smaller , suggesting that a different rearrangement point , usually at the level of inverted repeats [31] , [60] has been used . The microarray data indicate that the whole chromosome 23 was increased in Ba199SbIII2700 . 3 and this was corroborated by Southern blot analysis ( Fig . 6 ) . Indeed , the CHEF showed clearly that chromosome 23 had a higher hybridization intensity compared to Ba199SbII2700 . 2 ( Fig . 6B ) . Interestingly , polyploidy of chromosome 23 was also observed in Ba199SbII2700 . 4 . Intriguingly , this relatively modest increase in copy number was nonetheless correlated to a high MRPA expression at the RNA level ( Fig . 4 ) . Thus an increase in MRPA expression in L . amazonensis is correlated to either the formation of extrachromosomal linear amplicons or the increased ploidy of the chromosome . This study has shown that mechanisms of resistance to antimony found in Old World Leishmania can also be detected in New World species . This includes higher thiol levels ( Fig . 5 ) and increased expression of the ABC MRPA , whose gene product sequesters thiol-metal conjugates into an intracellular organelle [18] . Overexpression of several genes was found to correlate with increased thiols [15] , [16] , [50] and overexpression of spermidine synthase ( leading to polyamines , one constituent of trypanothione ) and trypanothione synthase ( supplementary Tables S2 and S3 ) could contribute to the observed increased thiols . Also we noticed that trypanothione reductase was overexpressed and this would maintain thiols into a reduced form and this gene was found overexpressed in field isolates [61] . Many other genes were found to be differentially regulated although analysis of two candidates did not allow finding a role in resistance . Nonetheless with all microarray experiments done with several different species it should now be possible to perform meta-analysis which could direct at further candidates for a better understanding of antimony resistance mechanisms in the protozoan parasite Leishmania . The study presented here should serve as a useful basis for analyzing antimony resistance in clinical isolates of new world leishmaniasis . Indeed , in vitro work mostly with the promastigote stage of old world leishmaniasis has led to a number of drug resistant markers [5] , [42] . These markers were shown to confer resistance in the amastigote or intracellular stage of the parasite [40] and even more importantly in L . donovani field isolates [20] , [61] , [62] , [63] . Since several markers were highlighted here with in vitro resistance in L . amazonensis , it would now be possible to test whether similar resistance mechanisms take place with drug resistant clinical isolates of New World leishmaniasis . | Leishmania are unicellular microorganisms that can be transmitted to humans by the bite of sandflies . They cause a spectrum of diseases called leishmaniasis , which are classified as neglected tropical diseases by the World Health Organization . The treatment of leishmaniasis is based on the administration of antimony-containing drugs . These drugs have been used since 1947 and still constitute the mainstay for leishmaniasis treatment in several countries . One of the problems with these compounds is the emergence of resistance . Our work seeks to understand how these parasites become resistant to the drug . We studied antimony-resistant Leishmania amazonensis mutants . We analyzed gene expression at the whole genome level in antimony-resistant parasites and identified mechanisms used by Leishmania for resistance . This work could help us in developing new strategies for treatment in endemic countries where people are unresponsive to antimony-based chemotherapy . The identification of common mechanisms among different species of resistant parasites may also contribute to the development of diagnostic kits to identify and monitor the spread of resistance . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"genome",
"expression",
"analysis",
"infectious",
"diseases",
"leishmaniasis",
"biology",
"genomics",
"parasitic",
"diseases",
"genetics",
"and",
"genomics"
] | 2011 | Gene Expression Profiling and Molecular Characterization of Antimony Resistance in Leishmania amazonensis |
Snakebite results in delayed psychological morbidity and negative psycho-social impact . However , psychological support is rarely provided to victims . To assess the effectiveness of a brief intervention which can be provided by non-specialist doctors aimed at reducing psychological morbidity following snakebite envenoming . In a single blind , randomized controlled trial , snakebite victims with systemic envenoming [n = 225 , 168 males , mean age 42 . 1 ( SD 12 . 4 ) years] were randomized into three arms . One arm received no intervention ( n = 68 , Group A ) , the second received psychological first aid and psychoeducation ( dispelling prevalent cultural beliefs related to snakebite which promote development of a sick role ) at discharge from hospital ( n = 65 , Group B ) , while the third received psychological first aid and psychoeducation at discharge and a second intervention one month later based on cognitive behavioural principles ( n = 69 , Group C ) . All patients were assessed six months after hospital discharge for the presence of psychological symptoms and level of functioning using standardized tools . At six months , there was a decreasing trend in the proportion of patients who were positive for psychiatric symptoms of depression and anxiety from Group A through Group B to Group C ( Chi square test for trend = 7 . 901 , p = 0 . 005 ) . This was mainly due to a decreasing trend for symptoms of anxiety ( chi-square for trend = 11 . 256 , p = 0 . 001 ) . There was also decreasing trend in the overall prevalence of disability from Group A through Group B to Group C ( chi square for trend = 7 . 551 , p = 0 . 006 ) , predominantly in relation to disability in family life ( p = 0 . 006 ) and social life ( p = 0 . 005 ) . However , there was no difference in the proportion of patients diagnosed with depression between the three groups ( chi square for trend = 0 . 391 , p = 0 . 532 ) , and the intervention also had no effect on post-traumatic stress disorder . A brief psychological intervention , which included psychological first aid and psychoeducation plus cognitive behavioural therapy that can be provided by non-specialist doctors appeared to reduce psychiatric symptoms and disability after snakebite envenoming , but not depression or post-traumatic stress disorder . Sri Lanka Clinical Trials Registry: SLCTR/2011/003
Snakebite causes significant morbidity and mortality and the highest burden exists in the poorer rural populations of South Asia , Southeast Asia , and sub-Saharan Africa . Globally , it has been estimated that snakebite results in as many as 1 . 8 million envenomings and 94 000 deaths each year [1] . In Sri Lanka , about 35 , 000 to 40 , 000 persons are treated in hospital for snakebite each year [2] . The actual number of bites is likely to exceed this number , as some victims seek traditional forms of treatment and do not come into contact with mainstream health services . The affected are often poor farmers or manual workers in whom the snakebite may result in loss of livelihood and good health [3] . Though the physical impacts of snakebite are well documented and researched , the long term psychological impact of snakebite remained largely unexplored . We recently reported that significant delayed psychological morbidity occurs in victims of snakebite , including increased rates of depression , anxiety and post-traumatic stress disorder ( PTSD ) , with associated negative psychosocial impact [4] . This study showed that snakebite victims had more depressive symptoms than controls based on the modified Beck Depression Scale and more symptoms of depression and anxiety measured by the Hopkins Symptoms Checklist . Fifty four per cent of the cohort met criteria for depressive disorder compared to only 15% of the controls . PTSD occurred in 22% of patients and 27% claimed that the snakebite caused a negative change in their employment with 10% stopping work and 17% reporting residual physical disability . This is hardly surprising , as the affected are often poor farmers or manual workers in whom the snakebite may result in loss of livelihood [4] . There are many myths surrounding snakebite . In many Asian and African cultures snakes are considered deities and hence snakebite can be misconstrued as punishment from the gods . These myths are not merely confined to Asia and Africa as is evident by the snake receiving prominence in most western health emblems due to its perceived mythical prowess and impact on health [5] . To this day in Sri Lanka , a ritualistic dance called the “sanninatuma” is performed to exorcise the demons causing eighteen common illnesses , and one of these dances specifically targets “Naga Sanniya” which literally translates to “snake madness” and is characterized by nightmares involving snakes and inability to perform activities of daily living [6] . “Naga sanniya” may be the first recorded description of psychological disability following snakebite . The physical impact of snakebite can be coloured by cultural beliefs , and it is not uncommon for people to believe that snakebite will lead to sapping of strength , diminished physical abilities , blindness , physical disfiguration and an overall inability to function at their premorbid level leading to avoiding work , withdrawing from social life and resigning themselves to a life of suffering [4] . Improved emergency care , availability of antivenom , and increasing numbers of victims seeking hospital based care has led to a reduction in mortality rates and physical complications of snake bite [7] . However , until recently , delayed psychological morbidity following snakebite was not recognized and psychological support is rarely offered to victims , although its inclusion into snakebite management protocols has been recommended [7 , 8] . Psychoeducation , which refers to the education offered to individuals with a mental health condition and their families to help empower them and deal with their condition in an optimal way , has been shown to be of benefit in the treatment of many mental conditions [9] . Although there are no precedents for the use of psychoeducation following animal bites , there is evidence for the usefulness of psychoeducation following traumatic situations [10] . Trauma based cognitive behavioural therapy is also recommended in the treatment of mental illness following stressful life events [11] . There is evidence to suggest that brief interventions based on trauma focused cognitive behavioural therapy are effective in the prevention of PTSD [12] . The aim of this study was to assess the effectiveness of a brief psychological intervention provided by non-specialist doctors in reducing delayed psychological morbidity and negative social impact associated with snakebite envenoming .
Ethical approval for the study was obtained from the Ethics Review Committee of the Faculty of Medicine , University of Kelaniya , Ragama . The study was registered as a clinical trial with the Sri Lanka Clinical Trials Registry ( SLCTR/2011/003 ) . All subjects provided written informed consent . The study was conducted in the Polonnaruwa District General Hospital in Sri Lanka from August 2011 to April 2014 . Polonnaruwa is situated in the northeast of the country and has a predominantly rural agricultural population . The area has one of the highest rates of snakebite envenomings in the country [13] . All snakebite victims admitted to hospital identified as being envenomed and requiring treatment with antivenom were eligible for inclusion . Exclusion criteria were those under 18 years of age , those with known mental illness , and those without basic fluency in the Sinhala language . After snakebite patients had received standard medical treatment for their snake envenoming , they were randomized to one of three study arms before discharge from hospital , after obtaining written informed consent . Group A received no psychological intervention , Group B received a psychological intervention based on psychological first aid and psychoeducation at time of discharge from hospital , and Group C received psychological first aid and psychoeducation at time of discharge and were subsequently recalled one month following discharge from hospital and provided with a psychological intervention based on trauma based cognitive behavioural therapy principles . All participants were assessed six months following discharge from hospital . A sample size of 195 ( 65 in each arm of the study ) was calculated in order to detect a 50% reduction in rates of depressive disorder in the intervention group assuming an incidence of 54% as detected in our previous study [4] , a power of 80% and a significance level of 0 . 05 . A 10% loss to follow up rate was assumed , resulting in an increase in the sample size to 216 . We selected depression for sample size calculation as it was an important disability that was identified in our previous study [4] . The brief interventions were administered by non-specialist doctors . The non-specialist doctors involved in the study were trained by a specialist psychiatrist , initially over a period of one week with continued support over the course of the study . They were trained on communication skills and counseling via practical demonstration . Patients were assessed for presence of psychological morbidity and functional status six months following discharge from hospital by a specialist psychiatrist blind to intervention status and trained in the usage of the study tools . Psychological distress was quantified using a number of measures: the Hopkins symptoms checklist– 25 ( HSCL-25 ) [15 , 16] , a modified Sinhala version of the Beck depression inventory [17] , the Sheehan Disability Inventory [18] , and the Post-traumatic Stress Symptom Scale—Self Report ( PSS-SR ) [19] . All have been previously validated and used in Sri Lanka [20] . The Hopkins Psychiatric Symptom Checklist measures a combination of depressive and anxiety symptoms . It does not provide a diagnosis of illness but based on the score , classifies subjects as positive or negative for psychiatric symptoms . The Beck's modified depression scale scores were categorized into no depression ( 0–15 ) , mild depression ( 16–24 ) , moderate depression ( 25–32 ) and severe depression ( >32 ) in terms of accepted figures . The established clinically significant item-average cut-off score of ≥1 . 75 for each sub-scale was used for the Hopkins somatic symptoms checklist . An overall cut-off of 15/30 and a domain specific cut-off of 5/10 were used for the Sheehan Disability Inventory . The generally accepted cut off score ≥20 on the PSS-SR was taken as compatible with post-traumatic stress disorder . The outcomes assessed were the proportions of patients with psychiatric symptoms overall , positive symptoms of depression , positive symptoms of anxiety ( based on the Hopkins Somatic Symptoms Checklist ) , depression ( based on Beck’s Modified Depression Scale ) , disability in family life , social life and work ( based on Sheehan’s Disability Inventory ) and PTSD . Analysis of quantitative data was done using SPSS version 16 on an intention to treat basis . Chi square test for trend and Fisher’s exact test were used to assess the differences between the study groups . After adjustment for multiple testing , a p<0 . 0125 was considered significant .
There were 225 snakebite victims [168 males , mean age 42 . 1 ( SD 12 . 4 ) years] who were randomized into one of the three study arms ( n = 75 each ) . Of these , 202 ( 89% ) ( Group A , n = 68; Group B , n = 65; Group C , n = 69 ) completed the study and were assessed at 6 months after discharge from hospital ( Fig 1 ) . Male farmers of working age were highly represented in the study sample . There were no differences in age , sex or occupation between the three groups ( Table 1 ) . Overall , the biting species was identified in 24 . 1%; the proportion of species identified was similar in the three groups . The proportion of patients who developed severe reactions to AVS [21] and who were treated in intensive care were similar in the three groups . Four patients developed tissue necrosis , but none required amputation . At six months , the overall proportion of patients who were positive for psychiatric symptoms of depression and anxiety was 18/68 ( 26 . 5% ) in Group A , compared to 9/65 ( 13 . 8% ) in Group B and 6/69 ( 8 . 7% ) in Group C . This decreasing trend was statistically significant ( Chi square test for trend = 7 . 901 , p = 0 . 005 ) . On further analysis , this decreasing trend was seen for both symptoms of anxiety ( Chi square test for trend = 11 . 256 , p = 0 . 001 ) , and symptoms of depression ( Chi square test for trend = 5 . 793 , p = 0 . 016 ) ( Table 2 ) . Depression was diagnosed in 21/68 ( 30 . 9% ) patients in Group A , 17/65 ( 26 . 2% ) patients in Group B and 18/69 ( 26 . 1% ) patients in Group C . These rates did not show a statistically significant trend ( chi square for trend = 0 . 391 , p = 0 . 532 ) ( Table 3 ) . However , on further analysis , the rate of severe depression was significantly higher in Group A ( 10 . 3% ) compared to Group B ( 1 . 5% ) and Group C ( 0 ) ( Fisher’s exact test p = 0 . 004 ) ( Table 4 ) . The overall prevalence of disability was 18/68 ( 26 . 5% ) in Group A compared to 11/65 ( 16 . 9% ) in Group B and 6/69 ( 8 . 7% ) in Group C with a statistically significant decreasing trend from Group A through Group B to Group C ( chi square for trend = 7 . 551 , p = 0 . 006; Table 5 ) . On further analysis , this decreasing trend was seen in relation to disability in family life ( p = 0 . 006 ) and social life ( p = 0 . 005 ) , but not in relation to disability at work ( p = 0 . 056 ) . The overall prevalence of PTSD was ( 17/202 ) 8 . 4% . The proportion of patients with PTSD was 7/68 ( 10 . 3% ) in group A , compared to 8/65 ( 12 . 3% ) in group B and 2/69 ( 2 . 9% ) in Group C which was not statistically significant ( Chi-square for trend = 2 . 448; p = 0 . 118 ) ( Table 6 ) . A sensitivity analysis was conducted assuming the worst possible scenario ( worst clinical outcome ) in the patients who dropped out after randomization without receiving the intervention and outcome assessment . The decreasing trend observed from Group A through Group B to Group C in the results of Hopkins Psychiatric Symptoms Checklist and Sheehan Disability Inventory remained statistically significant .
We found that brief psychological interventions by non-specialist doctors , which included psychological first aid and psycho-education at discharge and a single cognitive behavioural therapy based intervention one month after discharge , appeared to reduce psychiatric symptoms of anxiety and depression , and improve overall functionality , especially those related to family and social life in victims of snake bite envenoming . The interventions did not reduce the proportion of patients with PTSD or overall depression , but appeared to reduce severe depression . The apparent discrepancy in the fact that overall depression was not reduced though there was a reduction in severe depression in the intervention groups , may reflect the fact that the interventions are simply effective in converting severe depression to milder forms . Our findings have important implications given the potential the socio-economic burden that may result from psychological disability following snakebite envenoming in this predominantly subsistence farming population . The proportion of patients who developed severe reactions to AVS , who developed tissue necrosis and who were treated in intensive care were similar in the three groups . These factors are , therefore , unlikely to have influenced the differences in outcome between the three groups . Psychological interventions following trauma aimed at preventing psychiatric illness have generally shown mixed results [14] . Trauma based cognitive behavioural therapy is recommended in the treatment of psychological disorders following stressful life events [11] because there is evidence to suggest that brief interventions based on trauma focused cognitive behavioural therapy is effective in the prevention of PTSD [12] . Recent studies also support single interventions such as ours as being useful [22] . A study on psychological interventions following trauma from Chile has shown reduction in rates of depression and improvement of functional levels , although PTSD rates did not improve [23] . Cognitive errors are known to dominate the thoughts of a person afflicted with secondary depression [24] . Therefore , they may be more easily targeted by providing psychological first aid and psychoeducation and cognitive behavioural therapy . However PTSD constitutes a constellation of symptoms less under cognitive control . This may not respond to brief psychological interventions . Rates of PTSD were not reduced by the interventions conducted in our study and this reflects the findings of studies conducted elsewhere [12 , 25] , and underscores the view that one intervention may work better for a particular symptom but not for another . The challenge would be to design a brief intervention that is able to reduce all types of symptoms to some extent , although it would possibly not be the ideal intervention for each individual symptom . Although there was a trend towards reduction of psychiatric symptoms as well as improved functionality in both intervention groups , the best outcome was seen in the group which received both psychological first aid and psychoeducation and the cognitive behavioral therapy based intervention . Though the interventions were designed on existing models and delivered in a structured manner , the factors that played a role in reducing psychological morbidity remain to be determined . Common therapeutic factors such as confidence in the therapist , explaining illness factors and interaction with the therapist have a positive effect regardless of type of psychotherapy provided [25] . We are unable to ascertain if it was the specific therapy provided or the interaction and counseling provided by a health care professional that caused the beneficial effect seen in our study . Further analysis of the interventions will be required to improve them further . Nevertheless , the fact that psychological interventions provided by non-specialist doctors seemed to help in reducing some psychological morbidity in snakebite victims is encouraging and warrants further exploration . Psychological therapies are provided by health care professionals other than psychiatrists and psychologists . These include general practitioners , counsellors , therapists , social workers and psychiatric nurses . The lack of qualified psychotherapists is a worldwide problem [26] . Snakebites occur predominantly in poorer social settings in Asia and Africa , and so it is unlikely that specialist mental health services will be optimal in these settings . Training non-specialist doctors to provide brief psychological interventions may be a viable alternative option . Having a non-specialist doctor providing the psychological intervention as opposed to a psychiatrist or psychologist may even improve compliance , given the stigma associated with mental illness which remains a significant barrier towards accessing mental health care [27] . Our study has limitations . As ours was an intention to treat analysis , the dropout rate after randomization may have affected the results . However , there was no change in the overall results after a sensitivity analysis was conducted assuming the worst possible clinical outcome in the patients who dropped out after randomization without receiving the intervention and outcome assessment . Also , as more than one outcome of the intervention was assessed , multiple comparisons had to be made . However adjustments were made for multiple comparisons when calculating significance . Psychiatric caseness was not confirmed by a psychiatrist or by using detailed protocols , as we used only screening instruments for depression and anxiety . However , all of the instruments we used have been previously validated and used in Sri Lanka [20] . We did not have any information on pre-event rates of depression or anxiety in the study group which may have affected our results . In an attempt to minimize such an effect we excluded patients with known mental illness from the study . It is commonly believed that some snakes induce more fear and could be psychologically more traumatic than bites of other snakes . Although of potential interest , we were unable to perform a subgroup analysis of our results based on biting species as the offending snake was identified in a minority of cases . This is the usual situation in rural Sri Lanka where the offending snake is brought to hospital very infrequently , venom antigen detection is not routinely available , and bites often occur in situations where the snake is not even seen clearly—eg . in scrub jungle , paddy fields . In conclusion , this study is the first attempt to treat the recently recognized problem of psychological morbidity following snakebite envenoming . The results clearly suggest a role for brief psychological interventions post-snake bite , provided by non-specialist doctors that should be achievable even in settings in which mental health services are sub-optimal . However , further research is required to refine the types of interventions , focusing on PTSD and depression . | Snakebite is an important health problem in many rural communities in tropical countries . However , little is known about the lasting physical and mental health effects following a bite . We recently reported that mental problems , with harmful social outcomes , can occur in many people after they are bitten by a snake . As the affected are often poor farmers or manual workers , this may affect their livelihoods . We , therefore , performed a trial which looked at the effectiveness of short psychological interventions , lasting about 15 and 20 minutes , which can be provided by even non-specialist doctors , in reducing these mental and social problems in people bitten by snakes . Our results show that such interventions may indeed be helpful to reduce some of these problems , but more research is needed to improve these interventions , especially so that they that can reduce post-traumatic stress disorder and depression after snakebite . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | A Randomized Controlled Trial of a Brief Intervention for Delayed Psychological Effects in Snakebite Victims |
The development and homeostasis of multicellular organisms relies on gene regulation within individual constituent cells . Gene regulatory circuits that increase the robustness of gene expression frequently incorporate microRNAs as post-transcriptional regulators . Computational approaches , synthetic gene circuits and observations in model organisms predict that the co-regulation of microRNAs and their target mRNAs can reduce cell-to-cell variability in the expression of target genes . However , whether microRNAs directly regulate variability of endogenous gene expression remains to be tested in mammalian cells . Here we use quantitative flow cytometry to show that microRNAs impact on cell-to-cell variability of protein expression in developing mouse thymocytes . We find two distinct mechanisms that control variation in the activation-induced expression of the microRNA target CD69 . First , the expression of miR-17 and miR-20a , two members of the miR-17-92 cluster , is co-regulated with the target mRNA Cd69 to form an activation-induced incoherent feed-forward loop . Another microRNA , miR-181a , acts at least in part upstream of the target mRNA Cd69 to modulate cellular responses to activation . The ability of microRNAs to render gene expression more uniform across mammalian cell populations may be important for normal development and for disease .
The complexity of developmental processes in metazoans relies on mechanisms that confer a degree of robustness against environmental and genetic variation [1] . microRNAs are small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level by reducing mRNA stability and/or translation . Their role in dampening gene expression makes microRNAs potential building blocks for gene regulatory circuits that can stabilize gene regulatory networks [2–5] . Gene expression is subject to intrinsic stochasticity associated with mRNA transcription and translation , as well as extrinsic noise such as fluctuations in upstream regulators . Gene expression noise is not restricted to protein coding genes: the expression of primary microRNA transcripts , their processing into pre-microRNAs , nuclear export , processing into mature microRNAs , association with RISC components , etc . , presumably all have stochastic components . The participation of microRNAs in the regulation of protein-coding genes could therefore add noise contained in both microRNA and protein-coding systems . Feed-forward loops ( FFLs ) are recurrent network motifs that can reduce gene expression noise by buffering fluctuations in upstream regulators [6] . Placing the expression of a microRNA and its target mRNA under the control of common upstream regulators can link the production of mRNAs to the production of microRNAs that target the mRNAs . Theoretical considerations [2] and computational simulations [7 , 8] suggest that this circuit topology , which resembles an incoherent FFL , allows microRNAs to buffer protein expression against fluctuations in the activity of upstream regulators [9] . In silico models predict that FFL regulation enables microRNAs to reduce not only the level of target gene expression , but also cell-to-cell variability [7 , 8] . Data from synthetic circuits indicate that co-expression of microRNAs and target mRNAs can reduce temporal fluctuations and in some cases cell-to-cell variability in reporter gene expression [7 , 10] . Emerging experimental evidence supports a role for microRNAs in biological robustness [2] . microRNAs affect several phenotypic traits in Drosophila , for example by stabilizing the regulation of the enhancer of split transcription factor to guide sensory organ development under conditions of environmental flux [11] . Loss of microRNAs can increase the variation of primordial germ cell numbers [12 , 13] and sensory bristles [14] , and quantitative phenotypic traits in the Drosophila cuticle [15] . These data demonstrate that microRNAs can buffer variation in phenotypic traits , but it is not clear whether this is achieved by reduced variation in the expression of microRNA target genes or the operation of thresholds for phenotypic outcomes [16] . Zebrafish miR-26b and ctdsp2 mRNA are encoded by the same primary transcript , and ctdsp2 mRNA is a target of miR-26b [17] . The processing of miR-26b is developmentally controlled during neuronal differentiation , effectively initiating a microRNA-mediated incoherent FFL but the consequences for cell-to-cell variation in the expression of ctdsp2 have yet to be established [17] . microRNAs can dampen temporal oscillations in gene expression in C . elegans [18] and reduce fluctuations in the average expression of reporter constructs in mammalian cells [19] . Measurements at the population level , but not in individual cells , showed that methyl CpG-binding protein 2 ( MeCP2 ) acts through BDNF to induce the neuronal miRNA miR-132 , which feeds back to repress MeCP2 [20] . However , simple negative feedback loops like this may increase noise as determined experimentally and computationally [7] . The miR-17-92 family forms a complex network with Cyclin D1 in neuronal progenitors , and the variability of Cyclin D1 expression was increased by heterozygosity in Dicer [21] . The relationship between microRNAs and variability of target gene expression is complicated in this system , since miR-17-92 is required for the differentiation of mouse cortical neuronal progenitors [22] , and reduced microRNA expression affects the frequency of proliferating neuronal progenitors as well as the expression of Ccnd1 within them [21 , 22] . That loss of microRNAs can also result in reduced variability in the expression of pluripotency markers was recently demonstrated for mouse ES cells [23] . Here we address the impact of the microRNA biogenesis pathway on cell-to-cell variability of endogenous gene expression in mouse thymocytes ( developing T cells ) . This system offers a number of key advantages . T cell development proceeds in a series of discrete developmental steps that are defined by the expression of cell surface markers [24] . This allows for ( i ) the precise definition and isolation of cell populations at specific developmental stages for the molecular characterization of microRNA and target mRNA expression , ( ii ) the use of developmentally regulated Cre transgenes for the synchronous deletion of conditional alleles of the RNase III enzyme Dicer , ( iii ) the verification of reduced microRNA expression at defined developmental stages , and ( vi ) like-for-like comparisons between control and Dicer-deficient cells at the same developmental stage . Thymocytes readily form cell suspensions that are ideally suited for analysis and sorting by flow cytometry , and high-quality reagents are available to enable quantitative flow cytometry at the single cell level [25] . Using this approach we demonstrate that microRNAs can reduce cell-to-cell variation of target gene expression in mammalian cells . The activation-induced microRNA target CD69 was regulated by microRNAs in two different ways . miR-181a affected variation by modulating the responsiveness of thymocytes to activation signals , acting at least in part upstream on the target mRNA Cd69 . Members of the miR-17-92 cluster were co-regulated with the target mRNA Cd69 , resembling an activation-induced incoherent FFL .
We previously characterised an experimental system where a developmentally regulated Lck-Cre transgene deletes a conditional Dicer allele in developing mouse thymocytes [26] . As a result , the expression of Dicer-dependent microRNAs was reduced by ∼90% at the CD4 CD8 double positive ( DP ) stage of development ( Fig . 1A ) [26] . miReduce analysis [27] of 3'UTR motifs associated with post-transcriptional de-repression in Lck-Cre DP thymocytes ( see GSE57511 ) showed enrichment for microRNAs miR-181 , miR-17 and miR-142 ( Fig . 1B ) . We evaluated flow cytometry as an approach to determine protein expression by individual cells . To estimate technical noise we examined CD8a and CD8b , which are expressed as obligate heterodimers in DP thymocytes . The mean expression and the coefficient of variation ( CV ) of CD8a and CD8b were very similar for control and Dicer-deficient DP thymocytes ( Fig . 1C , left and centre ) , as was the ratio of CD8a/CD8b expression for individual control and Dicer-deficient DP thymocytes ( Fig . 1D , right ) . The CV of these ratios defines the upper bound of technical noise [25] . Based on published criteria for quantitative flow cytometry [25] we identified antibodies directed against putative microRNA targets including CD44 , a predicted target of miR-21 ( www . targetscan . org ) and established target of miR-34 [28] as well as the predicted miR-181 targets Ly6a and H2-K1 ( www . targetscan . org; Fig . 1D ) . As expected based on elevated mRNA expression ( see GSE57511 ) , Dicer-deficient thymocytes showed higher average expression of CD44 , Ly6a and H2-K1 than control cells . Interestingly , the cell-to-cell variation of CD44 , Ly6a and H2-K1 expression was also increased in Dicer-deficient thymocytes ( Fig . 1D , E ) while there was no change in the negative staining control , MHC class II ( H2-Ab1; Fig . 1D ) . We used the CV as a stringent measure of variation because in contrast to the standard deviation ( SD ) , the CV is expected to decrease as the mean expression increases ( CV = standard deviation/mean ) . An increase in the CV at the same time as an increase in mean protein expression therefore unambiguously indicates an increase in cell-to-cell variation ( Fig . 1D , E ) . As the CV is expected to decrease with the level of expression , the finding of an increased CV in Dicer-deficient cells that expressed higher protein levels prompted several control experiments . First , we asked whether the increased CV was explained by residual microRNA-retaining cells . Experimental mixing and computational modeling experiments indicated that this was highly unlikely ( S1 Fig . ) . Second , the apparent impact on the CV could be due to technical limitations in the detection of low levels of protein expression: if microRNAs reduce expression below the sensitivity or our instrumentation we would detect expression—and associated noise—only in Dicer-deficient cells but not in wild type cells . To address this concern we asked whether the level of protein expression detected in wild type and Dicer-deficient cells was biologically meaningful . We sorted control and Dicer-deficient thymocytes according to the level of CD44 and Ly6a protein detected by flow cytometry and carried out quantitative reverse transcriptase ( RT ) -PCR for Cd44 and Ly6a transcripts ( Fig . 1F ) . The data correlated CD44 and Ly6a protein expression with the abundance of Cd44 and Ly6a mRNA in both control and Dicer-deficient DP thymocytes . Although Cd44 , Ly6a and H2-K1 mRNAs are not confirmed direct microRNA targets in thymocytes , these data demonstrate that our instrumentation discriminates meaningful levels of protein expression . To unambiguously determine the impact of microRNAs on cell-to-cell variability of target gene expression on a direct microRNA target in thymocytes we focused on CD69 , which is inducibly expressed in response to T-cell activation [29] . CD69 controls cell migration and sphingosine 1-phosphate signaling [30] , and the Cd69 mRNA is a well-characterised target of miR-181 and other microRNAs [31–33] . In response to activation signals through the T cell receptor ( TCR ) , DP thymocytes initiated the expression of CD69 ( Fig . 2A ) , and graded activation signals induced a proportional increase of Cd69 mRNA and CD69 protein ( S2 Fig . ) . As expected for an established microRNA target , average CD69 levels were higher in Dicer-deficient than in control DP thymocytes ( S2B Fig . ) . In addition , Dicer-deficient DP thymocytes showed an increase in the CV of CD69 expression ( Fig . 2B ) , and this increase was seen over a range of activation conditions ( Fig . 2C ) . The broader distribution of CD69 expression among Dicer-deficient DP cells was due in part to a greater fraction of CD69hi cells ( Fig . 2D , characterized by the co-expression of CD25 Fig . 2A ) . In addition , Dicer-deficient DP thymocytes showed an increased CV of CD69 expression within the CD69hi CD25+ subset ( Fig . 2E , F ) . Hence , Dicer-deficient DP thymocytes showed increased cell-to-cell variability in the expression of the microRNA target CD69 . This was true whether the CV was assessed for the entire DP thymocyte population , or separately for the CD69+ population or the CD69high CD25+ subset ( Fig . 2G ) . Taken together , our results indicate that microRNAs can shape not only the level but also the cell-to-cell variability of protein expression in developing thymocytes . To investigate the underlying mechanisms we next identified endogenous microRNAs that target Cd69 in DP thymocytes . The Cd69 3'UTR contains predicted sites for miR-181 , miR-130 and miR-17/20 ( http://www . targetscan . org ) and there is firm experimental evidence for Cd69 regulation by miR-181a , miR-130 and the miR-17-92 cluster ( which encodes the microRNAs miR-17 , -18 , -19a , -19b , -20a , and -92 [34] in T lymphocytes [31–33] . To evaluate the impact of endogenous microRNAs on the expression of proteins linked to the 3'UTR of Cd69 we developed a dual fluorescence reporter construct . The construct encodes the two fluorescent reporter proteins , eGFP and mCherry , under the control of separate retroviral long terminal repeat ( LTR ) and mouse Pgk promoters , as well as a cloning site 3’ of the eGFP transcript ( Fig . 3A ) . In a manner similar to luciferase reporter constructs , 3’ UTRs of interest can be cloned into this site to measure their impact on the expression of GFP relative to mCherry . In contrast to heterologous reporter assays , however , this system allows to delineate the biological activity of endogenously expressed microRNAs after retroviral gene transfer of the reporter construct into primary cells . We characterised this dual fluorescence reporter system in mature CD4+ T cells that were isolated from lymph nodes and activated in vitro to render them receptive to retroviral gene transfer ( S3A Fig . ) . To determine the impact of Dicer on the expression of eGFP linked to the 3'UTR of Cd69 we transduced wild type and CD4Cre Dicerlox/lox ( Dicer-deficient ) [35] CD4+ T cells with the reporter construct containing the entire Cd69 3'UTR ( Fig . 3A , Cd69 3'UTR ) . Fig . 3B shows a representative dot plot of mCherry and eGFP-Cd69 3'UTR expression in control ( black ) versus Dicer-deficient CD4+ T cells ( red ) . Compared to the empty control vector , wild type CD4+ T cells expressed eGFP-Cd69 3'UTR at a lower level and Dicer-deficient CD4+ T cells expressed eGFP-Cd69 3'UTR at a higher level ( Fig . 3B , C ) , indicating that as well as repressive miRNA-binding sites , the CD69 3’ UTR may contain sequences that enhance expression . Mutation of the miR-181 site in the Cd69 3'UTR did not measurably affect the expression of eGFP in mature CD4+ T cells ( Fig . 3C ) , which express only low levels of the developmentally regulated miR-181 [31] . However , deletion of the miR-130 and particularly the miR-17/20 site resulted in increased eGFP expression in wild type CD4+ T cells ( Fig . 3C ) . Next , thymocytes were transduced with Cd69 3'UTR reporter constructs and maintained for 24 hours in reaggregation thymic organ cultures until the expression of fluorescent reporters by CD4+ CD8+ DP thymocytes was recorded by flow cytometry . In contrast to mature CD4+ T cells , the miR-181 site affected eGFP-Cd69 3'UTR expression in CD4+ CD8+ DP thymocytes , which express maximal levels of the developmentally regulated miR-181 [31] . The predicted sites for miR-130 and miR-17/20 within the Cd69 3' UTR also affected eGFP reporter gene expression in DP thymocytes . Taken together , these results show that eGFP-Cd69 3'UTR expression was Dicer-dependent in mature CD4+ T cells ( Dicer-deficient thymocytes could not be used successfully for retroviral gene transduction and subsequent reaggregate organ cultures ) and that the impact of predicted microRNA binding sites reflected the developmental regulation of microRNAs [31] . We focused our subsequent analysis on miR-181a and the miR-17-92 cluster . To explore the influence of miR-181 on the CV of CD69 expression we analysed DP thymocytes deficient in mir-181ab1 , which accounts for most of the miR-181a and -b copies in DP thymocytes [36] . Following activation , miR-181-deficient DP thymocytes showed increased mean CD69 expression ( control = 245 ± 17 , mean miR-181 ko = 278 ± 10 , n = 26 , P<10–10 , 2-tailed T-test ) . Interestingly , CD69 expression in miR-181-deficient DP thymocytes also showed an increased CV ( Fig . 4A ) over a range of activation conditions ( Fig . 4B ) . The increased CV was due mainly to a higher fraction of CD69hi cells among miR-181-deficient DP thymocytes ( Fig . 4C ) . The CV of CD69 expression within the CD69hi subset was only mildly affected ( Fig . 4D ) . These results show that miR-181 is an important determinant of cell-to-cell variability in CD69 expression in activated DP thymocytes , and is required to restrict the fraction of CD69hi DP cells . This is consistent with a role for miR-181 as a modulator of TCR signaling [36–38] ( Fig . 4E ) . miRNA expression responds to T-cell activation signals [34 , 35 , 39–45] . Many microRNAs are downregulated upon T-cell activation [40–43] , but the expression of the miR-17-92 cluster is upregulated in activated mouse and human T cells [45] . Since the miR-17-92 cluster encodes microRNAs that target the Cd69 3'UTR , including miR-17 and miR-20a ( Fig . 3 ) , we investigated how the expression of miR-17 and miR-20a was affected by the activation of DP thymocytes . We applied graded stimuli ( 0 , 0 . 1 , 1 and 10μg H57/ml ) to induce a graded increase in Cd69 mRNA expression ( Fig . 5A ) . Interestingly , this graded increase in Cd69 mRNA was accompanied by a proportional upregulation of miR-17 and miR-20a expression ( Fig . 5A ) . Next , we asked how miR-17 and miR-20a expression was related to the range of responses by individual cells to a uniform extracellular signal . When stimulated with a fixed concentration of TCR antibody , DP thymocytes expressed a range of CD69 protein , from undetectable to high ( see Fig . 2A ) . We applied a uniform stimulus ( 1μg H57/ml ) and sorted DP thymocytes that expressed no detectable CD69 protein ( CD69 neg ) , low levels of CD69 ( CD69 lo ) , intermediate levels of CD69 ( CD69 med ) or high levels of CD69 ( CD69 hi ) . Increasing expression of CD69 protein correlated with increasing Cd69 mRNA levels , and with incremental expression of miR-17 and miR-20a ( Fig . 5B ) . Hence , activation signals of increasing strength induce a proportional upregulation of the microRNAs miR-17 and miR-20a and the target mRNA Cd69 . Furthermore , cells exposed to a uniform stimulus show a range of responses , and the induction of microRNAs and mRNA target is coordinated with the expression of the protein encoded by the target mRNA in individual cells . These findings suggest that miR-17 , miR-20a and Cd69 are co-regulated . Mechanistically , the transcription factor Myc provides a link between thymocyte activation and the coordinated regulation of Cd69 and the miR-17-92 cluster . Myc expression is upregulated by signals that drive lymphocyte activation and mediates downstream transcriptional responses [46] ( Fig . 5B ) . Myc and Cd69 are induced by shared signaling pathways downstream of the TCR [47] , and Myc directly activates transcription of the miR-17-92 cluster [48] . These data indicate that the microRNA target mRNA Cd69 and microRNAs of the miR-17-92 cluster form an incoherent feed-forward loop in response to TCR signaling ( Fig . 5C ) . The coordinated regulation of miR-17 , miR-20a and Cd69 in response to TCR signaling provides a potential mechanism for restricting cell-to-cell variability of microRNA target gene expression . To explore this idea further we implemented computational models of noise regulation by microRNAs . In one model , a microRNA and target mRNA are induced together and the microRNAs inhibits the translation of the mRNA as part of an incoherent feedforward loop [8] ( S4A Fig . ) . In an alternative model , a co-regulated pair of microRNA and mRNA interact to induce mRNA degradation [49] ( S4B Fig . ) . Both models predict that microRNA feedforward regulation reduces the mean and the CV of target expression . To implement a more specific model of CD69 regulation we estimated the mRNA copy numbers for Cd69 and the microRNA copy numbers for miR-17 and miR-20a in resting and activated T cells ( see legend Fig . 5D ) . This model predicts that thymocyte activation results in mean CD69 expression of 887 with a CV of 10 . 2% when Cd69 mRNA and miR-17/miR-20a are induced together ( activated with microRNA FFL , filled grey histogram in Fig . 5D ) . In contrast , induction of Cd69 mRNA without upregulation of miR-17/miR-20a results in a higher mean ( 1300 ) and increased CV ( 14 . 6% , activated without microRNA FFL , filled red histogram in Fig . 5D , P<10–4 ) . This result is consistent with our experimental data where the mean and CV of activation-induced CD69 expression were significantly elevated in Dicer-deficient thymocytes: in the absence of a functional microRNA biogenesis pathway , the activation-driven increase in Cd69 mRNA was not balanced by increased miR-17 and miR-20a expression .
microRNAs are essential for mammalian development [50] due to their diverse range of regulatory roles in gene expression . They facilitate developmental transitions by the reciprocal regulation of microRNAs and their targets in cell types derived from a common progenitor [23] and by participating in regulatory circuits with switch-like functions [5] , buffer against environmental and genetic variation [2–5] , limit intrinsic transcriptional noise ( by allowing mRNA 'overproduction' and post-transcriptional removal of excess transcripts ) [2 , 51] and reduce extrinsic noise as part of FFLs [2 , 7 , 8 , 10] , as demonstrated in the current study for a mammalian developmental system . The inducible expression of the established microRNA target Cd69 [31–33] allowed us to explore molecular mechanisms by which microRNAs affect the cell-to-cell variability of target gene expression in thymocytes . miR-181 is a known modulator of TCR signal transduction [36–38] and our data show that the deletion of mir-181ab1 affected the CV of CD69 expression mainly by altering the proportion of thymocytes that expressed CD69 at high levels . The expression of miR-181a is downregulated as thymocytes mature [31] and in this way may account for developmentally regulated changes in the responsiveness of thymocytes to TCR signaling . Our data are consistent with this model and further suggest that developmental regulation of miR-181a reduces cell-to-cell variability of thymocyte responses to TCR signaling . A different mechanism applies to the regulation of CD69 by miR-17 and miR-20a , two microRNAs of the miR-17-92 cluster . Our data show that the expression of these microRNAs is induced together with Cd69 mRNA in response to TCR signals , and that the expression of CD69 protein , Cd69 mRNA and miR-17/miR20a is proportional in thymocytes . This co-regulation of microRNAs and target mRNA has the potential for feed-forward regulation . While the specific circuitry that places Cd69 and miR-17-92 under the shared control of TCR signaling remains to be elucidated , Myc and Cd69 are induced by shared signaling pathways downstream of the TCR [47] , and Myc directly activates transcription of the miR-17-92 cluster [48] . Computational and experimental data suggest that FFLs can confer microRNA-mediated robustness of target gene expression by reducing noise that originates upstream of the transcription of the target mRNA itself [8 , 10] . Modeling the impact of microRNA feedforward regulation either by translational inhibition or mRNA degradation predicted a reduction in the mean and CV of target expression [8 , 49] . This was confirmed by modeling the experimentally estimated copy numbers of Cd69 mRNA and miR-17 and miR-20a in resting and activated T cells . Of note , while all models captured the ability of microRNAs to reduce both the average expression and the CV of microRNA targets , they nevertheless overestimated the actual impact of microRNA-mediated feed-forward regulation . Neither model fully predicted the complexity of the data , specifically the experimentally observed skewing of expression at the top end of the expression spectrum . This indicates that the current models do not fully capture the integration of microRNAs into biological circuits and their impact on gene expression . TCR signaling drives developmental decisions in thymocytes according to a specific set of rules: too little signal results in a failure to differentiate ( 'neglect' ) , too much signal results in activation-induced cell death ( 'negative selection' ) [24] . Intermediate signals induce thymocyte differentiation ( 'positive selection' ) towards CD4-expressing T helper and CD8-expressing cytotoxic T cells . The nature and strength of signals also directs differentiation towards specialized T cell subsets such as regulatory T cells ( Treg cells ) and natural killer T cells ( NK-T ) . The functionality of CD4 , CD8 , Treg and NK-T cells depends on their TCR specificity and it is therefore critical that signal strength and lineage choice are appropriately matched [24 , 52] . microRNAs are intimately involved in T cell lineage choices [26 , 35 , 36–38 , 53–57] . The ability to mount predictable responses to extracellular signal is therefore as important for T cell development as it is for other developmental decisions and we speculate that the exploration of microRNA-mediated regulation of cell-to-cell variation in gene expression in other cell types will prove relevant for understanding normal development and disease .
Mouse work was done according the UK Animals ( Scientific Procedures ) Act under the authority of project licences issued by the Home Office , UK . LckCre Dicer [26] , CD4Cre Dicer [35] and mir-181ab1-deficient mice [36] have been described . Fixation and intracellular staining of thymocytes were done as described [39] , Antibodies used were RM4-5 ( anti-CD4 ) , 53-6 . 7 ( anti-CD8a ) , 53-5 . 8 ( anti-CD8b ) , PC61 ( anti-CD25 ) , H1 . 2F3 ( anti-CD69 ) , IM7 ( anti-CD44 ) , E13-161 . 7 ( anti-Ly6a ) , AF6-88 . 5 ( anti-H2-K1 ) , and 11-5 . 2 ( anti-H2-Ak; Becton Dickinson ) and cells were analysed and sorted on FACS Calibur , LSR II and FACS Aria instruments ( Becton Dickinson , Oxford , UK ) . Mature CD4+ T cells were activated with anti-CD3 and anti-CD28 for 24 hours , thymocytes were activated with the indicated concentrations of plate-bound T cell receptor beta antibody H57-597 and 2ug/ml of anti CD28 ( 37 . 51 ) for 18 hours . Dual Fluorescence reporter constructs were based on pMSCVpuro plasmids ( Clontech ) and contained cDNAs for the fluorescent reporter proteins eGFP under the control of the retroviral LTR and mCherry under the control a separate Pgk promoter , as well as a cloning site in the 3’ UTR of eGFP for the introduction of 3’ UTRs . 3’ UTR fragments were cloned from lymphocyte cDNA and microRNA site mutations introduced by PCR . Retrovirus was produced and activated mature CD4+ T cells or newborn thymocytes were transduced by spin infection as described [58] . Cells were reaggregated with dissociated stromal cells from deoxyguanosine-treated embryonic thymi as described [59] , recovered 24 hours later and reporter fluorescence was assessed by flow cytometry . To model the relationship between GFP and mCherry we used orthogonal linear regression , with the relative level of eGFP to mCherry calculated as the slope of the fitted line . These ratios of eGFP expression to cherry expression are normalised to the eGFP/mCherry ratio of the empty vector , to quantify the change in eGFP expression in experimental vectors compared to the empty vector . By comparing eGFP expression from control and Dicer-deficient cells the level of miRNA-dependent repression can also be observed . RNA was extracted from three biological replicates of Dicerlox/lox and DicerΔ/Δ DP thymocytes , and processed for Affymetrix Mouse Genome 430 2 . 1 array hybridisation as described [58] . Gene expression array data have been deposited at Geo under accession number GSE57511 . Array data were analysed using dChip ( http://www . dchip . org ) . Microarray probe sets were mapped to Refseq transcripts [60] . microRNA sequences were from miRBase [61] . 3' UTR nucleotide motifs were identified using miReduce [27] . Total RNA was isolated using RNAbee ( Tel-Test , Friendswood , TX ) and reverse transcribed . PCR reactions included 2x SYBR PCR Master Mix ( Qiagen ) , 300nM primers and 2 μl of cDNA as a template in 50μl reaction volume . Cycle conditions were 94°C for 8 min , 40 cycles of 94°C for 30 sec , 55°C for 30 sec , 72°C for 1 min , followed by plate read . All primers amplified specific cDNAs with at least 95% efficiency . Data were normalized to the geometrical average of two housekeeping genes , using the CT method as outlined in the Applied Biosystems protocol for reverse transcriptase-PCR . Primer sequences were ( 5' to 3' ) : Ywhaz fw CGTTGTAGGAGCCCGTAGGTCAT rev TCTGGTTGCGAAGCATTGGG Ube fw AGGAGGCTGATGAAGGAGCTTGA rev TGGTTTGAATGGATACTCTGCTGGA Computational modeling of microRNA effects on target gene expression was done as described [8 , 49] . | microRNAs are integral to many developmental processes and may 'canalise' development by reducing cell-to-cell variation in gene expression . This idea is supported by computational studies that have modeled the impact of microRNAs on the expression of their targets and the construction of artificial incoherent feedforward loops using synthetic biology tools . Here we show that this interesting principle of microRNA regulation actually occurs in a mammalian developmental system . We examine cell-to-cell variation of protein expression in developing mouse thymocytes by quantitative flow cytometry and find that the absence of microRNAs results in increased cell-to-cell variation in the expression of the microRNA target Cd69 . Mechanistically , T cell receptor signaling induces both Cd69 and miR-17 and miR-20a , two microRNAs that target Cd69 . Co-regulation of microRNAs and their target mRNA dampens the expression of Cd69 and forms an incoherent feedforward loop that reduces cell-to-cell variation on CD69 expression . In addition , miR-181 , which also targets Cd69 and is a known modulator of T cell receptor signaling , also affects cell-to-cell variation of CD69 expression . The ability of microRNAs to control the uniformity of gene expression across mammalian cell populations may be important for normal development and for disease . | [
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] | [] | 2015 | microRNAs Regulate Cell-to-Cell Variability of Endogenous Target Gene Expression in Developing Mouse Thymocytes |
Genetic studies have identified a core set of transcription factors and target genes that control the development of the neocortex , the region of the human brain responsible for higher cognition . The specific regulatory interactions between these factors , many key upstream and downstream genes , and the enhancers that mediate all these interactions remain mostly uncharacterized . We perform p300 ChIP-seq to identify over 6 , 600 candidate enhancers active in the dorsal cerebral wall of embryonic day 14 . 5 ( E14 . 5 ) mice . Over 95% of the peaks we measure are conserved to human . Eight of ten ( 80% ) candidates tested using mouse transgenesis drive activity in restricted laminar patterns within the neocortex . GREAT based computational analysis reveals highly significant correlation with genes expressed at E14 . 5 in key areas for neocortex development , and allows the grouping of enhancers by known biological functions and pathways for further studies . We find that multiple genes are flanked by dozens of candidate enhancers each , including well-known key neocortical genes as well as suspected and novel genes . Nearly a quarter of our candidate enhancers are conserved well beyond mammals . Human and zebrafish regions orthologous to our candidate enhancers are shown to most often function in other aspects of central nervous system development . Finally , we find strong evidence that specific interspersed repeat families have contributed potentially key developmental enhancers via co-option . Our analysis expands the methodologies available for extracting the richness of information found in genome-wide functional maps .
Among all vertebrates , the developing central nervous system segments into a forebrain , midbrain , hindbrain , and spinal cord [1] . The forebrain is further segmented into the telencephalon and diencephalon . In mammals , the dorsal portion of the telencephalon gives rise to the neocortex ( isocortex ) . The mature neocortex is a complex six-layered structure unique to mammals [2] , [3] . It has been associated with higher cognitive functions [4] , and defects in this structure are the likely source for many neurologic and psychiatric diseases [5] . Early in development , this region consists of a layer of progenitor cells lining the ventricles called the ventricular zone ( VZ ) . Progenitor cells of the VZ produce intermediate progenitor cells that migrate out of the VZ to form the subventricular and intermediate zones ( SVZ-IZ ) ; daughter cells from both areas migrate past the SVZ-IZ to form the laminar structure of the cortical plate ( CP ) , in an inside out fashion [6] , [7] ( Figure 1A ) . While the anatomy , histology , and gene expression patterns of the developing neocortex and its progenitor populations have all been well studied , attention is only starting to focus on gene regulation during neocortex development [8] . The advent of chromatin immunoprecipitation and related capture technologies , coupled with deep sequencing ( ChIP-seq ) allows us to obtain whole genome maps of active enhancers through development , and beyond . The study of enhancers provides several advantages: First , it reveals a sizable layer of genomic susceptibility to disease that extends beyond protein coding sequence , and has remained almost invisible hitherto . Second , because enhancers integrate signals from upstream transcription factors and signaling pathways , enhancer maps can unravel the causality of gene expression and developmental processes . Finally , observing enhancer sequence and function change between humans and related species promises to provide additional insights into the evolution of our brain . Here , we produce an active enhancer map in the dorsal cerebral wall at E14 . 5 using ChIP-seq to assay for the enhancer-associated co-activator protein p300 . We proceed to validate multiple enhancers next to genes of particular interest to neocortical development . We also develop a series of computational analyses that demonstrate the riches of information exposed by this type of assay for studies of neocortex development and evolution . Our methodology can be combined with current research in other tissues to advance our understanding of the complex regulatory networks that underlie organ development .
To identify enhancers that function during neocortex development , we dissected the dorsal cerebral wall , which includes the developing neocortex and its progenitor populations , from E14 . 5 mouse embryos ( Figure 1A ) and performed chromatin immunoprecipitation followed by high-throughput sequencing ( ChIP-seq ) with an antibody against the enhancer-associated p300 co-activator complex ( see Methods ) . This approach has successfully identified tissue specific developmental enhancers in several other contexts [9] , [10] . We identified 6 , 629 p300 bound sites ( >2 . 5 kb from the nearest transcription start site ) , which are candidate developmental enhancers ( Table S1 ) . As seen with other sets of enhancers [11] , the majority of these elements are distal , with 65% being more than 50 kilobases from the nearest transcription start site ( Figure 1B ) . To globally assess the quality of our peak set , we first correlated the set with the pre-existing body of knowledge of neocortex development . Because p300 is an active enhancer mark , we asked whether our set of E14 . 5 p300 elements is correlated with gene expression patterns in the assayed tissue at the assayed time point . GREAT ( for Genomic Regions Enrichment of Annotations Tool ) is an approach and web tool ( at http://GREAT . stanford . edu/ ) devised specifically to assess enriched functions within a set of genomic regions thought to regulate the adjacent genes [11] . GREAT associates each gene in the genome with a variable length regulatory domain , bracketed by its two neighboring genes . GREAT holds a large body of knowledge about gene functions and phenotypes , curated from multiple different sources . Each term in GREAT is a list of genes that have functional commonalities ( e . g . “involved in axon guidance” ) . Terms for a similar perspective of biology ( e . g . , molecular function ) are collected into a GREAT ontology . To quantify gene expression coherence we examined our set of p300 elements against the GREAT “MGI expression” ontology . This ontology is built from the MGI Gene Expression Database [12] , and lists endogenous genes expressed in specific anatomical structures at specific developmental stages during mouse development , curated from the literature . To test our p300 set of elements against the GREAT “MGI expression” ontology , GREAT iterates over 8 , 374 different tissue-timepoint combinations ( terms ) found in the MGI expression ontology , asking whether p300 elements are particularly enriched in the regulatory domains of genes of any particular term . For example , 1 , 226 genes in the human genome are annotated for “Theiler stage ( TS ) 22 cerebral cortex” , which corresponds to our tissue and timepoint of interest [13] . Their GREAT assigned regulatory domains cover 15 . 86% of the genome . Of the 6 , 629 p300 elements , 1 , 051 ( 15 . 86% ) are expected in the regulatory domains of these 1 , 226 genes by chance , whereas 1 , 811 p300 elements , 1 . 72 times as many , are in fact observed ( p-value: 9 . 5×10−124 ) . GREAT shows similar strong enrichments for TS22 telencephalon and forebrain expressed genes ( Table 1 ) . At E14 . 5 , the transient embryonic ventricular ( VZ ) and subventricular ( SVZ ) zones generate neurons that migrate across the intermediate zone ( IZ ) to the overlying cortical plate ( CP ) , where they differentiate to form the neocortex . Because the tissue we measured contained all these areas , we wanted to know whether the different areas are well represented in our p300 set . To do so we utilized data from a recent study that used RNA-seq to measure expression levels in the VZ , SVZ-IZ , and CP at E14 . 5 , obtained via laser capture microdissection ( LCM ) [14] ( Figure 1A ) . First we note that p300 itself is expressed very similarly in all three regions: 10 . 83 RPKM ( mean Reads Per exonic Kilobase per Million mapped reads ) in the VZ , 11 . 05 in the SVZ-IZ and 9 . 11 in the CP; in the 23rd–24th percentile of all measured genes in all three regions . By comparing expression of all genes across the three regions we constructed three smaller lists of genes exclusively expressed in only one of these regions ( see Methods ) . We then used GREAT to assess our p300 set enrichment next to these region-specific genes . The set is enriched against all three ( p-value between 1 . 1×10−25 and 1 . 8×10−18 ) , suggesting that the p300 set sampled the major regions of the E14 . 5 developing neocortex ( Table 1 ) . A very recent publication reports 4 , 425 peaks from assaying p300 in E11 . 5 mouse forebrain , and 1 , 132 peaks from assaying a p300/CBP antibody in P0 mouse cortex [15] . CBP is a close paralog of p300 which plays a similar role in mediating active enhancer interactions . Of our 6 , 629 E14 . 5 peaks , 1 , 340 ( 20 . 21% ) overlap the E11 . 5 set , and 235 ( 3 . 55% ) peaks overlap the smaller P0 set of peaks . Both enrichments are highly significant , attesting to the quality of our set ( uniform shuffling of our E14 . 5 peaks , fold 53 . 68 for E11 . 5 forebrain and fold 28 . 53 for P0 cortex ) , yet 5 , 153 ( 77 . 73% ) of our E14 . 5 peaks are novel , overlapped by neither set . Another publication assays CBP in E16 . 5 cortical neurons cultured for 7 days , before and after membrane depolarization [16] . They obtain fewer than 1 , 000 peaks before and approximately 28 , 000 peaks after stimulation , the latter mostly subsuming the peaks pre-stimulation . Of our 6 , 629 E14 . 5 peaks , 2 , 187 ( 32 . 99% ) are overlapped by the larger set . This overlap is also highly significant ( uniform shuffling of our E14 . 5 peaks , fold 15 . 09 ) , while 4 , 442 ( 67 . 01% ) of our peaks are unique . Previous studies of p300 ChIP-seq sets report up to 80% success in validating enhancer candidates using a transient transgenesis approach [9] , [10] , [17] . We chose ten enhancer candidates from our E14 . 5 p300 set , which lie next to genes known or suspected to play a role during embryonic neocortical development . None of these enhancer candidates overlapped a p300 peak from previous E11 . 5 forebrain ( including both dorsal and ventral telencephalon ) or P0 data [9] , [15] , and none have been reportedly previously tested in the VISTA browser [18] . Eight ( 80% ) of these ten E14 . 5 p300 peaks drive reproducible expression in the developing neocortex in at least 3 , and always a majority of positive embryos ( Figure 2A–H; Figures S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 ) . Coronal sections reveal that the assayed enhancers drive dorsal-specific expression , exclusive of the ganglionic eminences of the ventral telencephalon ( Figure 2I–P ) . Sections also reveal laminar restriction of enhancer activity ( Figure 2Q–X , see Discussion ) . Transient transgenesis experiments are low throughput and costly . To provide a higher-throughput cost-effective assay we also tested our ten candidates in a transient transfection system , where the dorsal cerebral wall is dissected and dissociated from the brains of E14 . 5 mice and then left to incubate for two additional days along with the transfected reporter constructs ( see Methods ) . Five ( 63% ) of the eight positive transgenics scored significantly higher than our empty vector and two negative transgenics in our transfection system ( Figure 2AG ) . This suggests that our transient transfection system can provide a reliable , if imperfect , rapid system for preliminary screening of candidate developmental enhancers . Our set of over 6 , 000 candidate enhancers likely regulates multiple different developmental processes that are taking place in the dorsal cerebral wall at E14 . 5 . We use additional GREAT ontologies to parse out multiple different functions ( Table 1 ) : Using the Gene Ontology ( GO ) Molecular Functions ontology we see that our highest enrichment is for regulation of genes that themselves are involved in gene regulation ( 307 enhancers , p-value: 7 . 5×10−37 ) , such as Fox , Sox and Pax transcription factors . The GO Biological Processes ontology highlights candidate enhancer groups that regulate processes well known to take place during neocortex development , including gliogenesis , axon guidance , and general telencephalon development . The Pathway Commons ontology highlights enhancer groups regulating specific pathways , including Notch , Reelin and netrin . The Mouse Phenotype ontology allows one to focus on groups of enhancers that regulate genes that share common cortical developmental defects , including abnormal neuron differentiation , abnormal forebrain development , and abnormal brain commissure development ( Table 1 ) . ChIP-seq of different transcription factors ( TFs ) in a variety of contexts has shown them to bind reproducibly next to thousands of target genes . In particular , TFs have been repeatedly shown to bind near hundreds of genes specific to the contexts they are known to regulate , suggesting a high “fan out” of transcription regulation [11] . To search for some of the most abundant transcription factor binding motifs in our p300 set , we employed a standard three phase approach: First , we ran several published motif discovery tools to search de novo for over abundant motifs in our data; the obtained motifs were then compared to our library of known TF motifs to collapse redundant motifs; finally , the combined set of known and putative novel TF motifs were predicted across the p300 set and assessed for over-abundance against GC-matched control regions from the mouse genome ( see Methods ) . We identified a number of distinct enriched motifs , most of which belong to known important regulators of neocortex development ( Figure 3 ) . The Neurod/Neurog ( 2 , 452/6 , 629 enhancers = 37%; fold: 2 . 39 ) , Lhx/Lmx ( 2 , 129 = 32%; fold: 2 . 42 ) , Nfi ( 325 = 5%; fold: 4 . 14 ) , and Rfx dimer ( 195 = 3%; fold: 3 . 33 ) motifs are all highly enriched in the candidate p300 enhancers . Factors from all four families have known roles in mammalian brain development [7] , [19]–[21] . We also discovered two novel motifs enriched in the set: an alternative configuration from the known Nfi dimer motif [22] ( 379 = 6%; fold: 2 . 06 ) and a novel Hox dimer motif ( 473 = 7%; fold: 2 . 32 ) . The candidate enhancers we measured exhibit a tendency to cluster together , with some genes having tens of p300 peaks in their predicted regulatory domains . To determine what would be expected by chance , we randomly distributed the 6 , 629 peaks across the genome 1 , 000 times . In this random null ( which controls for gene regulatory domain length ) , we never observed any gene associated with more than 15 peaks ( Figure 4A ) . In our true set , the most heavily regulated genes are associated with 20–42 peaks each . We can also use GREAT to rank all genes in the genome for the likelihood associated with the observed number of enhancers per gene vs . the length of the individual gene's regulatory domain ( note that in this test , a gene with a smaller regulatory domain containing multiple enhancers , can rank higher than a gene with a much larger regulatory domain which contains more enhancers ) . When this variant of the GREAT test is run , the top ten most significant genes are the same ten genes with the absolute largest number of observed enhancers ( p-value between 1 . 3×10−15 and 1 . 6×10−31 ) . Three of these genes , Nfib , Sox4 and Sox11 are already known to play key roles in forebrain development . Three other genes , Zfp608 ( Figure 4B ) , Auts2 and Tle3 have previously been noted for their specific neocortical expression patterns , though their roles in its development are not well understood . Intriguingly , two additional gene deserts , flanked by the gene pairs Mn1-Cryba4 ( Figure 4C ) and Gse1-Fam92b , all with unknown roles in neocortex development , are also packed with p300 elements ( Table 2 ) . The six-layered neocortex is a mammalian specific innovation , while the progenitor populations are present in non-mammals [2] , [3] . In non-mammalian jawed vertebrates ( Gnathostomata in Figure 5A ) , the post-mitotic neurons do not organize into a six-layered cortex [3] , [6] . In birds , for example , the neurons in the CP develop into the hyperpallium . Although the hyperpallium is topologically analogous to the neocortex , it has a nuclear structure rather than a laminar structure [3] . We examined cross species ( orthologous ) conservation of our 6 , 629 candidate enhancers to trace their origins and mode of evolution . The majority ( 4 , 278; 65% ) of our candidate enhancers exhibit signatures of evolutionary sequence constraint ( PhastCons score >350 ) , suggesting that they have been evolving under purifying selection for millions of years . Very few elements appear specific to the mouse lineage . In particular , over 95% ( 6 , 317 ) are orthologously conserved to human . Over 86% ( 5 , 737 ) are common to all eutherian ( placental ) mammals . Nearly a quarter ( 1 , 543; 23% ) of our peaks pre-date the mammalian innovation of the neocortex . In comparison , fewer than 5% of heart p300 ChIP-seq peaks [9] are conserved outside of mammals , and over 35% of forebrain p300 ChIP-seq peaks from E11 . 5 embryos [10] are conserved outside of mammals ( Figure 5B ) . The forebrain encompasses both the telencephalon and diencephalon , and at E11 . 5 it consists of mostly progenitor cells [7] . The deeper conservation of E11 . 5 forebrain enhancers is consistent with the hypothesis that the early forebrain is more homologous across vertebrates [1] . For 214 of our elements , the human ortholog has been tested in a mouse transgenic enhancer assay at E11 . 5 [23] . 148 of these elements function as developmental enhancers at this earlier time point . As expected , the majority of these elements indeed show expression in the forebrain . However , large and highly significant ( all P<10−5 , see Methods ) subsets of active elements drive expression in additional structures of the developing central nervous system , including the midbrain , hindbrain and neural tube ( Figure 5C ) . Of our 6 , 629 p300 elements , 289 ( 4% ) are conserved in fish . The zebrafish ortholog for 21 of our elements were assayed in a large zebrafish enhancer screen [24] . Twenty drive reproducible expression patterns in the developing zebrafish embryo . Again , the majority is seen to drive expression in the zebrafish forebrain ( Figure 5D ) . Although a fraction of our candidate enhancers likely evolved from pre-existing enhancers ( above ) , others have likely arisen de novo [25] , [26] . One mechanism of particular interest for the generation of novel enhancers is through the co-option of mobile elements [27]–[29] . To determine if repetitive elements may have been co-opted as dorsal cerebral wall enhancers , we compared the overlap between our p300 set and all annotated interspersed repeat families in the UCSC genome browser . To control for the very different abundance of different repeat families , we shuffled our p300 set 10 , 000 times and noted the number of times the random sets overlapped each repeat family . For comparison , we repeated the same procedure with the four sets of previously obtained E11 . 5 p300 elements in forebrain , midbrain , limb and heart [9] . The most abundantly overlapping family of repeats with our E14 . 5 data is the MIRb family , which overlaps 238 p300 elements . This family has been noted before to be among the largest contributors to gene regulatory co-option among all mobile element families [30] . However , because many more copies of this repeat family are found in the genome , its fold enrichment of 1 . 84 against random overlaps is relatively low . In contrast , three poorly studied repeat families are found to make an extremely unlikely contribution to our p300 set: MER130 , UCON31 and MER124 . For the most enriched , MER130 , 22 ( 24% ) of 90 instances identified in the mouse genome overlap our E14 . 5 set , a 73 fold enrichment over expected ( Figure 6 ) . The p300 peaks we collected can at times be combined with signatures of genome evolution to accelerate functional analysis and hint at evolutionary developmental events of potential interest . For example , Fezf2 is an important gene for neuronal fate determination . A recent paper studied the genomic regulation of Fezf2 during neocortex development [8] . The authors first identified four sequence conserved genomic regions ( dubbed E1–E4 ) flanking Fezf2 . When each was separately deleted from a BAC containing a reporter gene knocked into the Fezf2 gene locus – only E4 affected neocortical reporter gene expression . Impressively , the authors went on to show that a knockout of the E4 enhancer resulted in aberrant cortico-spinal projection , similar to mutant mice where the E4 target gene Fezf2 has been deleted specifically in the cortex [8] . If we look at our data , E4 overlaps the one and only p300 peak observed in 180 kb of genomic sequence flanking the Fezf2 locus in that BAC ( Figure 7A ) . During early neocortex development , Fezf2 and Tbr1 work in antagonistic fashion to determine different neuronal projection fates [31] , [32] , suggesting that a Tbr1 regulatory element may play a similar key role to Fezf2's E4 . Downstream of Tbr1 lies a 230 kb gene desert containing dozens of conserved elements , but completely devoid of our E14 . 5 p300 peaks . A single p300 element lies in the 50 kb upstream of Tbr1 , 5 kb upstream of the gene , making it an intriguing candidate for further analysis ( Figure 7B ) . While the p300 peaks may currently serve to functionally pit Fezf2 and Tbr1 against each other , their evolutionary profile is markedly different . The Fezf2 proximal p300 peak ( E4 ) is conserved to fish , and does not overlap any known repeat . The human orthologous sequence of this peak drives forebrain expression in E11 . 5 transgenic mice [18] , and the zebrafish orthologous sequence drives forebrain expression in 24-hour zebrafish embryos [24] . In contrast , the Tbr1 peak is found only in mammals , and at its center lies a co-opted AmnSine1 repeat instance . The AmnSine1 repeat family is significantly enriched in our E14 . 5 set ( 3 . 9 fold , Figure 6 ) . Intriguingly , of the 16 instances we observe overlapping our p300 set , four lie in the regulatory domains of genes that play crucial roles in neocortical neuron fate determination: Tbr1 ( above ) , Satb2 ( elt2 in Figure 2 ) , Sox5 , and Reln . Indeed , the Satb2 co-opted element was recently characterized as a neocortex-specific enhancer [33] .
In this study , we have identified the first genome-wide set of p300 bound regions specific to E14 . 5 dorsal cerebral wall . We have shown using GREAT and by sampling candidates experimentally that the set we obtained is highly enriched in active enhancers for neocortex development . This set of candidate enhancers provides a rich source for studying neocortex development and evolution . Three major cell populations contribute critically to neocortex development at E14 . 5 ( Figure 1A ) . By curating population specific gene expression data into a GREAT ontology , we show that enhancers serving all three major populations are enriched within our set . We also used other GREAT ontologies to subdivide the large enhancer mass into subsets that serve specific processes of interest in different dorsal cerebral wall populations at this stage , strongly suggesting that despite the heterogeneity of input material , numerous insights can be had into the different processes taking place in this developing tissue ( Table 1 ) . Key transcription factors ( TFs ) often bind directly ( both proximally and distally ) next to a large number of genes in their relevant context [11] . This allows us to utilize motif discovery to predict key TFs and TF dimers found in a large number of our active enhancers ( Figure 3 ) . In circuit design terminology this property is known as large “fan out” ( in this case of regulatory interactions ) from TF to target genes ( via binding sites and enhancers ) . When we turn our point of view from regulators to regulated genes , we first looked for target genes with large “fan in” , namely genes in whose regulatory domains lie a larger than expected number of p300 peaks ( Figure 4 ) . The mammalian genome is known to contain multiple large gene deserts carrying numerous conserved and likely cis-regulatory sequences [34] . However , one cannot deduce from sequence patterns alone how many cis-regulatory regions are active simultaneously in any given functional context . Here we show that a number of genes carry dozens of p300 peaks in their regulatory domains during neocortex development , many more than would be expected by chance . It has been hypothesized that multiple seemingly-redundant enhancers co-exist in order to generate expression patterns that are robust to environmental variation [35] , [36] . Multiple enhancers targeting the same gene also likely reduce the variability associated with stochastic gene regulation [37] . Finally , it is also possible that different enhancers target different cell populations during neocortex development . In focusing on the ten most heavily regulated genes ( Table 2 ) , we discover three well known genes in the context of neocortex development , and three additional genes already suspected of playing an important role because of their restricted expression pattern during neocortex development and correlations with neocortical-associated diseases . We also find two intriguing gene deserts , dense in p300 elements , that are flanked by two pairs of genes with no known role in neocortex development . In both cases , transcriptional evidence is not seen for other , possibly non-coding , transcripts within the gene deserts , and in both cases only one of the two flanking genes appears to be expressed in the neocortex ( Figure 4 ) . In both cases this gene is either a known transcription regulator ( Mn1 ) , or is suspected of being one ( the coiled-coil Gse1 gene ) . Perhaps one of the most challenging questions to ask from enhancer data such as ours lies at the intersection of genomics and genetics . Namely , which enhancers form the “weak points” of the network , or in other words , which enhancers will cause a clear developmental defect when mutated ? The Fezf2 E4 enhancer provides one such example in the context of the neocortex ( Figure 7 ) . The Fezf2 gene belongs to a small network of transcription factors that controls cell fate determination within the neocortex [38] . Scanning the p300 landscape around the other genes in this network we find a particularly compelling landscape around the Tbr1 gene , with a single peak proximal to this key target gene , and few others further away ( including elt4 from Figure 2 , over 50 kb upstream ) . At the center of the proximal peak lies a co-opted instance of AmnSine1 . Strikingly , AmnSine1 overlapping p300 peaks are found next to several additional key genes for early neocortex development , suggesting that perhaps a subset of AmnSine1 co-option events were crucial in laying out the cortical projection network as we know it today [39] . Members of multiple interspersed repeat families have likely contributed important enhancers during genome evolution ( Figure 6 ) . This contribution has been previously noted based on the large intersection between conserved non-coding sequence and sequences from mobile element origins [30] . The functional roles of the co-opted loci , however , could not be easily deduced from sequence alone . By intersecting mobile elements with functional data , we are able to assign specific functions to subsets of loci . This allows us to highlight several poorly studied repeat families in the context of neocortex development , as well as shed new light on cases such as the MER121 family , which was previously studied in sequence [40] , but can now be implicated in contributing to limb development ( Figure 6 ) . Interestingly , nearly half of AmnSine1 and MER121 human instances were very recently found to overlap open chromatin from 41 cell types , suggesting possible enhancer activity in multiple additional contexts [41] . Two of our tested enhancers – elt4 and elt7 – drive expression in the most superficial cells of the developing neocortex ( Figure 2 ) . These patterns match a domain of the expression and functional activity of Tbr1 and Bhlhb5 , their nearby and likely respective target genes [42] , [43] . The other six enhancers are active primarily in the CP and SVZ-IZ . In total , six of the eight positive enhancers drive expression largely within the domain of activity of the putative target gene [14] , [44] . Two enhancers drive expression patterns that include a zone outside the detected expression regions of the putative target . These elements – elt1 and elt6 – drive expression in the CP and SVZ-IZ although their putative target genes ( Eomes/Tbr2 and Id4 ) are expressed primarily in the SVZ-IZ and VZ . These elements may regulate a different nearby gene or their in vivo expression pattern may be modified by flanking regulatory sequence or epigenetic state not captured in our transgenic constructs . Interestingly , our validated enhancers mostly drive expression outside the VZ . Our statistical analysis suggests that our full set is strongly enriched near genes expressed predominantly in the VZ ( Table 1 ) . Moreover , of 40 enhancers showing expression in the VZ of the dorsal pallium at E11 . 5 [15] , 26 ( 65% ) are marked by p300 peaks in our E14 . 5 set . Finally , as the large ( but far from exhaustive ) number of vignettes in our paper illustrates , the biggest challenge for the study of functional genomic data is twofold: First , to develop a set of approaches and tools to mine these datasets and their combinations for the almost staggering wealth of information they offer . Second , a broader challenge relates to the coming together of different disciplines of researchers , including functional genomicists , computational biologists , developmental biologists , geneticists , and more , so that the mining of this data is maximized .
Embryos were harvested from timed pregnant embryonic day 14 . 5 ( E14 . 5 ) Swiss Webster mice ( Charles River ) . The dermis , skull mesenchyme , and bone primordia were removed and cortical caps were dissected with curved forceps and placed in PNGM ( Lonza ) . The medial structures , cortical hem/hippocampus and choroid plexus were cut off in a secondary excision . Dissected dorsal cerebral wall tissue ( 0 . 15 g ) was snap frozen in liquid nitrogen . Tissue was fixed in 1% formaldehyde for 15 minutes . Chromatin was isolated , sheared and immunoprecipitation was performed using 30 micrograms of chromatin and 4 micrograms of anti-p300 antibody , C-20 ( Santa Cruz SC-585; Genpathway ) . Chromatin from the same sample was processed for the input control . Library construction and sequencing was done using the Illumina GA II format ( Illumina ) . This produced 17 , 460 , 074 uniquely mapped 36 bp reads for the treatment and 15 , 669 , 334 uniquely mapped reads for the input control . ChIP-seq reads were mapped to the mouse genome ( UCSC mm9 assembly , NCBI MGSCv37 ) using ELAND , retaining only reads that map uniquely with 2 or fewer mismatches . Peaks were called using MACS [45] with the p300 ChIP-seq reads as the treatment file , input DNA reads as the control file , and the parameters “--nomodel , --shiftsize = 100 , -g mm” . Peaks overlapped by an exon , within 2 . 5 kb of a transcription start site , or suspected in non-unique read mapping were removed . Exon and transcription start site annotation was obtained from the UCSC knownGene track ( build 5 ) [46] . The median fold enrichment over input for our 6 , 629 peaks is 7 . 11 ( and average 7 . 83 ) . To evaluate functional and expression enrichments , we used GREAT v2 . 0 . 0 [11] with the default association rule ( 1 kb+5 kb basal domain with up to 1 Mb extension and curated regulatory domains ) and default significance thresholds ( region-based binomial fold ≥2 , region-based binomial FDR≤0 . 05 , gene-based hypergeometric FDR≤0 . 05 ) . A lower region-based binomial fold criterion was used for the MGI Expression ontology . We evaluated specific enrichment in the ventricular zone , subventricular and intermediate zones , and cortical plate using a custom-built ontology based on a recent RNA-seq dataset [14] . We consider a gene to be specific to a layer if it has a layer RPKM ( mean Reads Per exonic Kilobase per Million mapped reads ) >64 and >2× ( RPKM of the adjacent layer , or average of both adjacent layers for the subventricular and intermediate zones ) . The ten candidate elements for transgenic and transfection assays had p300 fold enrichments ranging from 4 . 92 to 19 . 18 ( 90th to 1st percentile , with average rank in the 37th percentile ) . Candidates were PCR amplified from mouse genomic DNA ( Clontech ) , cloned into pENTR/D ( Invitrogen ) , and then Gateway cloned with LR Clonase ( Invitrogen ) into a HSP68-lacZ-Gateway DEST vector ( a gift from Nadav Ahituv , UCSF ) . Primers are listed in Table S2 . Constructs were linearized with SalI prior to injection . Transgenic mice were generated by pronuclear injections of FVB embryos ( Xenogen Biosciences , Cranberry , NJ ) . Embryos were harvested at embryonic day 14 . 5 , fixed , whole mount stained for lacZ , embedded in paraffin , sectioned , and counterstained using Nuclear Fast Red ( Vector Laboratories ) . For transfection of cortical neurons , elements were cloned into the firefly luciferase vector , pGL4 . 23 ( Promega ) containing Gateway cassette A ( Invitrogen ) . Neurons from the dorsal cerebral wall were dissected as for ChIP-seq , dissociated using 0 . 25% trypsin and 10 ug/ul DNase , transfected with experimental luciferase construct and a pRLTK Renilla control in a 96-well nucleoporator ( Lonza ) then plated onto poly-D-lysine coated 96-well plates ( NUNC ) in PNGM ( Lonza ) . Media was changed 4–6 h after transfection , and luciferase assays were done 48 h after transfection . Luciferase assays were done using a DLR 100 kit ( Promega ) according to the manufacturer's instructions and read using a Promega Glomax luminometer . All animals were treated under protocols #18487 and #21758 approved by Stanford University Institutional Animal Use and Care Committee . Length and GC-matched regions were selected randomly from the mouse genome to provide a null set for the 6 , 629 E14 . 5 peaks . We then ran ten different published motif discovery tools on the set of peaks and controls: Allegro [47] , AlignAce [48] , BioProspetor [49] , CisFinder [50] , MDscan [51] , MEME [52] , MoAn [53] , MotifSampler [54] , NestedMica [55] , and Weeder [56] . Near identical motif predictions were combined . In a previous work we compiled a library of motifs ( position weight matrices ) for hundreds of different transcription factors from public motif databases and primary literature [57] . We combined the de novo motif candidates with our library of known motifs . The set of known and putative novel motifs was then predicted at a motif match threshold of 0 . 9 [58] in both our peaks and the control set of regions . Motif fold enrichment was calculated as the number of candidate enhancers with a match to the motif divided by the number of random regions with a motif match . Motifs over two fold enrichment are reported in Figure 3 . We considered a candidate enhancer to be under purifying selection if it overlaps a region from the UCSC mm9 PhastCons Elements track ( phastConsElements30way ) that scores at least 350 [59] . We tagged candidates with depth of conservation based on pairwise alignment nets from UCSC [60] . We obtained all regions of the genome in the level 1 and 2 nets; eliminated large duplications ( genomicSuperDups track ) [61] , pseudogenes ( pseudoYale60 ) , and known exons ( knownGene:exon ) [46]; and considered a basepair reliably conserved to a given clade only if it is conserved to the previous clade . Clades were represented by: euarchontoglires ( human hg19 , chimp panTro3 , rhesus rheMac2 ) ; eutheria ( elephant loxAfr3 ) ; mammalia ( platypus ornAna1 ) ; amniota ( chicken galGal3 , lizard anoCar2 ) ; tetrapoda ( frog xenTro3 ) ; gnathostomata ( tetraodon tetNig2 , fugu fr2 , zebrafish danRer7 , stickleback gasAcu1 , medaka oryLat2 ) . For clades with multiple representatives , a basepair is considered conserved if it aligns to any of the representatives , except two genomes are required for gnathostomata . A candidate enhancer is tagged with the deepest clade to which at least 200 bp of the candidate is conserved . In Figure 5B , “non-exonic basepairs” are all basepairs in the mouse genome not in large duplications , pseudogenes , exons , or gaps . The VISTA Enhancer Browser [23] includes results for mouse transgenic enhancer assays for candidate human DNA sequences . We obtained 1 , 255 tested human sequences , and mapped the sequences to the mouse genome ( mm9 assembly ) using liftOver ( -minMatch = 0 . 8 ) and lastz ( --seed = match6 , --hsptresh = 1800 , --gappedthresh = 5000 , sequence identify ≥65% , entropy ≥1 . 8 ) . We successfully mapped 1 , 188 enhancers , including 176 forebrain enhancers . The tested sequences overlap 214 of our candidate enhancers , with 93 active in the forebrain . The significance of tested E14 . 5 candidate enhancers driving activity in the different mouse tissues ( Figure 5C ) is calculated using a hypergeometric enrichment test ( for example , forebrain: hyper[93/214; 176/1 , 188] ) . The zebrafish cneBrowser [24] , [62] includes results for zebrafish transgenic enhancer assays for candidate zebrafish DNA sequences . We obtained 164 tested zebrafish sequences , and mapped the sequences to the mouse genome ( mm9 assembly ) using lastz ( --seed = match6 , --hsptresh = 1800 , --gappedthresh = 5000 , sequence identify ≥65% , entropy ≥1 . 8 ) . We successfully mapped 129 enhancers ( 21 overlap a candidate E14 . 5 enhancer ) , including 31 forebrain enhancers ( 11 overlap ) . The significance of tested candidate E14 . 5 enhancers driving activity in zebrafish tissues ( Figure 5D ) is calculated using a hypergeometric enrichment test ( for example , forebrain: hyper[11/21; 31/129] ) . The repeat-annotations ( RepeatMasker open-3 . 2 . 8 ) for the mouse genome ( mm9 ) were downloaded from RepeatMasker ( http://www . repeatmasker . org/ ) . For each p300 ChIP-seq set , we measured the observed overlap with each interspersed repeat family . To determine the expected overlap , our p300 set was shuffled randomly across the genome 10 , 000 times . For each of these shuffles , the overlap with each repeat family was measured . The expected overlap is the average of these shuffles . Fold enrichment is calculated as observed/expected . The Z-score is ( observed-expected ) /standard deviation . Note that because we used only uniquely mapped reads ( of length 36 ) we may miss some peaks and overlaps with the most recently active repeat families whose genomic copies may still hold long stretches of identical bases . However , all families highlighted in the text are old and no longer active such that the reads overlapping them resolve accurately and comprehensively . | Sequencing based technologies provide global snapshots of transcriptional regulation . These data promise insights into gene regulation , disease susceptibility and organismal evolution . They also provide a methodological challenge in distilling specific hypotheses from large masses of data . Most work to date has focused on deriving broad biochemical insights . Here we obtain the active enhancer landscape of the dorsal cerebral wall during early neocortical development . We show that our set likely contains enhancers from both the developing neocortex , the ventricular , subventricular and intermediate zones , and develop methods to separate this mass into subsets of interest in particular contexts . We discover novel enhancers next to key neocortex development genes . We show that some known key and novel genes are regulated by dozens of enhancers each , and find known and novel enriched binding sites for key transcription factors in our set . Nearly all newly discovered enhancers are conserved in human . A quarter of loci are shared with non-mammalian vertebrates . We show that the human and zebrafish orthologs of our enhancers mostly drive expression in related nervous system contexts . We also show that particular interspersed repeats were preferentially co-opted into potentially key neocortex development enhancers . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Enhancer Landscape during Early Neocortical Development Reveals Patterns of Dense Regulation and Co-option |
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